Anyway. The session.

Ask most educators how AI could support diverse learners, and differentiation comes up almost immediately. Simpler text for struggling readers. Alternative versions of tasks. Visual summaries. It’s a reasonable answer, and it reflects exactly what Bee Shaw and I found when we surveyed over 100 New Zealand educators for our study published in the Journal of Technology and Teacher Education last year (you’ll find the pre-print on another post here). When asked how AI could best support inclusive practice, reducing admin to free up face-to-face time came first, creating engaging content second, and differentiation of resources third.

That’s not a bad list. But it’s an almost entirely reactive one. And I’ve been sitting with the question it raises: educators are being sold specific tools to help them differentiate, but what if differentiation, even AI-assisted differentiation, isn’t actually the goal?

The differentiation trap

When I was working in initial teacher education, differentiation was one of those skills that trainee teachers knew they needed but couldn’t quite get hold of. It had an almost mythological quality to it. I used to think of it a bit like the early scenes in Harry Potter where the students are trying to cast spells and mostly just end up making frogs explode. They know what they’re aiming for, they’ve been told it’s important, but the gap between the idea and the actual execution is enormous.

And to be fair, differentiation is genuinely hard. When you’re standing in front of a class with a wide spread of abilities, needs, and preferences, adapting your approach in the moment is good teaching. It’s responsive, it’s human, and it matters. AI is actually useful here. It can generate a simplified version of a text, produce an alternative explanation, create a visual summary, and do it in minutes rather than the hour wrestling with the printer or photocopier on a Monday morning with the rest of the staff.

But here’s the thing: differentiation, by its nature, responds to variability after the fact. A learner struggles, or we anticipate they might, and we adjust. The design itself stays the same. We add an alternative, a support, a workaround. AI makes that work faster and more varied. But it doesn’t change the underlying question we’re asking, which is: how do we fix this for that learner?

Universal Design for Learning asks a different question entirely. It asks: have we designed for variability from the start?

What UDL actually means

I’ve written about UDL before, back in 2014, so this isn't a new argument. What's new is having AI good enough to generate an image that makes the point for me. The ramp in the image above isn't a workaround bolted on after someone complained. It's architecturally part of the building, the same concrete, the same curve, the same intention. That's what designing for variability from the start actually looks like.

UDL applies the same logic to learning. CAST describes three networks that govern how we learn. The affective network governs the why: what motivates and engages us. The recognition network governs the what: how we perceive and make sense of information. The strategic network governs the how: how we plan, organise, and express what we know. UDL asks educators to anticipate variability across all three before they design, not as add-ons for some learners, but as the default starting point for everyone.

The distinction matters enormously when you think about how AI fits in. AI applied to a differentiation model makes individual fixes faster. AI applied to a UDL model makes the design better for everyone, before anyone has to struggle.

Two tools for a different question

If the argument for UDL is convincing but the practice feels abstract, that’s where I wanted to meet educators with something concrete. Over the past couple of years I’ve built two tools designed to shift the conversation from differentiation toward UDL, one that started as something for beginning teachers trying to get their head around inclusive design, and one that came out of the Ako Aotearoa research work.

The first is a UDL Custom GPT I built back in 2024 (it still feels strange to write that). I originally made it for RTLBs to share with teachers who needed support adapting lesson plans, particularly those early in their career who understood that UDL mattered but weren’t quite sure what it looked like in practice. It’s not a resource generator. Its job is to interrogate a lesson design against the three UDL networks, asking questions that surface whether a design anticipates variability or just responds to it. The questions it generates are often more useful than any resource it might produce.

The second is the Inclusive AI Assessment Checker, developed through the Ako Aotearoa project. The idea came out of an AiEdCoP session where Ryan Noonan demonstrated Canva Code, and I started thinking about what it would look like to view a lesson plan through the eyes of different learners (I actually re-built it using Gemini in the end- see earlier point about API key!). It lets educators paste a lesson or assessment task and see it through the perspectives of diverse learner personas — Māori learners, neurodivergent learners, ESOL learners — before they teach.

Those personas are provocations for reflection, not substitutes for real learner voice. When actual community members were involved in the co-design work, they surfaced needs that no AI-generated persona had anticipated, things rooted in lived experience, whakapapa, and community context that a language model simply can’t replicate. The tool is a starting point. Then go and talk with your learners.

The question that matters

One voice from our educator research keeps coming back to me: “Our school is poorly resourced in all areas. Our students are in survival mode. AI is a wealthy school problem.”

That’s worth sitting alongside the marketing. Because the pitch for AI-assisted differentiation is everywhere right now, faster resources, personalised pathways, adaptive tools. And some of it is genuinely useful. But educators are being sold specific tools when what most of them actually need is a framework for thinking about design. A tool that generates a simplified text faster doesn’t ask whether the original design was excluding anyone. It just makes the workaround more convenient.

UDL isn't a tool. It's the question you ask before you open one. The tools will keep coming and the pitches will get better, but the question underneath all of it stays the same: are you designing for variability, or just fixing it faster? If you want to work through that question with a community of educators, AiEdCoP is a good place to do it, and if you’d like to watch the session where I discussed this with our members you’ll find that here.

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Gander, T., & Shaw, B. (2025). Navigating the AI landscape: Educator insights and pedagogical implications in New Zealand. Journal of Technology and Teacher Education, 32(3), 1–27.

Gander, T. (2025). InclusiveAI: Empowering Aotearoa, an inclusive approach to AI literacy in tertiary education (Final Report, Project 45195). Ako Aotearoa Research and Innovation Agenda.

Impact of AI Feedback on Student Assessment Practices - Watch Video

Link to the slides from the presentation

Link to the student-facing GPT

Link to the AI reflection guide

Here is the submitted abstract:

The Impact of AI Feedback on Student Assessment Practices and Outcomes 

This study examined the impact of using a pre-assessment submission Generative Pre- trained Transformer (GPT) on student learning and assessment practices. The GPT was available on the assessment page and allowed students to choose to receive generative AI feedback before making their final submission. In addition, students filled out a reflective form to evaluate their use of generative AI. Data was collected through pre- and post-surveys using the UTAUT scale, as well as AI literacy and critical reflection questions. Interactions with the GPT were recorded and transcribed for qualitative analysis. After the intervention, revised assignments were evaluated using rubrics, and survey data measured student experiences, engagement, AI literacy, and critical reflection. The implementation of this tool promoted transparent AI use and improved the quality of student submissions. Additionally, students' AI literacy increased. These findings contribute to the existing literature on AI in education and provide practical recommendations for integrating AI in learning and assessment. Implementing a GPT is a simple and practical first step in supporting the integration of generative AI into assessment.

Through the AI-focused Community of Practice (CoP), we collaboratively designed a survey to capture insights from educators. The findings reveal a spectrum of attitudes: some educators view AI as a transformative aid for reducing administrative workloads and personalising learning, while others express concerns about potential issues like plagiarism and ethical misuse. For instance, there were reports of school boards misusing AI for data analysis, breaching privacy and trust.

One significant theme is the lack of AI literacy and professional development—over half of the educators had no training in AI. Many also noted the absence of policies to guide ethical AI integration. The educators with access to AI found it beneficial in creating differentiated resources and providing faster feedback, showing its potential to support inclusive and personalised learning.

This study emphasises the need for strategic policies and professional development to help educators navigate AI's complexities effectively. As New Zealand's education sector moves forward, we see an opportunity to promote AI literacy and responsible use, creating environments where AI supports, rather than replaces, human-centred teaching and learning.

For those interested in the broader implications of AI in education, this research highlights how we can leverage technology thoughtfully to enrich pedagogical practices while addressing its inherent challenges. Several people have asked for access to the full research report so I am including the preprint version to facilitate broader access and early engagement with our findings.

The AI literacy framework is the result of a Delphi study which established four levels of AI literacy: Know and understand AI, use and apply AI, evaluate and create AI, and beyond AI literacy. Geri and I were interested in how our learners were using and applying AI literacy to support their assessments, so we turned the descriptors from level 2 ‘use and apply’ into 28 Likert scale questions and added some questions focusing on assessment.

On the night before sharing the research, I recorded a reasonably raw video of my short 15-minute presentation, and you can view that below (or at the previous link for the x2 speed version) and find out how I linked AI literacy to the computer game Lemmings(!) as well as other important things like the results. I have also collated all of the links (Presentation slides, symposium short paper, AI literacy survey, AI literacy framework, AiEdCoP and an opportunity to collaborate on the next stage of the research) here on this flowpage so you can follow up on anything that you find relevant.

We are interested in collaborating on subsequent iterations of this research to build a broader picture of AI literacy, if you are interested please contact me by the 20th of September 2024, as we have a team ready and will be starting the next phase.

If you are on a bus or have screaming children running around your house and you can’t watch the video you can read the summary of the presentation below. I copied the transcript from the video and asked chatGPT to rewrite it as a blog post using my usual writing style here on the blog. I think it turned out quite well! AI-generated content follows…

Why AI Literacy Matters

AI literacy, in my view, is the cornerstone of integrating AI tools thoughtfully and ethically into education. It's not just about understanding how to use these tools—it's about knowing when to use them, the risks involved, and how to mitigate those risks. With AI playing an increasingly significant role in educational settings, it's crucial that educators and students alike understand both the potential and the pitfalls of these tools.

One of the key challenges we've encountered is around assessment. There's a growing concern that AI tools could undermine the integrity of assessments, leading many institutions to simply halt any experimentation with AI in their practice. This, I believe, is a roadblock to progress. Assessment is an integral part of learning, and rather than shutting down the conversation, we need to rethink how we approach it with AI in mind.

AI and the Lemmings Metaphor

A metaphor that often comes to mind when thinking about AI in education is the classic game Lemmings. In the game, these little creatures wander aimlessly until they fall off cliffs, we need to support them to complete the level by enabling a safe pathway to the finish line. This could be likened to our students’ journey through our courses; many educators are either blocking the use of AI tools—essentially acting as the "stop lemming"—or they're relying on AI detection tools at the bottom of the cliff, hoping to catch students in the act of misusing AI. Neither approach is particularly effective.

Instead, I propose that we focus on understanding AI literacy at a deeper level (a bit like the lemming that digs down to explore different outcomes), helping students navigate these tools more thoughtfully. In the same way that you could give a lemming an umbrella to soften its fall, we can equip students with the right knowledge and skills to use AI tools without falling into unethical practices ☂️.

Our Research on AI Literacy in Assessment

Our research focused on evaluating AI literacy through a survey of students, most of whom were enrolled in postgraduate programs at academyEX. We used a framework developed by Kathryn, Dave and Mahsa, which provides a broad lens for understanding AI literacy across various educational levels. The survey aimed to assess not only how students were using AI in their assessments but also why they were using these tools and how effective the framework was in identifying gaps in their AI literacy.

Some key findings from our research included:

• Self-Rated AI Literacy: A surprisingly high percentage of students rated their AI literacy as either high proficiency or advanced. This could indicate a self-selection bias, as those who are already comfortable with AI are more likely to engage in a survey on the topic.

• AI in Assessment: 75% of the students reported using AI in their assessments, with the primary reason being efficiency and time management. Many of the participants are working professionals, often time-poor, and they see AI as a way to streamline their study processes.

