Creativity, Divergent Thinking, and AI

José Antonio Bowen and C. Edward Watson have a new book out called Teaching with AI: A Practical Guide to a New Era of Human Learning. I am very impressed with Bowen and Watson and their publisher, Johns Hopkins University Press, for putting together such a comprehensive book on generative AI in higher education so quickly. I worry it will be out of date in a year, but right now, in the summer of 2024, it's an impressive orientation to teaching and learning in this new age of artificial intelligence.

I spent two days this week at Abilene Christian University leading workshops at the Adams Center, the ACU center for teaching and learning. I enjoyed my time there, not only interacting with the faculty and staff in the workshops, but reconnecting with friends and colleagues in Abilene whom I hadn't seen in a while. The workshops weren't all about AI, but the topic certainly came up more than once. I've only read about half of Bowen and Watson's new book, but I ended up sharing several insights from the book that were relevant to our conversations.

For example, Bowen and Watson talk a lot about creativity in the book, and generative AI's roles in creative processes. Drawing on the field of design thinking, they write, "Most problem solving... is a combination of both divergent thinking (what might I be missing? how else could I look at this?) and convergent thinking (what is the best solution?)." A design process might involve a phase of brainstorming lots and lots of ideas (divergent thinking), followed by grouping of those ideas into three or four main categories (convergent thinking). Where might AI come into this process? "Since AI is such a prolific idea generator," they write, "it is especially useful in the divergent phases of this process."

I shared this with my colleagues at ACU when we were talking about ways AI might be useful in own work as academics. When designing a new course, we might turn to a ChatGPT or Claude and ask it for topic ideas for the course, knowing that these tools can generate lists and lists of such ideas. That's the divergent phase. When it comes time to evaluate those lists and decide which topics should actually be in the course, that is, the convergent phase, we're more likely to rely on our own expertise than ask the AI for help.

This notion that generative AI can enhance divergent thinking shows up in a recent study by Sabrina Habib and colleagues published in the Journal of Creativity (and recapped by me on my blog last month). They had students in a course on creative thinking and problem solving generate novel uses for everyday objects like bricks and pens and paperclips. They found that students who uses AI as part of that brainstorming came up with more ideas and with ideas more quickly than students not using AI. However, the really out-of-the-box ideas came from the students not using AI, and some of the students using AI reported that it was harder to generate new ideas after having seen the AI-generated list.

That points me back to how Bowen and Watson described divergent thinking in that passage I quoted above: "What might I be missing? How else could I look at this?" As is often the case in brainstorming, it's more useful to see what you can come up with yourself before turning to other sources, whether those other sources are people or AI chatbots. This is useful to remember as we academics start to incorporate AI into our work and especially useful as we teach students about using AI thoughtfully and effectively.

For more on the role of generative AI in creative work, check out Teaching with AI by Bowen and Watson. It is, on the whole, very positive about the potential of AI in higher education, so if you're looking for a critical analysis of the problems with AI, this isn't that book. And for more on AI and creativity, listen to my interview with Garret Westlake, executive director of the da Vinci Center for Innovation at Virginia Commonwealth University, on episode 21 of the Intentional Teaching podcast.

A Call for Jamboard Replacements

You may have seen the news that Google Jamboard is going away this October. Many of us discovered Jamboard back in 2020 when we were looking for new digital teaching tools, and it quickly became one of my favorite such tools. It's a very easy to use whiteboard-style collaboration tool, and I use it workshops all the time. I was disappointed to hear that Google would be discontinuing it later this year, but I wasn't surprised, given Google's track record of sunsetting really useful tools. (RIP, Google Reader.)

One of my goals this summer is to find another tool that I can use in place of Jamboard going forward. I'm going to dig into some candidates, including the ones that Google tells me I can migrate my jamboards into: FigJam, Lucidspark, and Miro. However, if you know of other potential Jamboard replacements, please let me know so I can try them out, too! I'll report out on my experiments in a future newsletter.

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