In January 2023, I wrote a post titled Practical Strategies for ChatGPT in Education. At the time, ChatGPT had just been released, and educators were grappling with its implications. The post outlined six strategies where ChatGPT could be used: planning, refreshing, improvising, personalising, collaborating, and communicating. It quickly became one of the most trafficked parts of my site (along with the Teaching AI Ethics series) and has since turned into a webinar, a face-to-face professional learning session, and core part of my book Practical AI Strategies. It’s also a module in my online course of the same name, which has hundreds of educators learning to work with GenAI.
A year and a half later, it feels like the ground beneath our feet has shifted dramatically. We’ve seen the release of more powerful models like GPT-4, 4o, and Claude 3.5 Sonnet, as well as significant advancements in their capabilities. It’s time to revisit and update the Strategies for using GenAI in education.
Moving on from the original six strategies of planning, refreshing, improvising, personalising, collaborating, and communicating, I’m going to explore six new areas where educators in K-12 and Higher Education might make use of Generative AI. Like the first series, this is very much focused on the educators using the technology for themselves, not with students in the classroom; I still believe this is the best way to introduce the tools.
These strategies focus on text-based Large Language Models (LLMs) like GPT and Claude (I’ll leave multimodal GenAI for another time), emphasising how educators can use these tools to enhance their practice. The six new strategies are:
- Designing
- Differentiating
- Engaging
- Imagining
- Editing
- Evaluating
In this post I’ll focus on the first three strategies.
Designing
AI-assisted curriculum design has come a long way since early 2023. Recent LLMs can now analyse curriculum documents, suggest more robust cross-curricular connections, and incorporate contextual materials uploaded by users such as PDFs, Word Docs, images, and spreadsheets. This allows educators to create more dynamic, adaptive curriculum resources, plans, and materials.
Technology companies have been quick to propose tools which take the “work” out of lesson planning, or simply “add sparkle” to existing materials. But I think we can do better, and many educators agree.
Drawing on language from multiliteracies and multimodal discourse, I think of curriculum design as a process of creating meaning through multiple modes of communication, and drawing on the vast range of professional expertise in the teaching profession. This isn’t just click-a-button lesson plan generation: it’s a more thoughtful use of the technology which doesn’t replace the educator.
Example prompts:
Analyse the <Jurisdiction> Curriculum for Year 8 Science and suggest three cross-curricular connections with Mathematics. Use internet browsing to verify your points and provide links to the appropriate standards. Create a table of comparisons in markdown formatting.
<Upload curriculum documents and design thinking cycle information as PDFs> Based on these curriculum documents, suggest a unit plan for a 6-week cross-curricular project linking Science, Technology, and Art for Year 9 students. The unit will be part of a term-long focus on STEAM education and should be based around the attached design thinking cycle.
Differentiating
Recent developments in AI have significantly improved its ability to work with individual needs. While we should be wary of the hype around “personalised learning” (and I used the term ‘personalising’ in the original Strategies), GenAI can be a powerful tool for differentiating content and instruction.
LLMs can help educators adapt materials for different learning styles, abilities, and backgrounds. They can suggest modifications to lesson plans, generate varied practice exercises, and even predict potential learning obstacles. However, it’s crucial to remember that AI cannot replace the nuanced understanding that educators have of their students’ needs. LLMs, of course, have no lived experience and no physical understanding of the classroom environment.
Example prompts:
Generate a set of practice problems on algebra for Year 8 students, ranging from basic to advanced difficulty. The student is required to expand the expression and solve a linear equation. Create three sets of ten questions of increasing difficulty. Use code interpreter [ChatGPT feature] to check answers and generate an answer sheet. Use code interpreter to create two documents for download with questions and answers.
<Upload deidentified student data and lesson plan> Based on this data and lesson plan, suggest differentiation strategies for students who are struggling with <concept> and those who need additional challenge.
Engaging
While much of the focus on GenAI in education has been on boring chatbots and homework helpers, these tools have the potential to create much more engaging learning experiences. However, we must be careful not to conflate engagement with entertainment or assume that AI-generated content is inherently more engaging than traditional methods: there is simply no need to pour AI special sauce over every lesson.
LLMs can help educators design more interactive and varied learning activities that don’t actually use AI, create contextually relevant examples, and generate thought-provoking questions. The key is to use these tools to support and enhance engaging pedagogical practices, not to replace them.
Example prompts:
Suggest five interactive activities to teach the water cycle to Year 5 students.
<[Upload unit plan> This was a largely successful unit of work, but towards the end it started to become a bit tedious. Based on this unit plan, suggest ways to incorporate elements of real, immersive, and engaging storytelling to increase student engagement with the material.
Part two coming soon
In the second half of this post I’ll be exploring imagining, editing, and evaluating, three more strategies where GenAI could prove genuinely useful for educators.
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