• How AI is Used: Students reported using AI as a learning aid, to generate content, for research and analysis, and to support their writing. The ability to reduce cognitive load and organise ideas was frequently mentioned as a key benefit.

Challenges and Opportunities

One of the lowest-scoring areas in our survey was around the ethical use of AI tools. While we do discuss ethics in our classes and workshops, it’s clear that this is an area where more support is needed. Students want practical guidance on how to use these tools responsibly, and they want clear policies and guidelines from their institutions.

Looking Ahead

As we move forward, it’s critical that we continue to explore how AI can be integrated into assessment practices in a way that is both equitable and effective. AI literacy is not just a technical skill—it’s about understanding the broader implications of these tools and ensuring that students are equipped to use them thoughtfully.

For those interested in this area, I encourage you to join our growing AI in Education Community of Practice, where over 350 educators and professionals meet regularly to share ideas, trial new approaches, and discuss the future of AI in education. We are also looking for others to collaborate with for the next stage of the research, so please reach out if you are interested.

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Over to Derek to explain…

A couple of weeks ago I had the privilege of attending the launch of an exciting new education app called Mixiply at Newlands Intermediate School here in Wellington.

The story of Mixiply started back in 2017, when digital design teacher, Marianne Malmstrom , attended the Future Realities – AI, IoT and AR/VR Hackathon in Wellington where she met up with Jim Taylor, who is the Emerging Technologies Architect at Theta (NZ) . Jim and Marianne ended up collaborating on a pitch for the Hackathon which their team ended up winning!

Jim realised there was an opportunity for students and Theta to work together. Theta agreed, and put up some development money, with the caveat that the students would be part of that development. This is part of the Theta innovation lab philosophy: you don’t develop new technologies in a lab in isolation, you have to involve the end users.

Their collaboration resulted in Marianne returning to her job at Newlands Intermediate and establishing a student group she called her MiniDevs. Marianne’s idea had gelled nicely with Jim’s plan to design a platform that would allow users to create pop-up mixed reality ‘museums’ using the Hololens – an idea that Marianne hoped would help schools move past content and focus on the learning that happens when students create their own content.

Marianne now runs a series of HackMinis (her version of hackathons for kids) for every class at Newlands Intermediate. Every team must use Mixiply to document their learning and give the final presentations – because it was built for self-driven learning. With this experience under their belt, her students have also participated in another Hackathon organised by Microsoft, in which they had the opportunity to pitch their ideas, form teams and create a prototype for an app, game or activity that will benefit their community. 

“This is a real product – whether it actually comes to fruition and is launched isn’t the point. The process is what we’re interested in, because this isn’t a game or an exercise. The entire process is incredibly authentic,” Marianne says. “We’ve all come together in this space, the kids, the educators and the developers. With our three perspectives, we can create something much more robust and authentic.”

Jim, an emerging technologies architect at Theta, agrees wholeheartedly, and says he’s privileged to have the opportunity to experience the lateral and innovative thinking the MiniDevs have brought to the development table. “It’s great to be able to come and work in the community and help these guys and girls understand what’s involved in the software process. But they’re also helping us, because we’re getting first-hand, invaluable input into the platform. The kids are really good at challenging you and introducing you to new ideas that maybe as a grown-up, somebody who’s a bit more formal in their thinking through years of working in the industry, might not have come up with. It really helps with the creative process, having that interaction.”

Jim is quick to say that Mixiply isn’t just a tool — “it’s a comprehensive ecosystem that empowers users to create, learn, and innovate. By combining state-of-the-art technology with intuitive design, Mixiply is setting new standards in digital creation and collaborative learning,” he told me.

Here’s some screenshots they shared with me of the AI in action …

plus this aid for capturing reflections:

… and the new interactive, collaborative whiteboard …

As you may tell, I was enormously impressed with what I saw and heard at Newlands, and through my conversations with Marianne and Jim – not just with the product, Mixiply, but also with the entire process of how it has been developed, and continues to evolve. This is a superb example of what can be achieved through effective partnerships between schools and their community – drawing on the wealth of talent that exists beyond the school gate and bringing this into the experience of the students’ learning. Of course, none of that happens just by saying it should – I need to acknowledge the passion and enthusiasm of Jim and Marianne, as well as the permission-giving leadership of the NIS principal, Angela Lowe. This is an example of the learning ecosystem working well!

Want to learn more?

The AI in Education Community of Practice, coordinated by Tim Gander from AcademyX, currently has over 500 members, all interested in sharing the challenges and opportunities they face in understanding more about AI in education. They hold fortnightly virtual meetings on Monday’s from 5pm. Their next meeting, on Monday 2 September will feature Jim and Marianne speaking about Mixiply and their experiences together in establishing the MiniDevs group, and how the product is being used and developed by the students. Click here to join this community and receive an invitation to the next meeting where you can hear more of Jim and Marianne’s story.

About Derek

Derek is regarded as one of NZ education’s foremost Future Focused thinkers, and is regularly asked to consult with schools, policy makers and government agencies regarding the future directions of NZ educational policy and practice.

A Vision of Education in 5 Years with Successful AI Integration for New Zealand

I recently read a position paper by Ouyang and Jiao (2021) discussing three AI paradigms in education. Interestingly, this was published before Chat-GPT was unleashed on the public. However, the three suggested paradigms seem helpful in examining potential visions of the future of AI in education (Table 1).

The role of AI and interactions with the learner distinguish the three paradigms. In Paradigm One, AI directs the learning process, and the learner acts as a recipient. In Paradigm Two, AI supports learning, and the learner collaborates with the system. Paradigm Three views learners as leaders of their own learning, empowered by AI to personalise their learning experience.

While the large tech companies will see easy solutions to push content to the learner in automated systems (paradigm one), we need to look beyond this to maximise AI's potential in education (paradigms two and three). Below are potential scenarios that we could see in five years.

Paradigm 1: Personalised Learning Journeys: AI could enable the creation of customised learning pathways for every student. Imagine AI-driven digital textbooks, like those being developed in South Korea, adapting in real-time to a student's learning pace and style. In this future, instead of one-size-fits-all lessons, students in the same classroom could work on different tasks and activities based on their individual needs, as determined by AI. Effective differentiation is one of the overwhelming challenges faced by teachers each day, and this could be a valuable solution.

However, we need to be careful here that this does not turn into an amplified factory line of education with a soulless robot pushing learners to more of a 'qualification'. You just have to look at the Google reviews of online learning platforms like this to see how much students dislike this teacherless approach - how can this be done differently? Can we avoid AI being used to amplify bad practices in education? This aligns more with what I think AI can do well and quickly (creating content), rather than being critical of what it actually could do to support educators and students in an age of AI.

Paradigm 2: AI - A Collaborative Partner in and out of the Classroom: AI could transition from a technological tool to a collaborative partner in education. The sources from WEF and the OECD suggest that AI could play a crucial role in:

• Empowering Teachers: AI could take over administrative burdens, like grading and lesson planning (p.10), allowing educators to focus on providing personalised support and mentorship and fostering meaningful relationships with their students (p.3). This shift could redefine the teacher's role, requiring them to become skilled facilitators of AI-powered learning experiences.

• Transforming Assessment: Continuous, AI-driven assessment could replace high-stakes testing. AI systems could be designed to track student progress in real-time, providing instant feedback, and allowing immediate adjustments (p.11) to learning pathways. This could lead to a more holistic understanding of student learning.

• Breaking Down Barriers: AI-powered language translation and adaptive content could create truly global classrooms (p.16), connecting students and educators from different countries and cultures. This type of technology could make quality education more accessible to learners in underserved communities and those with disabilities.

This approach requires a greater understanding of how the tools can benefit teaching and learning. Rather than just receiving AI-generated content, the teacher and the student need to build skills in effectively using the tools for learning. Our education system needs to change for this to happen, as it requires us to dismantle assessment and look at what students are really learning at school, and why.

Paradigm 3: AI-augmented learning: This is where it takes a bit more imagination (and trust), and I'm still not entirely comfortable with this potential future. Students and teachers could opt into a full-time personal assistant that they have agency over, understanding how and when to interact with it, bringing it in to augment their learning and their lives.

If we are going to have this level of learner-augmented AI, it is going to need a great deal of data (think of the quantified self - perhaps an extended version of your Garmin 'morning report' or 'Apple Health' with a built-in AI assistant that shares life/learning/work updates and advice based on the conversations that you have combined with digital interactions). However, who will be ready to hand that data to the large faceless companies that own and run LLMs? This is amplified when considering learner data and the almost immeasurable ethical considerations needed to keep young people's data safe. Do we have the capacity to develop something bespoke in New Zealand that we could trust? 

Creating the environment for change

For any of these paradigms (particularly two and three) to be realised in the next five years, we should focus on the following areas; 

Closing the digital divide: We know a huge gap exists in New Zealand. For any steps to be taken forward, we must commit to narrowing the gap between those with access to technology and the skills to use it and those without.

A Focus on Responsible and Ethical AI Development: This is critical, and the importance of ethical considerations in developing and implementing AI in education should not be underestimated. We have seen that the ethics of the main companies leading the AI charge do not always align with what we expect from companies with so much of our data - see Scarlet Johansen vs OpenAI. In particular, we need to focus on;

• Addressing Bias and Ensuring Equity: AI systems should be designed and used responsibly to avoid perpetuating or exacerbating existing societal biases. This requires diverse teams of developers, ongoing monitoring for bias, and clear guidelines for ethical AI use (p.128).

• Prioritising Data Privacy and Security: Protecting student data is paramount. Robust data privacy and security protocols would be crucial, ensuring transparency about data collection practices, obtaining informed consent, and safeguarding sensitive information (p.12).

Essential Skills for an AI-Driven World: Successful AI integration would require a greater emphasis on AI literacy for all. This would involve:

• Equipping students with future-ready skills: Curricula must prioritise skills like critical thinking, problem-solving, digital literacy, ethical reasoning, and creativity (p.9). AI could be incorporated into all subjects, not just STEM fields, helping students develop a nuanced understanding of its applications and implications.

• Preparing educators to thrive in an AI-enabled environment: Robust training and ongoing support would be essential for teachers to effectively integrate AI into their pedagogical practices. This includes developing their confidence in using AI tools, understanding ethical considerations, and adapting their teaching strategies to leverage AI's capabilities (p.12).

A Call for Collaboration and Ongoing Evaluation: All sources, policies and guidelines that I have read stress that successfully integrating AI in education requires a collaborative, human-centred approach, where:

• Educators, policymakers, researchers, and technology developers work together (p.4) to create AI-powered tools and resources that are pedagogically sound, ethically grounded, and meet the needs of diverse learners.

• Ongoing research and evaluation are crucial to understanding AI's impact on learning outcomes, student and teacher well-being, and the broader educational landscape.

Of course, the successful integration of AI in education is not without its challenges; addressing issues like potential job displacement, ensuring equitable access to technology, and navigating the ethical complexities of AI will be crucial. Understanding the broader impacts of mass energy consumption, the worrying reports of AGI, and the downfall of the human race should also be placed within any discussions regarding the future of AI in education. However, the potential benefits of AI, if implemented thoughtfully and responsibly, could revolutionise education, creating more personalised, engaging, and equitable learning experiences for all.

