Beyond Being Creative
Weighing how AI works for the creative learner
Welcome back! In this series, we take a closer look at how education is shifting as a result of generative AI tools like ChatGPT. Our previous articles touched on how instructors might co-design curricula alongside AI and how researchers consider supercharging inquiry with AI partners. Today, we explore the role that new AI might play in the creative process undergirding both of these areas.
On the surface, it’s all about tools. A new wave of AI apps is enabling creative minds to co-build 360 images, hybridized poetry, stylized videos and even 3D objects on their own. Writers and artists are teaming up with AI engines like ChatGPT to jump-start their design process and evaluate their paintings. In some cases, AI tools are being integrated throughout the entire creative cycle.
But what does this mean for students and educators? Where does the creative mind and learning self fit in an increasingly AI-driven world? More specifically, what do humans bring to the party, creativity-wise? Three aspects of the creative process that might be worth a closer look include: how we question, how we elaborate, and how we nudge.
How we question
Inside the creative process, questions can serve to pivot and to propose, to frame and reframe. Traditionally, this has been a human-to-human affair. But when a thought partner is not available, AI programs can also help to pose questions that inspire more human questions, facilitating loops of iterative inquiry. When the creative process becomes too knotty, AI has also shown the capacity to narrow down ideas and surface hidden insights in complex data.
With this in mind, what types of questions might learners be most responsible for? Ambiguous questions require one to wrangle with uncertainty, which can be problematic for computers. Questions that make intuitive leaps are difficult to explain, much less to program. Lastly, questions that reframe a problem require a feel for an audience that is often tacit and inexplicable.
AI can also bolster creative inquiry by investigating new ways of answering old questions. For example, AI can search literature and non-fiction for abstract ideas like “Why does love change over time?” or “How does knowledge elevate the soul?” While Google search is traditionally based on keywords, new search capabilities can link idea to idea, unlocking new connections and patterns of discovery.
In case learners don’t have access to the right person to ask a question, customized chatbots will increasingly allow users to interview and consult a virtual facsimile of their favorite artist or expert. These so-called “digital twins” are AI systems trained on one person’s data for an extended amount of time. As a result, a basic chatbot grows to mimic one particular person. This opens up the possibility to bounce questions like “How do you get unstuck when you’re in a creative rut?” to renowned creatives like music producer Rick Rubin.
To be clear, this is not exactly the same as talking to Rick Rubin. It’s important to distinguish between people and simulations of people. But it can be more than good enough as a tool to help us spark new thoughts.
How we elaborate
As ideas grow, they inevitably branch out and expand. Are there elements of this part of the creative process that are best suited for human input versus AI input?
We might start with a basic approach to using ChatGPT. A new user of ChatGPT could first think of it as an answer machine: I ask a question, ChatGPT gives me an answer. This might feel like ChatGPT as a research assistant or a boosted search engine.
However, beyond answering questions, generative AI tools can also serve as an experiment engine for elaboration. For example, a user could ask ChatGPT to build on an existing song:
To test out another path, a user might ask ChatGPT to rewrite it with a different tone:
For an alternative approach, you might ask ChatGPT to rewrite it in the style of a haiku:
What we’re doing here is not trying to get ChatGPT to give us the “right answer,” but rather to use ChatGPT to explore a divergent set of possibilities to expand our thinking. A designer might call this exploring sacrificial concepts that push our thinking.
Beyond chatbots, flexible and modular AI-boosted idea boards are also leveling up the iteration process. By clicking and dragging, the user can program AI models to generate images or widgets for idea experimentation. Concepts can be automatically compressed or expanded — and even recombined — with no coding at all. This flexible approach allows the learner to program a digital canvas to serve their particular needs and preferences, and does this in a way that feels like familiar whiteboarding tools like Google Jamboard or MURAL.
Additionally, while learners are good at producing ideas, elaborating on them over time can be challenging. AI tools are beginning to support this part of the creative process by setting up pathways to iterate concepts. For example, to organize ideas for iteration, some programs promise to operationalize your messy stack of notes or to extract and regroup ideas from across your computer. These apps largely integrate AI into existing workflows in order to reduce friction and rapidly find a fit with learning technology.
How we nudge
The right nudge can make or break creative work. Knowing where and when to nudge can be challenging, especially to an artist or learner deep within their creative journey. So, what role might creative learners be best suited for here? What about educators? What about AI?
Consider how we discover new connections. There may be a more human quality in finding unusual connections, especially ones that require intuition or cultural background. For example, could an AI have guessed that peanut butter and pickle sandwiches can be quite good? Or what about intuiting that an old Celtic song could find new life as a punk rock melody?
While AI is less capable at inuiting the right connection, it can nonetheless spur new directions. AI apps can now cook up wild combinations of ideas in user-friendly interfaces — and if you don’t like the concoction, there is no cost to try again until you find the right fit. Designers are packaging this creative partnership so that learners can outline a new direction alongside an AI, then evaluate the output on their own and decide if this propels their creative vision.
For more seasoned creatives, AI can also act like a visual design intern — just give it a word or phrase, and a color palette magically appears. For those students in digital media classes already focussing on industry tools, AI partners are now even integrated into Adobe products to nudge art in new directions.
That’s all for now! Stay tuned for next time as we take a look behind-the-scenes with education-minded folks considering how to develop their own AI tools.
This article was co-written by Josh Weiss, director of digital learning solutions at Stanford Graduate School of Education, and Glenn Fajardo, instructor at Stanford d.school.
