R3 1.14 July 27, 2023: Seven Approaches for Teaching with AI; Other Favorite Resources
Seven Approaches for Teaching with AI; Other Favorite Resources
The time is right to start moving from the general to the specific within the AI discussion. Now that the fall class planning season is underway, I’ve been particularly keen on articles that offer concrete suggestions and examples of what AI looks like when it’s used in actual activities and assignments. These examples are what I think faculty will find most helpful as they consider what AI tools could mean for their own teaching practices and scholarly work. In particular, I think the first steps we faculty ought to take – after our first pass of exploring the basics of how they work – is to envision how AI would be used within one’s own academic discipline. For me, that vision inspires ideas that I can start putting into practice in the semester to come.
With that, here is one particularly interesting article offering a framework for teaching with AI, along with (highly useful) example prompts to use within systems like ChatGPT or Bard. It features a wealth of the kind of detailed ideas I’ve been looking for, and even better, these are organized into a conceptual framework that maps quite well onto college pedagogy.
Citation:
Mollick, E. R., & Mollick, L. (2023). Assigning AI: Seven Approaches for Students, with Prompts. SSRN Electronic Journal, 1–46.
DOI:
https://doi.org/10.2139/ssrn.4475995
Paywall or Open:
Open
Summary:
This article aims to give a detailed, practical framework for using AI in teaching, with the two main goals of helping students “learn with AI and to help them learn about AI.” The authors’ suggestions are organized into seven distinct approaches, each with a particular role for AI and a particular pedagogical purpose. These different applications of AI in teaching are interwoven with suggestions for managing down sides and dangers, particularly the risk of students placing too much trust in AI-generated output. Example prompts (i.e., text that is input into an AI tool to produce output) are given for each of the main approaches.
Key Concepts:
The authors’ framework for different AI uses casts AI tools in seven possible roles, each with a specialized purpose. These include
1. AI as a tutor - for building knowledge
2. AI as a coach – for building metacognition
3. AI as a mentor – for providing feedback
4. AI as a teammate – for building collaboration intelligence/skill
5. AI as a tool – for extending student performance/what students are able to do
6. AI as a simulator – for engaging students in practice
7. AI as a student – to check for understanding
Along with descriptions and examples of each, the authors list pedagogical benefits and risks. For example, role #5 above (AI as a tool) can help students accomplish more in less time, but could inappropriately encourage them to outsource higher-level thinking rather than just lower-level routine work.
Choice Quote from the Article:
While our guidelines for students differ with each approach, in each set of guidelines we focus on helping students harness the upsides while actively managing the downsides and risks of using AI. Some of those downsides are well-documented, others are less so; specifically, our guidelines are designed to keep students from developing a sense of complacency about the AI’s output and help them use its power to increase their capacity to produce stellar work. While it may be tempting for students while in school (and later, at work) to delegate all their work to the AI, the AI is not perfect and is prone to errors, hallucinations, and biases, which should not be left unchecked. Our guidelines challenge students to remain the “human in the loop” and maintain that not only are students responsible for their own work but they should actively oversee the AIs output, check with reliable sources, and complement any AI output with their unique perspectives and insights. Our aim is to encourage students to critically assess and interrogate AI outputs, rather than passively accept them. This approach helps to sharpen their skills while having the AI serve as a supportive tool for their work, not a replacement. Although the AI’s output might be deemed “good enough,” students should hold themselves to a higher standard, and be accountable for their AI use.
Why it Matters:
At this point in the development of educational AI, a major barrier is the overwhelming and sometimes confusing array of advice presented to instructors. I predict that some faculty will give up on incorporating AI into their fall teaching, not because of philosophical objections or technological limitations, but because they simply don’t know where to begin. This is why I particularly appreciated the idea of offering a conceptual framework – essentially, a way of dividing and conquering all the possible applications of new AI tools in all possible teaching situations. I also applaud the theoretical backing the authors offer for different facets of the framework (e.g., citing Metcalfe’s work on feedback to support the idea of using AI as a mentor).
I think that these ideas will be especially helpful when coupled with the advice to look at AI through the lens of one’s academic discipline – i.e., how AI might be productively put to use by professionals working in a particular field. The seven principles in the article could be good prompts to get faculty coming up with ideas, perhaps with the advice to first choose one of the seven approaches, then consider how that approach might look when implemented in a particular course or discipline. I also like the idea of asking faculty which of the seven arenas (building knowledge, metacognition, collaboration and so on) are the most difficult to address in a given course, then having them zero in on the corresponding approach.
Lastly, I thought the inclusion of specific prompts as well as instructions for students was an enormous strength of the article. Like the seven different approaches themselves, I think these details will be inspirational. Even if faculty don’t want to or can’t use these materials verbatim, they can use them as models of how to make their visions of AI-supported teaching a reality.
Most Relevant For:
Faculty preparing to use AI in classes; instructional designers; faculty professional development directors and other leaders responsible for guiding policy and practice relating to AI
Limitations, Caveats, and Nagging Questions:
Let me preface these limitations by saying that this article is hands down the most detailed, realistic, and well-thought out guide to general AI teaching practice that I have seen so far.
With that, here are a few things to keep in mind. The level of detail is a strength, but means the article is probably a bit long for some faculty to tackle, especially as the fall planning rush gets underway. Faculty PD directors and others looking to provide resources to time-pressed faculty might want to distill the main points and provide those as a starting point, rather than sharing the whole article. The summary in Table 1 (p. 4) could be a helpful component to include in a condensed overview. So could the concise list of risks given on page 5.
When using this article as a basis for faculty workshops or trainings, it will be a good idea to avoid implying that this list of seven approaches are an exhaustive list of all possibilities. No doubt we will see many more frameworks, lenses, and theories for understanding AI in education. This one is a powerful start, but won’t be the last word on teaching with AI.
Other Current Favorite AI Resources:
Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3). https://doi.org/10.30935/cedtech/13176
Darby, F. (2023, June 27). 4 steps to help you plan for ChatGPT in your classroom. The Chronicle of Higher Education. https://www.chronicle.com/article/4-steps-to-help-you-plan-for-chatgpt-in-your-classroom?cid=gen_sign_in&sra=true
Gurung, R. (2023, January 17). Tackling ChatGPT head on: A student assignment. OSU Center for Teaching and Learning. https://blogs.oregonstate.edu/osuteaching/2023/01/17/tackling-chatgpt-head-on-a-student-assignment/
Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., Tlili, A., Bassanelli, S., Bucchiarone, A, Gujar, S., Nacke, L.E., & Hui, P. (2023). Exploring user perspectives on ChatGPT: Applications, perceptions, and implications for AI-integrated education. Preprint/early stage research posted by the authors. Retrieved from http://arxiv.org/abs/2305.13114
Stanford, D. (2023, July 12). Incorporating AI in teaching: Practical examples for busy instructors. Daniel Stanford’s Substack.
UMass Amherst Writing Program (n.d.). The current conversation about ChatGPT. https://www.umass.edu/writingprogram/chatgpt/current-conversation