At the blueprint working group we were also tasked with developing several key tasks that could be implemented in the next year to support change.

Immediate Action…

We can take the next steps over the coming year to enable the thoughtful and effective integration of AI in education.

• Prioritise Infrastructure: Education systems should prioritise equitable access to affordable, high-quality connectivity and digital learning resources. This includes ensuring all learners and educators have access to reliable internet connectivity and digital devices at school and at home. This is essential for the equitable use of AI-powered tools and resources.

• Develop Clear Guidelines and Policies: There is an immediate need for education systems to establish clear guidelines and policies to ensure the ethical and effective integration of AI. This includes:

  • Addressing data privacy concerns: Develop and implement robust data privacy and security protocols to protect sensitive student information. This includes ensuring consent for data collection, anonymising data, and limiting data collection to only what is necessary for educational purposes. 

  • Promoting ethical AI use: Immediately establish ethical guidelines for AI use in schools that consider potential bias, transparency, accountability, and the impact on student and teacher well-being. This can be achieved by adapting existing ethical frameworks, such as the soon-to-be-released AI Competency Framework for Teachers by  UNESCO, to suit specific national contexts. 

  • Creating a culture of responsible AI use: Encourage teachers, students, and other stakeholders to participate as co-designers in the research and development of AI-enabled educational tools. This collaborative approach will help ensure these tools are pedagogically sound, relevant, and used effectively. 

• Support Teacher Development: We should focus on building teacher capacity and confidence in using AI for teaching and learning. This can be achieved by:

  • Integrating AI into teacher training: Initial teacher education programs should incorporate training on the use of digital tools for learning across all subjects.

  • Providing ongoing professional development opportunities: Continuous professional development for teachers should include training on the use of AI in teaching, learning, and assessment. A community of practice involving interested and passionate educators could drive this (join here!).

  • Fostering AI literacy: Develop and implement government-endorsed AI curricula that cover ethical considerations, understanding algorithms, and the proper use of AI tools. This will help equip both teachers and students with a foundational understanding of AI. 

As we slowly integrate AI into education, it becomes clear that the steps we take must go beyond adopting new technologies or succumbing to 'AI solutions' imposed by big tech companies. We need a responsible and ethical approach prioritising human-centred learning experiences which can celebrate and enhance educators' skills. With this in mind, charting the future of AI integration in New Zealand education requires us to move beyond paradigm one, where AI merely automates and directs learning, risking education becoming solely a transactional pursuit of qualifications and credits.

Processing these thoughts and understanding more about the potential paradigms prompted me to revisit Gert Biesta's domains of purpose —qualification, socialisation, and subjectification, which provide a framework to transcend the possible limitations; we risk being trapped in the qualification domain if we only focus on paradigm one, content pushed to speed up the process of gaining credits. By integrating AI with a focus on socialisation and subjectification alongside qualification, we can cultivate critical thinking, ethical reasoning, and meaningful societal contributions in our students. This holistic approach ensures that AI enhances education by preparing students not only for personal success but also for active and positive participation in shaping our shared future.

This post was written by me, a Human, I used NotebookLM to help synthesise the sources mentioned in this post. I’ve tried a different approach to referencing the links by using the hyperlink and then the page number with the relevant reference - I hope that is helpful.

When our students have used AI to help with research project scopes or write a report section, they often skip essential information that has been intentionally clear in course readings (e.g., just using a dictionary.com reference to define their methods). On top of this, it is also very obvious where they have 'inserted' information from generative AI that isn't accurate; it just sticks out like a sore thumb - perhaps burnt on the hot end of a flaming bowling pin. While consistently encouraging generative AI use, we also want to ensure our students were using it critically with accuracy and clarity. With that in mind I made an interactive course helper bot using BotPress that aimed to collate all of the readings and resources for the methodology course I'm currently teaching. The idea was that the bot could be asked any question based on those resources - without using open access to the internet. Without these distractions and further opportunities for hallucinations, the students could deepen their knowledge of the specific readings and use that to support the research project scopes or project reports. However, after running the bot for over six months, the analytics tell me that there have been about ten uses of my friendly AI bot helper, and I'm confident that a few were mine! 

So, when I saw a demo of NotebookLM a couple of weeks ago, I was excited to try a different approach. NotebookLM is an AI-powered research tool from Google that helps users generate insights and summaries from their own notes and documents. It seemed like more of a student-driven version of what I was trying to achieve (AI-supported accuracy and clarity in interactions with course content). Instead of me choosing the resources that the AI has access to as its knowledge base, NotebookLM allows the student agency by incorporating an easy interface that either links to a Google Drive or supports uploaded documents (such as their course notes and resources).

To test it out, I uploaded the OERs that we use for course content, as well as the course lecture slides and notes, to NotebookLM and then created some key notes that would be useful for students. From that point, I could also choose to share the notebook, and students would be able to interact and chat with the resources in their copy of the notebook, much like how you'd copy a Google Doc, not just comment on the same one.

When I originally proposed using the BotPress bot, I was aiming to create a 'course assistant,' as highlighted in Boston College's useful role descriptors.

Now, after using NotebookLM for a while, I can see how it can better align with Boston College’s initial descriptors. Depending on how students choose to engage with the tool and the sources they upload, it can offer a combination of roles supported by transparency, scaffolding, cognitive load, and metacognition. *Update: The new podcast feature will support students (and anyone else using the tool with cognitive load - being able to hear a ‘humanised’ audio version of the content in an AI-generated podcast makes the content accessible in an output that is usually under 10 minutes.

• The tool can primarily act as an equitable and transparent course assistant, helping students understand how a course works by enabling open chat access to course guidelines, notes, and assessments and supporting the processing of multiple texts and readings. Once students develop their notes, NotebookLM provides them with specific prompts to create study guides from curated resources.

• If NotebookLM can access the relevant readings and the assessment rubric or marking schedule, it could also act as an assignment tutor by scaffolding submissions (see below).

• Once the student has an idea about the assessment structure, NotebookLM can also support them as a process coach by providing step-by-step guidelines on how to begin the submission and subsequent steps. The new ‘podcast’ feature here is going to make a big difference in summarising large amounts of information before the student begin digging deeper.

• If the student uses the custom note function, NotebookLM can also serve as a reflective guide to prompt questioning and reinforce learning. One thing I like about this is that you can pin notes to the top of the interface, unlike on ChatGPT where you chats, and the important parts within them just disappear into the mountains of text that it creates.

My favourite thing about the tool is that it will say it doesn't know the answer based on the sources provided, therefore not setting the dreams of a concise submission up in smoke, unlike our other fire-juggling generative AI tools. However, with the new audio feature, the audio content may have inaccuracies - so perhaps steer clear if you’d like to keep a level of accuracy.

You can try the tool out for yourself now, as it seems like most people have access to it, but I made this short video before it was available just to show the capabilities. It would be great to hear if anyone else has any other ideas for how it could be used. If you are looking to try out the new podcast feature then you will need to start a new notebook as I couldn’t manage to make it work on any of the existing notebooks that I had.

Using Notebook LM to Enhance Teaching and Learning - Watch Video

Overall, I can see that more organisations will move to a more customised walled garden to support the safe and ethical use of generative AI. However, this is a great first step to experiment with how it may look, and to my main point, I think this is an excellent study tool for students.

This post was written by me, a human - however I asked ChatGPT 4-o to come up with a title and I thought it was effective so used it!

This process is similar to one of the assessments our students submit, which requires them to identify a problem in practice or a challenge that will be met by a practice-based intervention. Currently, this is a formative assessment involving a collaborative oral element where the peer groups read each other's project proposals and then provide feedback to each other in a synchronous online meeting. This works reasonably well and is authentic in its purpose and outcomes; however, sometimes, the feedback isn’t as critical as required.

Reflecting on this with students, we’ve identified two main hurdles. First, the discomfort associated with providing critical feedback to peers, and second, the struggle to provide feedback on unknown territory - the classic conundrum of not knowing what you don’t know. This is particularly challenging in a ‘live synchronous’ assessment. The existing project scope assessment aims to evaluate the critical understanding of applied research methods and tools within the context of practice-based change projects, as well as how the students critically evaluate all ethical considerations and mitigations relevant to practice-based change projects.

Drawing inspiration from the University of Sydney’s innovative ‘two-lane’ approach to assessment, I’ve started reimagining our assessments to better equip our students for an AI-integrated future. In the model proposed by Danny Liu and Adam Bridgeman, ‘Lane 1’ assessments ensure students have mastered the skills and knowledge our programs require, while ‘Lane 2’ assessments motivate students to learn and teach them to engage responsibly with AI.

I often encourage our students to use generative AI to support the development of research ideas. I have written about this in the past, and based on my experimentation with AI for developing research proposals, the project scope assessment seemed the ideal opportunity to introduce a ‘lane 2’ generative AI approach. I could also see the potential of mitigating some of the shortcomings identified in student feedback about the assessment, particularly the challenge of providing critical feedback to peers. Devoid of feelings (as it so often lets us know!), AI can serve as an impartial ‘critical friend’, providing feedback that students may not have considered. This unique characteristic of AI, its lack of emotions, makes it easier for students to critique. I’m hoping that students can freely express their thoughts and criticisms without the fear of offending or hurting someone’s feelings. The aim is that this can build confidence in this area and, in turn, support critical feedback for real human peers!

The revised ‘lane 2’ project scope assessment encourages students to input their emerging project ideas into the generative AI using the provided multi-staged mentoring prompt. The generative AI then attempts to provide critical feedback and suggests improvements based on the parameters, in this case, learning outcomes written into the prompt. This process culminates in the synchronous online meeting where students collaboratively reflect on their original scope and the version with modifications suggested by the generative AI. They justify their critique against a framework based on the assessment outcomes, demonstrating the critical understanding and evaluation we aim to foster, and in doing so, they collaboratively navigate the lanes of learning in an AI-integrated world.

Another intentional outcome of this ‘lane 2’ approach is to raise AI literacy. While some students were using AI to support the development of their research ideas, others were avoiding it because they feared ‘being caught cheating’ or because they did not understand how to prompt effectively. By making generative AI use an integral part of the assessment and by reflecting on the output collaboratively, the students understand use cases for generative AI as well as the potential shortcomings of the tools.

In the revised assessment, I’ve intentionally included a comprehensive, multi-staged mentoring prompt designed to guide a student through the detailed process of developing a research proposal for a practice-based research project. It provides a structured and interactive framework where the AI acts as a mentor, offering critique and suggestions and ensuring the student is prepared before moving to the next stage. This method ensures thorough coverage of each aspect of the project scope, facilitating deep engagement and iterative refinement. This also provides a consistent experience for all learners and supports them with a framework for creating their own prompts. I’ve also indicated they are free to tinker with the prompt and make it work for their context. Further into the course, I will encourage students to create their own prompts and support critical evaluation based on the rubric suggested by Danny and Adam.

At this stage, we have tested the prompt using the generative AI models available to our students and trialled the concept with a small group of students. In the next iteration of the course, we will introduce the assessment with the AI prompt and aim to quantify the level of critical thinking based on a modified scale relevant to generative AI use. If anyone has any suggestions, it would be great to learn more!

