Many academics are worried about the threat to assessment that’s posed by generative AI models like ChatGPT, Claude, Gemini, and Perplexity. And while that’s a valid concern, there’s much less discussion about how AI might impact the work of academics. In this post, I share a few very simple examples of how you might consider getting started with generative AI in your scholarship.
Examples of academic use-cases
- Writing. Improve clarity, structure, and grammar in your writing. Create outlines or drafts of work you know well, as you can be more confident that you’ll spot errors.
- Research. Use conversational prompts to generate insights, lines of inquiry, ideas for research papers, and outlines of grant proposals.
- Literature reviews. Extract and summarise the key points from research papers and reports.
- Assessment. Generate a list of questions for quizzes and discussion in class, based on material you provide to the AI.
- Summaries. Create summaries of documents, presentations, and lectures. Ask AI to create 1000-word summaries of lectures and post those summaries to the learning management system for students. Or summarise a conference presentation for my blog.
Simple prompts to get started
- “Here is an example of a piece of writing I’ve produced. Please summarise the document in one paragraph that I can use for an Introduction.” Or, ask the AI to identify the strengths and weaknesses of the writing, with suggestions for improvement.
- “Summarise the key ideas about online learning from the attached journal article in two paragraphs.”
- “Give me give ideas for a presentation that addresses both the benefits and ethics concerns of new technologies in healthcare.”
- “Here is the text of a lecture I’m giving next week. Please give me a list of 10 questions I can share with students to help generate discussion on this topic.”
Getting started with generative AI is as simple as setting aside a couple of hours for experimenting with the technology. This will help identify what it’s good at, and where it fails.


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