Some notes on generative AI in higher education

I've been a little quiet on the blog this week, as I've been concentrating on reducing my end-of-trimester marking load. However, I came out of exile today to contribute to a discussion on generative artificial intelligence in higher education, for staff of Waikato Management School. The risk with any discussion of AI is that it degenerates into a series of gripes about the minority of students who are making extensive use of AI in completing their assessment. I was type-cast into being the person to talk about that aspect (in part because I will be doing that next week in a discussion at the School of Psychological and Social Sciences). However, I wanted to be a bit more upbeat, and focus on the positive aspects of AI for academics.

I don't consider myself an expert on AI. However, I have read a lot, and I pay attention to how others have been using AI. I've used it a little bit myself (and I'm sure there is much more use that I could make of it). I made some notes to use in the discussion, and thought I would share them. I link to a few different AI tools below, but those are by no means the only tools that can be used for those purposes. Where I haven't linked to a specific tool, then a general-purpose generative AI like ChatGPT, Claude, or Gemini will do the job.

I see opportunities for generative AI in four areas of academic work. First, and perhaps most obviously, generative AI can be used for improving productivity. There are many tasks that academics do that are essentially boring time-sinks. If we adopt the language from David Graeber's book Bullshit Jobs (which I reviewed here), these are tasks that are essentially 'box-ticking'. Where I am faced with a task that I really don't want to do, but I know that I can't really say no to, my first option is to outsource as much of it as possible to ChatGPT. "You want me to write a short marketing blurb for X? Sure, I can do that." [Opens ChatGPT].

Aside from avoiding bullshit tasks, there is lots of scope for using generative AI for improving productivity. I'm sure that a quick Google search (or a ChatGPT query) will find lots of ideas. A few that I have used, or advocated for others to use (because I'm useless at following advice that I freely give to others), are:

• Brainstorming - coming up with some ideas to get you started on a project. If you have the idea, but are looking for some inspiration, generative AI will give you some starting points to get you underway.
• Writing drafts - sometimes generative AI can be used to create the first draft of a common task, or to create templates for future use. For example, I got ChatGPT to re-write the templates that I use for reference letters for students, and for supervision reports for my postgraduate students. I can then adapt those templates as needed in the future.
• Editing - sometimes you have an email that you need to send, and you want to use a particular tone. With a suitable prompt, generative AI can easily change the tone of your email from 'total dick' to 'critical but helpful' (I may need to use this much more!).
• Condensing or expanding - Academics will often use ten words when four words would be enough. Generative AI can do a great job of condensing a long email or piece of text. On the other hand, if you need to expand on something, generative AI can help with that too.
• Summarising or paraphrasing - On a similar note, generative AI can help with paraphrasing long pieces of text, or summarising one or more sources. Some good tools here are Quillbot for paraphrasing, Genei for summarising text, or summarize.ing for summarising YouTube videos.
• Translation - Going from one language to another is a breeze. It may not always be 100% accurate, but it is close enough.

Second, generative AI can be used for teaching. Here's a few use cases in the teaching space:

• Writing questions or problem sets - generative AI can write new questions, but they aren't always good questions. However, it can be used to generate new context or flavour text on which to base a question, which is pretty important if you want something new but are feeling uninspired. Also, creating a problem set or quiz questions (multiple choice, fill-in-the-blanks, true/false) is fairly straightforward, by uploading your notes or lecture slides. However, I wouldn't use those questions in an online testing format (more on that when I post about assessment next week).
• Writing marking rubrics - With a short prompt outlining the task, the number of cut-points and marks, ChatGPT created the first draft of all of the marking rubrics for the BUSAN205 paper in A Trimester this year. I had to cut back on the number of criteria that ChatGPT was using, and modify the language a little bit, but otherwise they were pretty good.
• Marking to a rubric - Once you have the rubric and the student's submitted assessment, generative AI can easily mark the work against the rubric. You would want to check a good sample of the work to ensure you were getting what you expected, but this could be a huge time-saver for marking long written work (provided you can believe that the work is the student's own, and not written by generative AI!). In case you are wondering, I didn't do this in BUSAN205 (it didn't occur to me until this week!).
• Lesson plans - Creating lesson plans (which is more often a primary or secondary school approach to teaching than in higher education) is a breeze with generative AI. Just tell it what you want, and how much time you have, and it can create the plan for you. One useful tool is lessonplans.ai.
• Lecture slides - Most of us probably write our slides first, and write notes second. However, if you have the notes and want slides, then generative AI can save you the hassle. And the end product will likely be better than anything you or I could create (as well as conforming to recommendations like limits on the number of bullet points on a single page, etc.).

Third, generative AI can be used for assisting student learning (this is separate from students using it for completing assessment tasks). I can see two good use cases here:

• As a personal tutor - Using a tool like coursable.io or yippity.ai, students can create personalised flash cards or quizzes for any content that they upload. A link from your learning management system could point students to these useful tools
• Creating your own finetuned AI - This is one use case where I am very excited. By uploading my lecture slides, tutorials, transcripts of my lecture recordings, and posts from my blog, I think I can probably finetune my own AI. What better way for students to learn that from the chatbot based on their lecturer? I will likely be playing with this option over the summer.

Fourth, generative AI can be used as a credible research assistant. However, like any human research assistant, you would be wise to avoid taking anything that generative AI provide you uncritically. Applying high standards of due diligence will help to minimise problems of hallucination, for example (for comparison, I'm not sure what the rate of hallucination is among human research assistants, but I'm pretty sure it is not zero). Aside from some of the use cases above, which could apply to research as well, I can see these options:

• Literature review - It's far from perfect, but tools like Elicit or Consensus do a credible job of drafting literature reviews. It would provide a good base to build on, or a good way to identify literature that you might otherwise miss.
• Qualitative data analysis - Some of the most time-consuming research is qualitative. However, using a tool like atlas.ti, you can automate (or semi-automate) thematic analysis, narrative analysis, or discourse analysis (and probably other qualitative methods that I don't know the names of).
• Sentiment analysis - Sentiment analysis is increasingly being used in quantitative and qualitative research, and generative AI can be used to easily derive measures of sentiment from textual data. I'm sure there are lots of other uses cases for textual data analysis as well.
• Basic statistics - I've seen examples of generative AI being used to generate basic statistical analyses. This is particularly useful if you are not quantitatively inclined, and yet want to present some statistics to provide some additional context or additional support for your research.
• Coding - Writing computer code has never been easier. Particularly useful for users of one statistics package (like R or Stata or Python) wanting to write code to run in a different package.

Anyway, I'm sure that there are many other use cases as well, but those are the ones that I briefly touched on in the session today. I'll be talking about the negative case (the risks of generative AI for assessment, and how to make assessment more AI-robust) next week, and I'll post on that topic then. In the meantime, try out some of these tools, and enjoy the productivity and work quality benefits they provide.