A link to the prompt I developed can be found here. I’m sure there could be further refinements, so I’ll keep working on it. Any feedback would be appreciated.

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Encouraging generative AI use

We have been encouraging our students to use generative AI tools (while also understanding the risks) for over a year now. This has been supported by our AI guidelines, which are underpinned by our values of Ako and Pono. However, we are still having regular conversations with students who are afraid to use the tools or are relying on them a bit too heavily in assessments that need more of a human touch.

In the interest of an equitable approach to AI literacy, we decided it was important to support all of our learners in understanding how they can use these powerful tools to experiment and learn without the fear of being ‘caught cheating’. In addition, I wanted to test how reliable AI detection checkers were and make the case that you probably could use AI and not be detected, but I also wanted to discuss, ‘Is that what we are here for?' Overall, I was angling for an approach of radical transparency to enable learning.

A catalyst for the session was this example of how Chat-GPT has been used in peer-reviewed journal articles. See for yourself; you just have to enter “as of my last knowledge update” or a similar AI-bot-specific turn of phrase, such as “I’m very sorry, but I don’t have access to real-time information,” into Google Scholar. It won’t take you long to find an article that has slipped through the net.

I found this quite shocking and used the example to point out that the use of AI in academic work is rife, and even blatant misuse can go undetected in the first instance.

So, how do you cheat?

It will only take a quick search on YouTube and a 3.5-minute investment in a 7-minute video watched at 2x speed to work out how to evade Turnitin and all other plagiarism detectors—hence the overly enthusiastic clickbait image for this post!

In learning these new strategies, like app smashing, where you put your text through a variety of apps (e.g. language translators) to fool the AI detection software and/or use a range of prompts to make the text more ‘human’, I thought I’d test these skills out to see if it was worth our students going through the process.

In the presentation, I shared how I compared a piece of writing I did back in 2020, way before any chance of using generative AI, alongside the ‘adversarial’ approaches mentioned in the videos. I prompted a range of generative AI tools to create a similar piece of writing to my original (a short literature review on bug-in-ear coaching), compiled all the approaches in this document and created a table that indicated the variance in AI detection. I also ran the entire report through Turnitin here.

Thankfully, the original version was not detected as AI; this was also the case when I asked Grammarly to ‘improve’ my original writing - which I was slightly surprised about. Unsurprisingly, though, the raw ChatGPT/ChatGPT+Grammarly versions of the prompted output indicated a high likelihood of AI being used. I couldn’t even beat Turnitin by using ChatGPT, then translating it into Spanish, Romanian, and then back into English. It also seemed, in my experience, that undetectable.ai was easily fooled by Bing. However, undetectable.ai was the only tool that was able to fool Turnitin. If students want to use a tool like this they have to pay a subscription, and for me, this falls into another level of deceitfulness, similar to contract cheating. I’m not saying that if the tool is free, then you should use it to cheat, but this pay-to-cheat approach broadens the digital divide, disadvantaging learners without the financial means to pay for a subscription; the same applies to more advanced AI language models that are pay-to-use and are less likely to be detected.

Implications for inclusivity and equity

Perkins et al. (2024) have taken my mini-experiment to another level and tested not only one text input but 114 different samples across a range of tools to understand the approaches that could be used to outwit the AI detection tools. The study highlights that there are inconsistencies in the AI detection results. It concludes that the tools are unreliable and shouldn’t be used as the only judgement of originality regarding student work. However, I was interested to read that from the results, Turnitin did not identify any false positives and also missed 88 AI-generated examples, which indicates that if something is identified as AI-generated in Turnitin, then it is quite likely that it is. Turnitin concurs and claims that there is a less than 1% chance of a false positive.

However, most importantly, Perkins et al. (2024) highlight that generative AI text-detection software, designed with the noble intent of ensuring assessment fairness, appears to be a double-edged sword. On one hand, the tools promise to uphold integrity in the academic realm, but on the other, they seem to inadvertently create barriers to inclusivity and equity. This is largely due to the detection tool’s susceptibility to these ‘adversarial’ techniques, which can be manipulated by those equipped with technological resources (generally financially, but also anyone lacking a moral compass). Perkins et al. (2024) also highlight that the detection software’s propensity to penalise students who exhibit a higher level of complexity in their writing, including non-native English speakers, raises concerns. As it stands, AI detection tools work to a degree, but the use of generative AI detection software may inadvertently erect barriers to inclusive assessment rather than dismantle them. This calls for thoughtful and innovative integration of AI tools in education - and one way to do this is through radical transparency.

How to be transparent?

If you are going by the book, and I mean the APA 7th guidelines, it is not possible to cite ChatGPT directly as the interaction is non-retrievable, although I wonder if this is still the case with OpenAI supporting hyperlinks to chats? Regardless, the APA 7th guidelines still suggest a clear way to acknowledge generative AI use - as seen below.

The reference list would include:

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

In this example, I’ve indicated that a chat transcript is also available as an appendix. We have encouraged our learners to do this to enable transparency and link to a Google doc where they have copy/pasted the chat with the generative AI. You will also notice that this reference describes a collaboration process with generative AI rather than just indicating that I am quoting something that it made up. Quoting ‘knowledge’ from generative AI is one of the risks of the tool as we know it can be inaccurate.

The previously mentioned research article from Perkins et al. (2024) had an interesting declaration at the end of the document. Again, this supports a level of transparency from the authors. However, ‘develop draft text’ seems quite ambiguous. I’d like to see some more detail if this was a piece of student work.

Another approach that my colleague Paula Gair has introduced is through a critical reflection guide. A table that could be included at the end of any piece of work that clearly shows how the student has engaged with generative AI tools. The key elements of this approach enable transparency and insight into what the student has learnt by using the generative AI tool. Suggestions in the table prompt the student to reflect on how they used it, if it worked, if it was biased, safe, and/or ethical, and what could be implemented next time to improve the interaction.

Feedback from students is that this could be quite a bit of extra work, and some of our supervisors recollected the days when Google search first came out, and it was expected that you recorded and shared your Google search terms when submitting. I agree, it is extra work, and could in fact take longer using generative AI tools, rather than the ‘productivity 100x’ tool it is suggested as. However, it is important at this stage to be clear about how we use the tools, as this contributes to our learning. Perhaps when generative AI tools are integrated into every digital tool, this approach won’t be needed; however, until then, I think using a simple critical reflection framework like this really supports open and transparent use of generative AI.

An education or a certificate?

Students are going to use generative AI, and unless we create clear guidelines that support radical transparency and thoughtful and ethical use, the tools could be abused. The gap between those who have the means to use the tools (financially or through AI literacy levels) will rapidly pull away from those who don’t have the means.

To end the session, I shared an image that I created using Co-Pilot. I wanted to indicate that, ultimately, it was the student's choice whether to use generative AI to learn, or to cheat. Did they want an education gained through learning, or a certificate gained by using AI to do thinking?

The thing I love about this AI image is that the word certificate is spelt wrong: ‘certiffate,’ and that just sums it up nicely!

Prompt: create a visual meme that would make the point about a student having to choose between an education (by learning the lessons and experiences along the way) or a certificate (where they just use AI to answer the questions).

You can watch the recording of the session here… and the slides I used can be seen here.

This post was entirely written by a human - me, Tim Gander.

References

Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education. https://doi.org/https://doi.org/10.48550/arXiv.2403.19148

In March last year, Brendon Shaw and I wanted to learn more about how schools integrated AI tools to support teaching and learning. We formed an open community of practice that met every fortnight online and several times face-to-face. The community membership ranged from 4-50 members at different times and directed the themes we would focus on for the research. The emerging themes were understanding how generative AI could impact equitable outcomes for Māori, inclusive learning and effective pedagogy. From there, we collaboratively built a survey and shared this with a wide range of educators in Aotearoa.

Summary of Findings:

• The data has highlighted the pressing need for comprehensive guidelines supporting teachers to safely and confidently integrate generative AI into their classrooms. Many educators expressed concerns about the lack of professional development opportunities to enhance their AI literacy skills.

• A notable finding was the uncertainty among teachers regarding whether students or whānau were using generative AI tools, indicating a critical need for open and transparent conversations about AI ethics and usage.

• The report highlights AI's potential to achieve equitable outcomes for Māori students. This can be accomplished through the creation of culturally located content using AI. However, guidelines, professional learning, and clear policies are essential to support educators in avoiding cultural appropriation and bias when using generative AI.

• Educators believe that generative AI can reduce administrative tasks, enabling more face-to-face contact, building connections between students and teachers.

• Teachers also thought creating engaging content and supporting individualised learning plans with generative AI was a high possibility. This could enhance pedagogy and inclusivity in education.

The following steps for Brendon and I will be to write up the qualitative findings and publish these. We would also like to revisit the survey, re-initiate the CoP, and investigate if anything has changed from last year. Through this research, we hope to contribute to the ongoing dialogue on AI in education and its implications for teaching and learning in Aotearoa.

If you would like to be involved in the next phases of the, please contact either of us.

Thank you to Lou Fox, Michael Puhara and Dr Karatiana Taiuru for their thoughtful input and guidance.

In addition, Brendon and I will share the research findings at an online webinar with academyEX: Let’s talk about what schools are doing with AI.

Please find a link to sign up to the webinar here.

An example of what this might look like is below; I also created some icons in Canva, which I will link for download at the end with some other resources.

Level 0: No use of generative AI

This level applies to situations where the use of generative AI is prohibited or not feasible for legal, ethical, technical, or pedagogical reasons. For example, if users' data privacy cannot be ensured or if the generative AI tool is not relevant or appropriate for the learning objectives or the curriculum. At this level, staff and students should refrain from using generative AI and rely on other sources of information and knowledge. 

    This level could apply when students are taking a standardised test, such as an external NCEA exam. Using generative AI tools to assist on the test is not allowed and could result in serious consequences for the students and the school. Another example could be when marking student work - uploading work to LLMs for training purposes without the student's consent is unethical. Perhaps, in the future, we will not have this level, but for now, and with the way we ‘measure learning’, it might still be required.

Level 1: Limited use of generative AI

This level applies to situations where the use of generative AI is allowed but with restrictions and supervision, for example, if the generative AI tool is experimental, potentially unreliable, or if the users are not familiar or comfortable with the technology. At this level, staff and students should use generative AI only for specific purposes and tasks and only with the guidance and approval of a teacher or educator with expertise in the area. The users should also pay particular attention when verifying the accuracy and validity of the generative AI outputs and accurately cite the sources and methods used (as in all Gen AI use - this is clear on the poster).

    An example of a situation where this level applies is when students are learning a new language, such as Spanish or Mandarin. Using generative AI tools to practice their speaking and listening skills, such as Duolingo or Rosetta Stone, is allowed, but only under the supervision of a language teacher or tutor. The teacher or tutor should monitor the student's progress, provide feedback and corrections, and ensure that the generative AI tools are not replacing the human interaction and cultural context of the language learning process. Students should also check the quality and accuracy of the generative AI outputs and acknowledge the sources and tools they used in their learning portfolio.

