Generative AI and Creative Learning: Concerns, Opportunities, and Choices

Abstract

As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. That was true with personal computers, then with the internet, and now with artificial intelligence (AI) technologies. For each new technology, there are many different ways that we can integrate the technology into how we teach and learn. These choices are critically important: different choices can have very different outcomes and implications. How should we make these choices? I think it's important to carefully consider what type of learning and education we want for our children, our schools, and our society-and then design uses of new technologies that align with our educational values and visions. What does that mean for the integration of new AI technologies such as ChatGPT into our learning environments? In my view, the top educational priority in today's world is for young people to develop as creative, caring, collaborative human beings. With the pace of change accelerating in all parts of the world, today's children will face a stream of uncertain, unknown, and unpredictable challenges throughout their lives-and the proliferation of new AI technologies will further accelerate the changes and uncertainties. As a result, it's more important than ever for children from diverse backgrounds to have opportunities to develop their abilities to think creatively, engage empathetically, and work collaboratively, so that they can deal creatively, thoughtfully, and collectively with the challenges of a complex, fast-changing world. Unfortunately, I find that many of the current uses of AI in education are not aligned with these values-and, in fact, they could further entrench existing educational approaches at a time when significant changes are needed. Too often, today's AI technologies are used in ways that constrain learner agency, focus on "close-ended" problems, and undervalue human connection and community. But I also see intriguing opportunities for new generative AI tools, which can generate texts and images in response to natural-language questions and comments. I believe these new AI systems could be used in ways that support project-based, interest-driven creative learning experiences-expanding the ways that learners can imagine new ideas, build on their interests, design new projects, access diverse resources, and get feedback on their ideas. But this will happen only if people make explicit, intentional choices to use the new tools in this way. In this essay, I'll start by discussing my concerns about current uses of AI tools in education, then I'll explore how we might leverage new AI technologies to support creative learning experiences. Concerns In most critiques of AI systems, the focus is on problems that were not intended by the developers of the systems (e.g., biases or inaccuracies based on the sets of examples used to train the systems, and inadequate acknowledgement or compensation for the artists and writers whose work is Open in app Sign up Sign In Search Write used in the training) and on problems that arise when the systems are used differently than the developers had hoped (e.g., students turning in papers produced by AI systems as if the work were their own). These are important problems and need to be addressed. But in this essay, I have a different focus. I will be discussing why I'm concerned about many AI-in-education systems even when they work exactly how their developers intended and are used exactly how their developers had hoped. Concern #1: Constraining learner agency Back in the 1960s, as researchers were beginning to explore how computers might be used in education, there were two primary schools of thought. One focused on using computers to efficiently and effectively deliver instruction to the learner. The other focused on providing learners with opportunities to use technologies to create, experiment, and collaborate on personally meaningful projects. Seymour Papert referred to these two different approaches as instructionist and constructionist. Over the years, most AI researchers and developers have focused on the first approach, developing "intelligent tutoring systems" or "AI coaches" that provide instruction to students on particular topics, continually adapting the trajectory of the instruction based on student responses to questions. These systems have been promoted as a personalized approach to teaching, aiming to provide each student with customized feedback and instruction based on their current level of understanding, as opposed to a one-size-fits-all approach in which the same instruction is delivered to all students. With advances in AI technology, these tutoring systems have become more effective in delivering instruction that adapts to individual learners. For example, some AI tutors and AI coaches have demonstrated improved results when they deliver instruction through a virtual character that looks like the student's favorite teacher or favorite celebrity. I don't doubt these research results, but I worry that some of these "improvements" are perpetuating and reinforcing an educational approach that is in need of a major overhaul. To a large degree, AI tutors and coaches have been designed to be in control of the educational process: setting goals, delivering information, posing questions, assessing performance. That's also the way most classrooms have operated over the past couple centuries. But the realities of today's world require a different approach: providing students with opportunities to set their own goals, build on their own interests, express their own ideas, develop their own strategies, and feel a sense of control and ownership over their own learning. This type of learner agency is important in students' development, helping them develop the initiative, motivation, self-confidence, and creativity that will be needed to contribute