Short answer: all of the above. This is the second year UCF has held the conference, and it has grown significantly. More than 800 people from nearly all 50 states showed up for more than 200 presentations. I was struck by the pragmatic tone many faculty members took. AI is here to stay, they said. Employers expect new hires to have an understanding of the technology. Students are both intimidated by and curious about AI. As a result, attendees said they believed they had a professional responsibility to learn how the technology works and, where appropriate, use it in their teaching.
In the coming weeks I’ll explore in detail what your colleagues at other institutions are doing with AI, and share resources to help you figure out what your approach to the technology will be. For now, I’ll share some takeaways from the conference.
You can’t neutralize AI — so lean in. The ubiquity of generative AI is growing. It’s in our search engines. It’s in digital textbooks. It’s in learning-management systems. TikTok, meanwhile, is flooded with videos on how to use ChatGPT to do your homework.
In the opening session, Kevin Yee, director of UCF’s Faculty Center for Teaching and Learning, called this a Promethean moment. The future is going to be some sort of combination of human intelligence plus AI. Academe has a responsibility to understand the tools, explore the ethical dimensions of their use, grasp how to apply AI, and help students and society navigate a way forward.
AI is going to alter both the process and the products of learning. Because ChatGPT, Claude, and a host of other tools can easily mimic human writing, professors must get creative with their approaches to assignments and assessments. A big challenge will be how to evaluate students’ learning when it’s done with the assistance of AI. One presenter described how students used a log book to record the iterative process of working with AI, which helped show how their thinking had evolved. Others focused on how to create alternatives to the traditional research paper, such as podcasts, debates, digital storytelling, and games.
AI can help students refine their ideas. Several presentations focused on how AI can be a great tool to help students get out of the gate. One presenter, who had designed a project in which students use AI to develop research papers, said using AI to brainstorm research topics helped cut down on “screwing around” time. After all, how do students typically come up with ideas for papers and research projects? Often it starts with random Googling. In other sessions, professors said students had found AI valuable in helping them organize and edit their original work.
Students are as skeptical as anyone about AI. Several presenters said that working with AI tools had led students to see the tools’ limitations. In one session, which focused on using AI to turn research papers into podcast scripts, a professor described how students had found AI-generated scripts that were based on the students’ work to be flat and overly general on the first pass. Some preferred to write the script on their own; others spent a lot of time refining the script with support from AI.
Effective use of AI requires critical thinking, information literacy, and solid writing skills. That may have been the most significant point presenters made throughout the conference. In order to use generative tools well, students still need to employ higher-order skills. That includes knowledge of the subject, an awareness of what they’re looking for, and an ability to evaluate the AI’s output. This can’t happen without students’ having done a significant amount of work on their own.
Entirely AI-generated output is the new “C.” That was another common discussion point. Yes, some students will try to outsource everything to AI. The result will be mediocre, especially when their classmates are putting in effort. Anything that AI can do on its own should therefore become the new C (or D). And while C’s may be passing grades, several presenters pointed out that students only risk making themselves irrelevant if they have nothing to contribute but what anyone could generate with AI.
Two big questions stood out:
- How will AI affect the teaching of foundational skills? If AI can do a lot of elementary work, such as summarizing, outlining, defining, and classifying, does that mean students can skip to the next steps? Or do they still need to learn those skills? And if so, how should AI be treated in gateway courses?
- Who is going to train professors to teach with or about AI? I met several people whose institutions had little to no budget for such work. And given how quickly the landscape is changing, it seems as if one-time workshops might not be enough. Add to that the lack of time and energy many instructors have to devote to professional development, and we’re facing a problematic fall semester.
If you have thoughts on how you plan to respond to AI in your teaching, write to me at beth.mcmurtrie@chronicle.com, and your story may appear in a future newsletter. And if you have found helpful resources to get you up to speed on AI, please share them with me, and I can pass them along to newsletter readers.
Resources on AI
Here are some resources cited at the conference and elsewhere:
“AI Hacks for Educators: Practical Tips to Save Time by Using GenAI,” an open-source, downloadable guide written by Kevin Yee, Laurie Uttich, Eric Main, and Elizabeth Giltner of UCF.
Teaching With AI: A Practical Guide to a New Era of Human Learning, by José Antonio Bowen and C. Edward Watson. You can also find AI prompts and further detail on AI tools on Bowen’s website.
“Teaching Repository for AI-Infused Learning.” This UCF project is just getting off the ground. If you’d like to submit a project, see the website for details.
“Generative AI Product Tracker,” by Ithaka S+R, can help you stay on top of the tools.
“Syllabi Policies for AI-Generative Tools.” This crowdsourced document was created by Lance Eaton at College Unbound.
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— Beth
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