Level 2: Moderate use of generative AI

This level applies to situations where the use of generative AI is encouraged but with caution and awareness. For example, if the generative AI tool has been tested in the learning context and is considered reliable and beneficial, but it is also established that implementation is complex and potentially unpredictable. At this level, staff and students should use generative AI for various purposes and tasks, but with the understanding and acknowledgement of the limitations and implications of the technology. The users should also evaluate the quality and relevance of the generative AI outputs and provide feedback and suggestions for improvement. Again, at this level, users are encouraged to describe how they have implemented generative AI and even include transcripts or prompts to demonstrate how they have interacted with the tool.

    An example of where this level applies is when students write an essay, such as a persuasive or argumentative essay. The use of generative AI tools to generate ideas, outlines, or drafts, such as Essaybot or AI Writer, is encouraged, but with the awareness that the generative AI tools are not perfect and may produce errors, biases, or plagiarism. Students should also critically analyse the generative AI outputs and compare them with other sources and perspectives, such as peer reviews or academic journals. Students should also give feedback and ratings to the generative AI tools and suggest ways to improve them in the future. I have encouraged my students to take this approach with their research idea development here.

Level 3: Extensive use of generative AI

This level applies to situations where the use of generative AI is supported with creativity and responsibility. For example, if the generative AI tool is innovative, trustworthy and reliable for the selected task. At this level, staff and students could use generative AI for multiple purposes, but with the intention and commitment of enhancing the learning outcomes and experiences. The users should also explore the possibilities and opportunities of the generative AI outputs and create original and meaningful work based on them.

    For example, students create a multimedia project, such as a podcast or a video. The use of generative AI tools to produce content, such as Descript or Lumen5, is supported, but with the creativity and responsibility of the students to make their work unique and authentic. Students should also experiment with different features and options of the generative AI tools and discover new ways of expressing their ideas and messages. Students should also acknowledge the contribution and influence of the generative AI tools and share their work with others for feedback and recognition. I have encouraged students I am working with to use generative AI to support them in creating summaries of their research - this would be considered to be at 'level 3'.

Level 4: Free use of generative AI 

This level applies to situations where the use of generative AI is unrestricted but with respect and ethics. At this level, staff and students should use generative AI for any purpose and task, but with the recognition and appreciation of human values and rights. Again, as will all levels, acknowledgement of generative AI use is best practice.

    An example of a situation where this level applies is when students participate in a co-creation project, such as a game or a story. The use of generative AI tools to co-create content, such as AI Dungeon or StoryAI, is unrestricted, but with the respect and ethics of the users to treat the generative AI tools as partners and not as tools. Students should also engage and interact with the generative AI outputs and learn from their responses and feedback. Students should also respect the autonomy and agency of the generative AI tools and not abuse or manipulate them for malicious purposes.

How am I using this approach?

I have just finished writing a new course on research methods, and I have included these guidelines at the beginning and used the icons in any student task so it is clear how Gen AI can be used. There are some research tasks that can be enhanced with Gen AI; however, others aren’t that applicable. I will test the traffic light approach out over this new course and see how it could be improved or if it is even needed at all.

Perhaps an alternative approach to applying this (rather than having the teacher or school dictate how to use GenAI) would be to have students select the level that they thought would be appropriate for the task and then include the icon on the work - EG. L3 - AI with creativity. This would support building AI or digital literacy as well as agency.

Taking it further…

If you are interested in developing these ideas further, you could also use an acronym - I asked Bing Co-pilot to create one (this is something that generative AI is really good at), and this is what it created - I’m sure you could copy this and modify it to suit your school or workplace values.

**S**top: This is the level 0, where the use of generative AI is prohibited or not feasible, and users should not use it at all.

**M**inimize: This is the level 1, where the use of generative AI is allowed, but with strict restrictions and supervision, and users should use it only for specific purposes and tasks, and only with the guidance and approval of a qualified teacher or educator.

**A**nalyse: This is the level 2, where the use of generative AI is encouraged, but with caution and awareness, and users should use it for various purposes and tasks, but with the understanding and acknowledgement of the limitations and implications of the technology.

**R**einvent: This is the level 3, where the use of generative AI is supported, but with creativity and responsibility, and users should use it for multiple purposes and tasks, but with the intention and commitment of enhancing the learning outcomes and experiences.

**T**rust: This is the level 4, where the use of generative AI is unrestricted, but with respect and ethics, and users should use it for any purpose and task, but with the recognition and appreciation of the human values and rights of the technology.

However, I'm not really a fan of the acronym approach and prefer the levels, as the acronym could add another layer of confusion to the guidelines.

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Resources and links

Feel free to remix this idea and adapt it to your needs, as I am sure there could be some modifications to improve this approach; you will find a PDF of the poster guidelines here. The icons are in a folder here. If you would like access to the Canvas files to modify, please leave a comment or send me an email, and I’ll share them with you.

One way to approach this task is scenario planning, which helps us imagine and prepare for different possible futures. Scenario planning can help us identify the opportunities and challenges that AI may bring and the actions and strategies we need to take. I was introduced to this approach by John Bai at ASCILITE 2023. John and his team invited educators working in higher education to evaluate four strategic scenarios of possible AIEd applications developed based on critical macro-and meso-factors. The research found that it takes a collaborative approach to unpack the complexity that applying AI in education requires. You can read more about John's study here, as well as the scenarios that he used here: View of Future prospects of artificial intelligence in education (ascilite.org) 

In this post, I will share four scenarios illustrating how AI could impact different elements of education in 2024. Feel free to modify and adapt the scenarios to fit your context.

One thing that John and the team worked on in-depth was how they developed the scenarios; they developed five strategy elements from a diverse range of AI readings and then discussed the future options that could apply to each strategy. They then added the elements and options into a morphological box, which supported them in breaking the complexity down into a more manageable system. They could then investigate the total set of possible relationships or configurations between the elements. I am not suggesting that you do this, and I did not do this either. However, I did not want to simplify the work that had gone into the study. This approach might be worthwhile if you have specific values or goals that you would like to include in your scenarios - it is well worth looking at John's paper for more guidance. However, I tried a different approach to developing scenarios, the same method that anyone short on time at the end of the year would use... GenerativeAI!

I used the scenarios from John's article, Bing Co-pilot and some editing to come up with the following scenarios. When I facilitate this session, I will split the room into four groups to discuss their scenario, ask each group to present the scenario and their thoughts and then ask the whole group the broader questions. There are some similarities in the scenarios, and I hope these come out in the more general discussion. It would be interesting to see how this worked with different groups, for example I think students would have some great perspectives.

*Scenarios edited 24/5/24 to reflect ‘omni’ generative AI.

Scenario 1 - Generating Original Work with AI

A student uses ChatGPT-Omni to generate a short story for their English assignment. The tool not only uses advanced natural language processing and deep learning to create a diverse and engaging story but also provides a distinctive narrative style. The student submits the story without acknowledging the use of ChatGPT. The teacher suspects that the poem is not the student's original work and uses a plagiarism checker to verify it. The plagiarism checker finds that the poem was created with AI.

Some possible questions for discussion are:

• How should the teacher address the issue of academic integrity with the student? What steps should be taken to ensure a fair outcome?

• What guidelines should be established for the ethical use of AI tools like ChatGPT-Omni in assignments?

• How can students be educated about the responsible use of AI in creating content, balancing creativity, and originality?

Broader discussion:

• How can educational institutions create policies that both embrace the use of advanced AI tools like ChatGPT-Omni and uphold academic integrity?

Scenario 2 - AI-Curated Teaching Resources

A teacher uses ChatGPT-Omni to generate and curate lesson plans and resources for a unit on climate change. The tool uses generative models and deep learning to produce and select various types of content and resources based on the topics and objectives of the curriculum. The tool also verifies and validates the quality and relevance of the content and resources using web search and fact-checking. The teacher uses the content and resources generated by the tool without reviewing or modifying them. The students find some content and resources inaccurate, outdated, and inappropriate for their learning.

Some possible questions for discussion are:

• How should the teacher handle the situation? 

• What responsibility does the teacher have in verifying the accuracy of AI-generated content before presenting it to students?

• How can teachers effectively balance the efficiency of using AI tools with the need for accuracy and reliability in educational content?

• What training should teachers receive to enhance their skills in evaluating AI-generated resources?

Broader discussion:

• How can schools ensure that the integration of AI in teaching enhances educational quality without compromising content accuracy?

Scenario 3

A parent has developed a GPT to support their child's learning and performance as the student is behind with their work and does not have access to private tutoring or extra support. The tool has been designed to guide the student through the learning process without providing the correct answer directly. It offers personalised feedback and guidance based on the student's performance and progress. The tool also adapts the curriculum and the teaching methods to suit the student's individual needs and preferences. 

The student has become reliant on the tool for learning and assessment activities without consulting or collaborating with their teacher or peers. However, as a result, the student's skills and attainment have improved drastically beyond curriculum expectations. Their confidence has improved, and they are more motivated to engage in class. Other students are asking if they can use the tool.

Some possible questions for discussion are:

• How should the teacher handle the situation? Should the teacher intervene and communicate with the student and parent about the use of the tool? Should the teacher suggest that all students should be able to access the tool? Should the teacher attempt to create the tool for the whole class, how would other parents feel about this?

• How should teachers integrate AI tutoring tools in a way that encourages student autonomy and peer collaboration?

• What guidelines should be in place to ensure that AI tools complement rather than replace traditional teaching methods?

Broader discussion:

• How should the school regulate and monitor the use of AI tools to assist the learning and performance of students? Should the school provide or recommend any standards or guidelines for the use of AI tools for educational purposes? Should the school require or encourage the students to share and reflect on their use of AI tools and their learning outcomes and experiences? How should the school work with parents and caregivers to provide guidelines about AI use for school work at home? How can the educational system incorporate personalised AI tutoring tools like ChatGPT-Omni while fostering independent learning and social interaction among students?

Scenario 4

A teacher uses an AI tool to assess and evaluate the performance and progress of their students. The tool uses a webcam and speech recognition to capture and analyse the students' work and responses. The tool also uses data mining and analytics to generate and provide scores and feedback to the students and the teacher. The teacher uses the scores and feedback generated by the tool without reviewing or modifying them as it is the end of term and they are a bit tired. The students find some scores and feedback unfair, inconsistent, or irrelevant for their work and responses.

Some possible questions for discussion are:

• How should the teacher handle the situation? 

• How can teachers ensure that AI-generated assessment comments are fair and accurate?

• How should the school regulate and monitor the use of AI tools to assess and evaluate the performance and progress of students?  

• Should the school require or encourage the teachers to disclose and justify the use of AI tools and the sources of the scores and feedback they generate and provide? What are our ethical responsibilities?

• How can students be involved in the assessment process to ensure transparency and fairness? 

Broader Discussion

• What steps can be taken to balance the efficiency of AI-driven assessments with the need for human oversight and personalised feedback?

You could also create other scenarios about students using AI to create deepfake images of their teachers or peers, age restrictions on certain tools and open access on others, and students using VPN servers to still access tools even if the school restricts them. The possibilities really are endless.

Hopefully, the scenarios I have shared today will prompt deep dialogue and initiate an urgent discussion on how guidelines on generative AI use could be developed within the school community. If you have any other scenarios or have tried this approach, please let us know in the comments.

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In my next post, I will share my thoughts on what a flexible and supportive AI use policy could look like.

The first tool I used is called Research Rabbit. It is a web-based application that helps you find relevant papers for your research topic based on keywords, abstracts, or number of citations. You can also use it to snowball literature, which means finding more articles that cite or are cited by the ones you already have. Research Rabbit provides a visual summary that includes each paper's abstract, keywords, and citations so you can quickly decide whether it is worth reading. A particular strength of Research Rabbit is that you can select multiple articles and see how they are connected and then identify any key articles you may be missing in your literature review. I've also found it helpful how you can then sort the articles based on citations, ensuring you are referencing the seminal papers in the topic you are writing about.

The second tool I used in this workflow is called Lateral.io. When you have downloaded the PDFs of the literature you'd like to include in your review from Research Rabbit, Lateral.io can help you analyse and organise it. You can upload your papers to Lateral.io, automatically extracting key information such as authors, titles, abstracts, and references. You can also sort your papers into a bespoke literature review matrix, a table showing how each article relates to your research question, sub-questions, and themes. Lateral.io also allows you to annotate, comment, and share your literature review matrix with others.

Building on these two steps, I’d then use the literature review matrix I created in Lateral.io to scaffold the structure of my literature review and start writing!

Research Rabbit is completely free for students, and Lateral.io is also free to use. However, once you exceed the threshold of pages in Lateral.io, they'd like you to upgrade to a paid version. There is, of course, with all AI tools, a wide range of apps to support you in the literature review process, many of which overlap in functionality, and there may be one out there that can complete both of these steps without switching between tools. However, I felt these two tools worked quite well together, and I’ll continue to use this approach until I find a different way or someone suggests an alternative - please let me know in the comments if you have found a better approach! Good luck with your literature review 📝👏

The video below details each phase and how to use the tools.

Kickstart your literature review with AI🚀 - Watch Video

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A key point is that if you are going to use this approach, please attempt to understand and analyse the data yourself prior to analysing it with any AI tools, as we know errors are common with these tools, and they cannot be relied on for accuracy. Also, understand the ethical concerns of using AI tools to analyse your data. Always check the output for accuracy. I’d probably use this technique more as an idea generator or as a check for further themes once you have completed your initial analysis.

Here is a link to the document that includes the prompt ideas and fictional data you can experiment with to fine-tune the prompts to suit your needs.

**Update August 2024: I have now created a GPT that will analyse the data for you, it was built on the prompt linked in the document above. If you’d like more control and would like to understand a bit more about the process use the prompt, if you’d like to just throw in the data and watch GenAI work it’s magic then use the GPT!**

Analyzing Qualitative Data: Two Approaches - Watch Video

As you can see in the video, I only have the 5-minute budget version of Loom, so the video is nice and short, but it cuts off before I finish talking about how I would have worked through different iterations of the thematic analysis. Here, you want to bring in your identified themes or research questions to ask if any of these themes can be identified - an iterative approach is important here to ensure you can find what you are looking for.

I’d like to see if anyone can modify and improve these prompts, so let me know in the comments if you have a go with this approach to using AI to help with qualitative data analysis.

Have a look at my previous guide on how to collaborate with ChatGPT.

… and, of course…

Links to the posts (which I’ve migrated from Blogger to here on Substack) are below, with a short summary under each one…

I’d been blogging since 2011 about the small changes that I’d been making in my practice but in the second year of my MEd, I was lucky enough to be selected to be a Tātai Aho Rau CORE Education eFellow (partly down to blogging and sharing my practice the previous years I think). This experience enabled a great deal of inspiration on how I could change my practice. I used the monomyth as a structure to construct the narrative along the lines of action research and followed the story of Star Wars to try and capture what I was doing!

In this first blog post, I write about the start of the journey and how I had begun to explore the concept of learner agency and how to foster it in my students. I co-constructed the concept of agency with my students and created an online learning community on Google+ (!) to further the conversation and interaction.

I was frustrated by the constraints and limitations, such as a rigid timetable and pressure from assessments, so I wrote about how I had attempted to move beyond these. Such as dedicating a specific time for students to pursue their own interests and using positive reinforcement rather than punishments. I also discussed learning how to balance agency with accountability and responsibility. Looking back on this now, I still talk with teachers who are exploring agency about how it is interpreted and enacted - what does it mean for you and your learners? It was valuable learning for me, and I’m happy to still have a record of it - although Google+ is now decommissioned (RIP!!)

Once we’d established what learner agency looked like in our learning space, we actually had to do something about it! I explored the challenges of understanding and adapting to the NCEA assessment system. The solution I came up with was to involve the students in co-constructing the curriculum and designing their own assessments - creating a bespoke path for every learner based on their preferences. This ended up creating more engaged, authentic and relevant learning experiences. However, I was also battling for structure and self-regulation in order to ensure effective learning. A large part of the reflection was based on the gaps in knowledge that some students experienced and begin to explore how to address these issues. This is still an area that we are exploring in classrooms. However, I have noticed it is being supported by more of the positive ‘learning language’ that is being used in schools with students - I’ve noticed this with my own children especially.

The summary was a great way to bring together my learning. Although I’d only had 12 minutes to present the work in the research strand at ULearn 2014, I indulged with a full 20 minutes in this video which was a real luxury. On the post, I linked every resource that I made throughout the inquiry, and that was very helpful when I was presenting at conferences or talking to other staff, as this was what people were most interested in!

Finally, after presenting my project and the cumulative learning I experienced at as many conferences, national curriculum groups, local educator group meet-ups, online ‘EdChatNZ’ webinars and through my blog, I was asked to summarise this again for an ‘EdTalk’ run by CORE Education. They weren’t keen on me explaining it through the Star Wars narrative, unfortunately (😆); however, the video is a good summary of my experience - I think we still show it as part of our digital and collaborative learning course. One thing that surprises me is how much my right eyebrow goes up and down when I talk, and reducing that has been a real focus for me over the last decade!!

What was the point of sharing all of this at the time?

Well, of course, it was to support my own learning and record the changes that were happening in my practice, as well as the opportunity to interact with others. I was genuinely so passionate about instigating change, especially as new schools like Hobsonville Point were just beginning with exciting and future focussed pedagogical models. I was worried that students in traditional environments would be left behind. I’m not sure if what I was sharing had an impact, and I haven't attempted to measure it, but the analytics show each post had over 3.5k reads, so hopefully, some read until the end and maybe considered their teaching practice. There have been some other benefits to develop from sharing my work online. The most important to me is that I met other people who I am still connected with professionally and personally; while Google+ hasn’t lasted the test of time, our relationships have! When you open up your personal thoughts and reflections, and someone else then reads them, you already have something in common when you connect with them either online or face to face, and this can be the foundation of a great relationship. I also used my blog as a professional portfolio, which helped me to showcase my pedagogical approach and reflective nature, and this has supported employment prospects and admission to further study.

You can tell I’m an advocate for sharing learning in open environments, so if you have a blog or reflection, feel free to share it in the comments, and I’ll try and contribute where I can.

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I was a bit lost on the platform when I first started, as there seem to be endless options, and the whole monetisation part threw me off - I was worried about sending a bill to everyone in my address book! However, after setting up a few different publications for a range of people these are my tips for educators. To start your publication you can follow this link.

After reading these tips please add your own ideas in the comments so we can build up a useful knowledge base!

1. Don’t worry about setting up ‘paid’ or connecting Stripe - skip that step. You can even turn off ‘pledge’ subscriptions as if this is active each new subscriber will be asked to pledge regardless of whether you have Stripe connected or not. Substack wants you to do this as they take a 10% cut. To make this worthwhile, you would have to sign up your whole school and everyone you know and then convince them to pay to read what you write. If the stats from Substack are correct, only 5% of subscribers cough up for a $5 paid monthly subscription, so really not worth your while unless you are convinced that people are dying to hear what you have to say. Also, I don’t believe that anyone writing about education should charge for their knowledge without good reason - what are we trying to do here; improve outcomes for learners or make money out of other educators by putting up a paywall blocking something helpful?

   

2. Define your purpose - The About Page. You’ll need to tweak some of the settings to reflect your style, purpose and personality. Head to ‘Settings’ on your ‘Dashboard’ and scroll down to ‘About Page’. You can only make these changes on the web/online platform, as the app seems to be more about consuming content than creating it.

   

   Originally I thought this was about ‘you’, as in your bio, but it is about your publication (we’ll come to ‘you’ in a minute). So you’ll need to add the info here that will cover what you are planning to write about and why others might find it interesting - are you looking for critical feedback? Do you want people to build on your ideas? What is your aim? Of course, be sensitive to your workplace and the ethical implications of naming them or any of your colleagues without permission. You can remove the ‘why subscribe part’, but I’ve left it in. The ‘About’ page on your ‘Site’ (the place that hosts your posts) is the page that people will navigate to if they have seen your posts and want to know more about what they are signing up for. Have a look at other publications for an idea of how you might like to update yours. If you scroll to the bottom of the ‘About’ page, you’ll notice that there is a section that describes the ‘People’, and that’s you!

   

3. Who are you? Your Profile. To update the ‘People’ section you’ll have to go to your profile by either following this link or going to the top right on the dashboard, clicking on the small multicoloured circle (or profile picture if you added your picture already), and then click on your name where your email address is. You can then edit your profile, add your social accounts and update your bio (which will appear on the ‘about’ page we were just editing). If you already have a bio and find out that it is too long to include, you can just ask AI to shorten it for you. I’d really recommend adding your profile picture, as that enables your readers to connect with you even more - you probably don’t look like an orange.

4. Adjust the automated email. You’ll notice that if you have signed up for any other publications, you would have received an automated email from the author. We need to update this as the default is very generic, and unless you actually are a robot, or an American, it will not sound like you. Go back to the dashboard, head to settings and scroll down to ‘Welcome email’ and ‘edit’.

   

   For inspiration on what to write here, go through your inbox and see what Substack writers have written to you in their automated ‘Thanks for signing up’ emails. You're aiming to match your writing style here. It just feels a bit less ‘spammy’ if it seems like it is coming from you rather than completely automated and out of the can.

5. Make your publication stand out. You can add a logo really easily in the ‘Settings’, and here Substack has plugged into an AI image generator to help you out. Either use this (avoiding all text in images, of course, as this looks completely weird) or make your own in a couple of minutes on Canva, as I did.

   

6. What’s it called? You might even want to update the name of your Substack from *your name’s* Substack, as this is what it will default to when you set it up for the first time. It’s easy enough just by changing the text in the first box at the top of ‘Settings’ on your ‘Dashboard’- I called mine ‘Future Learning’ as that was the name of my original blog and consulting company before I started my current role (Phoebe actually came up with this name, it was before the MOOC platform ‘Future Learn’ and I’d bought the URL so I just stuck with it!). It is important to take some time here as the short description appears on the ‘welcome page’, which is the page that everyone is directed to before they get to your Substack publication for the first time. It encourages them to sign up, so make sure the description is accurate and concise.

   

7. Have a play around. Scroll down and explore the other options (yes, avoid the temptation to turn on payments) and see what else you can tweak. You can play around with site design and add pages for different parts (like a website - e.g. you might have a special page for resources or book reviews, or article reflections). Fortunately, all the designs are clean and simple, so you won’t be tempted to make a ghastly page with comic-sans and a couple of windings thrown in for good measure. Have a go at adding ‘tags’ (think edu-jargon) and even import blog posts from Blogger, Medium and a couple of other sites if you have written there in the past. This will help your publication be discovered if that’s what you are after. Have a look around at the settings. It’s really hard to break anything since the options actually tell you if you are going into the ‘danger zone’. Just keep away from that part!

8. Write, share and subscribe to others. When you are happy with the set-up of your publication the next thing to do is write something (perhaps introduce yourself, share your philosophy of education, review an article or reflect on something that happened recently). Share it with your community of practice (more on this in another post), and subscribe to other educators to grow your learning. One last tip - you can send yourself a draft of your post before sharing it with your subscribers by going to the ‘Settings’ cog on the bottom right of the page where you write your post or go to the ‘preview’ in the top right and send a draft email.

   

   There are a few other options to play around with that I’ll explore in more detail in a single post, for example, ‘chat’, ‘notes’ and collaborating with others as ‘guest authors’, as I think they are important and will set Substack beyond other platforms in the future.

   Don’t forget to leave your comments and tips below, as I’m sure there are a couple that I would have skipped over!

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When I was at teaching college, I usually spent most of the day teaching or worrying about what I was going to say when I was teaching - the rest of the day was spent writing lesson plans and then reflecting on the lessons that I had planned. There was a lot of planning. If I’d had to do less planning, I’d probably have been able to spend more time reflecting and, in turn, make more effective progress. While lesson planning is a skill that should be developed with practice, AI can help us be more efficient with our time.

_{Image created by AI (Prompt: a teacher planning a lesson in front of the TV on a Sunday evening in a simplistic drawing style in tones of blue)}

A while ago, I tried asking ChatGPT to plan a lesson, and the result was ok, but I ended up having to write half the lesson plan to provide enough context for the AI to work with. I also only have access to the free 3.5 version, so I can’t access live web pages. Many lessons are planned around an online source or webpage, and this is where Microsoft Edge and Bing Chat enter the room.

Before you start using the tool, you have to install Microsoft Edge on your computer (it might already be there if you have a Windows machine), and you have to make sure that you have ‘page context’ turned on, and this will allow Bing chat to access the web page (see gif below). You’ll find that option in the ‘app specific’ settings menu. You then thoughtfully navigate to a topical webpage and create a specific prompt to create your lesson plan based on that source. Of course, before using any AI tools, you should be aware of the challenges associated with bias and misinformation. I wrote about that here

Once I had found a web page that I wanted to base my lesson on, I clicked on the Bing Chat function in the top left and typed in the prompt: “Create a lesson plan from this webpage. It is for year 10 geography students in New Zealand. You need to include a plenary task, as well as a main activity, some collaborative work and a summary activity. Include timings and possible extension tasks.” The response was quite good, and I’d have happily taught that lesson by following the plan. See attached transcript here.

However, I started to think about how I could bring more culturally responsive practice into the lesson, so I modified the prompt to include approaches from a book I’d recently read, “Base the pedagogy on the research from Russell Bishop in 'Teaching to the North East.” Now the results were even better and perhaps more suited to teaching in New Zealand, including activities that supported manaakitanga and whakawhanaungatanga. You could create your prompt even more effectively by adding specific learning outcomes, as well as contextualising your class by highlighting the varying needs that could be present with your learners.

I then started to think about how I could push it further and went to a YouTube video of Cyclone Gabriel and typed a similar prompt. The results were again quite good, so I tried to push it further and asked at what points I should stop the video for critical discussion, this was even more impressive as I was expecting a hallucination here as ChatGPT usually makes this up, but this was spot on and linked directly to the video.

I was also curious as to how Bing Chat had linked the lesson to ‘Teaching to the North East’, so that was my next question…

By exploring different prompts and delving into more impactful pedagogical methods, I believe this approach to lesson planning can be highly beneficial in revitalising your current teaching strategies or creating innovative ways to educate. As with any AI or technological tool, success depends on how it is utilised to enhance learning, which ultimately depends on the quality of the teacher who is implementing the tool. I’d be interested to see how others have used AI to support lesson planning - please share in the comments below!

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As of the 1st of June 2023, less than 10% of schools and universities have formal guidance on AI. So where does that leave us as educators? Do we embrace it and use it to enhance teaching and learning, or do we just pretend that it will go away if we ignore it (much like Microsoft Internet Explorer/Edge - disclaimer: this is my new favourite browser with the integration of Bing being able to analyse web pages in browser - never say never!)

Proceeding with caution will clearly be the key. We have little individual control over how the tools are developed or shared, but we can decide how we interact with them. We must also recognise that we have been using AI for a long time (spam email filters, mobile phone photography, Alexa, Google Maps, all social media, Amazon shopping recommendations, Music and podcast apps) and have already contributed to its development. However, we can be mindful of how we use the tools, especially when working with young people.

At this time, with very limited guardrails on AI, I think that these are the areas where we need to build awareness and understanding; in no hierarchical order, just how they came into my head:

1) AI literacy

This is for us as learners, teachers, and the learners we work with. We must role model a high level of AI literacy to support those we work with. AI will transform our lives. We must understand how to interact with tools thoughtfully and ethically so they can emphasise our best human traits and support us in areas we need assistance. (Image below was created by AI with the prompt “We must understand how to interact with tools thoughtfully and ethically so they can emphasise our best human traits and support us in areas we need assistance in a simple style”!)

2) Privacy and information sharing

How much are you prepared to give up to AI to get what you want? What happens to your data when you enter it into ChatGPT? Do you know what Bing does with your search terms? Do your school policies and procedures allow you to enter school data and student work into AI? As we become more comfortable with the idea of AI, we will share more with it to perhaps get more back. However, your data can be exploited by AI, especially if you enter personal and confidential data - this can be sold or even used for identity theft or fraud. Advancements in AI can also use biometric data (for example, how we unlock our phones with facial recognition or a thumbprint) or voice and location data to monitor our movements and breach our privacy. If you have used an online tool to track your genetic origins, this data could be used to discriminate you from services such as healthcare, insurance and finance in the future due to genetics. This impacts you and any of your relations born after you - a heavy price to pay to find out that you are 3% Celtic. (Image below created by AI with the prompt “keeping data safe online in a simplistic style”.)

3) Data sovereignty 

This is related to the previous point; however more specific in that understanding that knowledge held by some people, specifically in New Zealand, is a taonga. I am not the right person to share expertise on this, but from my understanding, AI does not have the right to tell stories that belong to Iwi and Hapū; context is so important, but once AI takes hold of this precious mātauranga it can rewrite it and tell it any way that it likes. Who decides what information goes into LLM, and how can we make sure that some knowledge remains sacred?

4) Digital divide

It is relatively free to use AI now, as long as you don't mind contributing your data to developing more powerful AI. However, even in a matter of months, there seems to be an endless plethora of tools that fit our exact needs, from sourcing and reading literature to creating bespoke videos that are all built on the openAI API but have that extra layer of usability. Almost all of these apps are freemium, meaning you can use them in a limited capacity. Currently, we can all access the 3.5 version of chatGPT, while people on the paid version can use the more advanced 4.0. How long is it until openAi stops updating the public open-access version? As other apps bring this in, it could well be a ''pay for'' service, and in the end, the level of AI support you can access will depend on your income, again creating a divide. It will be like the difference between being able to access a personal assistant and having to do it all alone. Making the growing education gap even more chasmic.

5) Bias and stereotype reinforcement

A colleague of mine, Sophia Zhang, gave a great talk at a recent AI in education forum held by academeyEX; Sophia shared that there is a significant gender gap: Only 16 % of all AI professionals are women. UNESCO's Director-General Audrey Azoulay states, "There is an urgent need to rebalance the situation for women in AI to avoid biased analyses and to build technologies that take into account the expectations and needs of all of humanity"". Human bias is built into AI through conscious and unconscious assumptions about race, gender, or other ideological concepts. While AI designers are attempting to avoid this bias by understanding more about the training data, creating diverse teams and enabling effective feedback loops, there is no quick fix that does not involve cleaning the entire dataset for AI. You can check for bias yourself, and a good learning activity would be to use an image creator to type in terms and discuss the potential bias with your students. The image below was created by Bing image creator with the simple prompt: Doctor. Why is one of the images just a drawing, while the others attempt to depict real people?

6) Reducing creativity and personal voice

There are two trains of thought here; if used proactively, it can amplify your creativity by using elements of AI to build on an idea or thought. However, it can be used lazily to create without stretching the imagination too far by crafting prompts that will give you something similar to what you hoped for without going through the process. For example, Microsoft is soon releasing their own design tool that will suggest integrating images, backgrounds and even hashtags. What will this do to our creative desires? I'm not sure at this stage, as it seems like it is human nature to take shortcuts, but it is also human nature to create, so we may have the chance to be more creative in an offline way. You could even build on your skills to be more empathetic to the people you love (note to self). 

This is only a short list, and there are many other challenges, such as hallucinations and false information, filter bubbles created through AI and on a larger scale, the cost to run AI, the energy sources used to power AI, and of course, the world'sworld's end is also a concern for some very influential people.

The most straightforward and common-sense approach to the challenges we face in education has been presented by UNESCO, built on the  Beijing Consensus. Reinforcing a human approach may be the only way to survive an unknown future alongside AI.

(This post was written by a human - as with all posts on AI, it will probably be out of date in about a week. In fact, I rewrote this a couple of times based on the rapid developments in AI! The video at the end was created by adding the text from the blogpost into Lumen5 which is linked earlier.)

The following guide highlights some prompts, some follow-up questions and most importantly, what you need to do next to follow up and build your knowledge. While you could ask ChatGPT to write much of the introductory sections of a research report, it won’t stretch your learning or be worthwhile if you hope to gain anything from your study.

I have been using these prompts with students working on practice-based research projects, especially in the early phases of testing an idea, exploring the literature and understanding more about appropriate methodologies. These prompts could be applied to school-age students embarking on project-based learning through masters-level educators looking to change education practices on a large scale.

Just a few pointers on prompt creation before we begin:

• Be clear and specific — avoid using vague or very open-ended questions

• Use technical language — this is where your expertise as an educator comes in; you know and understand the relevant terms and language in your field, and this will help with more accurate responses

• Provide context — this is critical and, again an area of your expertise that will create more bespoke responses

• Use a variety of prompts — this will take some experimentation but try different phrases and combinations of words based on the same line of inquiry.

To start with, I’ll always start by defining the persona or identity, for example;

Prompt: You are a NZQA level 9 masters level research advisor supporting me with my practice-based research project.

I’d then continue the thread, building on previous ideas. The guide below follows the process our students could take at the beginning stages of a research project. It isn’t expected that you’d complete this all in one sitting as there is too much to cover; however, you’d come back to it by saving your ‘chats’ in the left-hand navigation menu.

I. Introduction/scoping the challenge:

Prompt: What are some current trends or emerging issues in [Education in New Zealand/related to x] that could be relevant to my research project?

• Further prompts are required here to uncover potential areas you could look into based on your context and the areas you are passionate about as an educator. You would follow up by learning more about what has previously been done in this area and looking for evidence in your context that this could be a potential challenge/opportunity.

Prompt: How can I ensure that my research project is original and makes a valuable contribution to the field?

• Different prompts here would lead you into the following sections of the report, and the follow-up for you would be to start reading more about your chosen topic through the literature review.

II. Literature Review:

Prompt: What are the seminal peer-reviewed academic texts related to [x]

• You will probably have to reduce your topics so they are less complex here; the literature-searching function seems quite limited, so it would be worth simplifying it. There is the risk of hallucination here where ChatGPT will make up entire research articles, including the authors and the DOI, and they do not exist. I have linked an example here in the transcript created through these prompts. However, I have found that chatGPT is good at comparing and contrasting topics, so this would be worth exploring.I mentioned earlier that I wouldn’t link to other tools beyond chatGPT; however, Consensus is reasonable (even the free version) and more reliable at finding literature than ChatGPT.One other tool to help you explore the literature is Elicit.

Prompt: Are there any key scholars or research studies that I should be aware of in relation to my research topic?

• Further prompts might ask for DOIs or web links. Remember that if you are searching for articles online, it is always a good idea to start with a verified source, for example, an online library through your place of study. This way, the DOI and metadata will be correct. Your follow-up would be to read the articles or use a tool like ChatDoc to summarise and explore the pdfs. You can also use ChatGPT and other AI to help with APA referencing.

III. Methodology:

Prompt: [Type in your research question(s)]

• Further prompts would undoubtedly be helpful here to identify gaps in the research. An interesting idea would be to ask ChatGPT for subquestions based on an overarching research question. Your follow-up here is to understand more about the suggested questions or modifications of your overarching question and return to the literature to cover any gaps in your knowledge or explore if more justification is required.

Prompt: Ok, so I will go with the following research questions.Can you provide suggestions on suitable methods for each research question? [Insert questions here…]

• Further prompts could help you understand more about the methods and suggest peer-reviewed sources to support your learning. Your follow-up would be (you guessed it!) to return to the literature and explore how the selected methods could be applied in your context.

Prompt: Can you recommend any other research methods or tools that would be suitable for my project?

• Further prompts could explore questions you might ask in each suggested method or if any particular frameworks would guide the data collection. You would then follow up here by finding those sources and learning about them.

Prompt: With regard to the methods that have been highlighted, can you suggest ways to ensure ethical considerations are addressed in my research project?

• Although AI cannot give ethical advice, it can highlight some areas you would need to consider to maintain safety for your participants and yourself — as the researcher, you need to make the final call here.

Prompt: How can I effectively communicate the outcomes of my project to different audiences? My key stakeholders will be [x]?

• Further prompts could explore particular communication channels that your stakeholders use. Your follow-up here would be to gain direct feedback and ask your stakeholders how they prefer to communicate.

Prompt: How can I ensure that my research project aligns with the standards and requirements set out by NZQA for a level 9 masters degree?

• Further prompts could explore opportunities to build on the attributes mentioned in the response within the context of your research. Your follow-up here would be to look at your overall research plan and integrate opportunities to ensure you can complete the project successfully.

Hopefully, these prompts have helped you move your ideas forward. There are obvious limitations; for example, at each stage, you could ask for the source in chatGPT; however, I’ve found that it needs to be more accurate, and chatGPT will probably make something up. I will write a follow-up post focusing on data analysis, discussion and conclusions, and a few more tips that can help with the research process.

This post was written by a Human, and the version of ChatGPT that the prompts were tested on was ChatGPT Mar 23 version, which means this all might be irrelevant by the time you are reading it!

If you are interested in looking at the transcript developed using the prompts above, you can see that here. When using chatGPT and AI, please exercise caution and refrain from adding information that isn’t yours. It’s essential to be considerate and respectful of others.

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This picture is the actual view I was confronted with, walking into our local community college gym, aged 16.  A place where we had played volleyball, netball and cricket was now being used to measure how much we knew about the things we had spent our school lives practically doing inside this hall, to see if I could pass GCSE PE.  It is now clearly ironic the amount of time that we spent actively exerting ourselves and demonstrating competency in the ‘doing’ of the skill was now going to be measured by writing down everything we knew about it in 2hrs to double check we knew what we were supposed to know.  The previous 24hrs before this vista had seen me cramming the information written on tiny ‘flash cards’ into my short term memory, even up to the walk down the corridor to find my seat I was memorising the diagrams I had drawn about things I don’t remember now, and haven’t really thought about since.  It was probably erased from my memory as I already had spent the previous 48hrs remembering everything I needed to know that might come up in the test about Alfred Hitchcock and the film ‘Psycho’ for a media studies exam (sadly the copy of the film I had was recorded from the TV on VHS and missed the opening sequence, unfortunately the main question in the exam was ‘In the opening sequence of the film, describe the symbolism used to depict the narrative texts used in the film’), I’m not sure how I even managed to pass media studies with a B+, but the whole experience traumatised me enough to never want to watch any Hitchcock again and also forget most of the things I crammed into my brain over those intense stress ridden 2 weeks. (By the way I took the picture at my old school, Pilton Community College while I was relieving at the school years later if anyone is thinking, ‘how did Tim sneak a camera into the exam hall in 1998, they were hard to conceal in your clearfile pencil case?!)

Anyway beyond nostalgia I’d like to think that education and also assessment has changed quite a bit in the last 20 years, however, I’m not confident that it has - you could probably go into your school hall at the start of this summer and you’d be confronted with a similar scene?  Even now you might even be thinking about how you are going to train your senior students to be able to ‘endurance hand-write’ for 3hrs in their English Lit exam, as all they do now is prod at phones with their thumbs or tap away at their keyboards and they don’t have the stamina for it?!  As we know the NZQA triumphantly announced that they would be digitising all exams by 2020, which sounds like an excellent step in the right direction, however is this a just ‘substitution effort’ (like putting a maths worksheet on a google slide presentation), or is it the beginning of bigger and better things?

In this blog post I want to project past the digitising of exams and look at a longer term vision of where assessment could go?

Society has changed in the last 20 years, and so have the skills we are expected to have to fit into and contribute to this society - particularly the key competencies that are at the front part of our NZC.  Many people involved in education, and many outside of education would argue that these competencies and a wider range of 21stC or future focussed skills should be integrated into external assessment, and especially digital assessment.  However this overwhelming area is extremely challenging to unpack and discuss how subjective and intangible things can be measured or quantified.  From looking at several models from around the world the short answer is that there is no definitive way to accurately and rigorously measure future focussed (21stC) skills, what it will require is accepting that there will be a range of approaches, and accompanying this it is paramount that a mindset is developed that is prepared to try out new methodologies, and also acknowledge that not everything will be successful. (the long answer is below!)

Why external assessment?

‘Who is external assessment for?'  You may have different opinions to this but it very much seems to me that external assessment is for the benefit of others, and not the learner, it is to prove that the learner has acquired something on a nationally comparable scale, either for tertiary, UE or employers - external assessment doesn’t really make too much difference to the learner apart from causing huge stress and anxiety, and perhaps that is the problem - we are assessing in a way that is ‘just in case’ and not authentically applied in any sense.  This was cramming and it worked to scrape me through, but I don’t remember any of it now, is that the intention?...

I feel the pressure of externalisation has de-professionalised educators to make a judgement.  Do we not trust the most valuable asset in our education system to make the assessments they are qualified to carry out - or do we not trust the appraisal system and ITE of our educators to enable them to be professional enough to do this, as well as understand where 21stC skills fit in?  For that reason I strongly believe that whatever system or platform we create for our students in the future of assessment must be mirrored in our own personal and professional assessment of skills and pedagogy, in doing this we will also understand and acknowledge what it is to be a learner again.  If we are asking our learners to explicitly demonstrate these future focussed skills how are we as educators doing it?

What could exist that would be more learner centric?

Perhaps the most effective way to enable a future focussed assessment system will be to create an online ‘learning portfolio’, I would veer away from calling it an assessment portfolio as the intention is to capture the learning as it happens, rather than the summative effect of assessment - it is also intended to be a living reflection of the person that owns it, that travels with them on their journey.  The skills that are easiest to teach and easiest to test are now also the skills that are easiest to automate, digitise, and outsource - therefore there will be some complexity to this but through thoughtful and effective use of technology I believe that this concept is achievable.  Portfolios are certainly not a new idea (I have one that I started making at my primary school in a old cardboard box in the loft!), but we have not had the tools to make them fully integrated and reflective of the whole person, they have been made with either outdated slow and clunky interfaces (eg. MyPortfolio), or in clear files and folders which get stored under the bed or in the garage!  This future focussed portfolio will be built on the following things and will give a better idea of the whole learner;

1. Variety of inputs - inquiry based projects, personal work, personal interests (video of playing guitar, creating a piece of artwork or similar passions), external assessments (thinning out..), external examples of learning, integration of MOOC’s studied through Cousera or EdX, externally based measures of 21stC skills (Dweck Mindset test, Duckworth Grit test Etc.)  This is an expanding list and would be adaptable for the future….

2. Self, peer, and teacher judgements on Cognitive competencies (creativity, critical thinking), interpersonal (communication, collaboration, leadership and global awareness) and intrapersonal (growth mindset, grit, intrinsic motivation, metacognition).  Or simply stick with the KC’s that we have already which can widely accommodate these skills, qualities and attributes

3. Reflection of the region/school/CoL that the learner comes from, it will be personalised or based on the learning challenges that they have set as a collective, for us on the the east coast, what would the Tairāwhti graduate look like?

4. A reflection of the endorsement economy for each individual user (UBER/TradeMe/AirBnB etc.), much like peer ratings or feedback.

5. The capacity to be shared, private or open in various parts.

6. The capacity to be connected to LinkedIn and Seek, and even have an API that will enable this to be searchable within these applications - this would be a way to enable the best graduates find the best suited jobs, learners could almost be ‘headhunted’ from 15 or 16 for the best creative and innovative jobs.

7. Interactive and living.

8. An overarching element of gamification, eg. progress bar to incentivise you completing your profile, use of the appointment mechanic to encourage learners to add something each month or term, badges could also be included?

9. The capability to accommodate learning analytics when they are good enough to tell a story about the attributes of a learner in the future (here is a post I wrote about this a couple of years ago).  I think this is a huge area and I am quite confident with the emerging trend of the quantified self that this will be a leap in the analysis of learning - combined with machine learning and the ability to customise the paths of learners to an extremely personal level with tools like galvanic skin response we will be able to quantify many of the physiological and cognitive responses to learning, and record and reflect on the process that each individual goes through.

As I mentioned I believe an underpinning factor of making this work will be if educators are also using this online learning portfolio to record their self, peer and student reflections.  This will not only ensure that this is a tool that has longevity, but our educators will also understand the importance of making these interactions rigorous and in depth.  Our educators should now move away from ‘this is how I will assess you at the end of the term’, with a system like this it should be ‘how will we assess each other?’, how will you let me know that I am doing my job in assisting you to learn, and this will also be reflected in the teacher ‘learning portfolio’.

Imagine if every learner in New Zealand had a unique and individualised portfolio that was exportable to the rest of the world, we have some extraordinary young people here, we only have to help them prove this to the world - I think that this could be a mechanism that will enable us to celebrate the unique individuals that we have in our education system - a one sized approach will not enable this to flourish.