In its purest form, the goal of teaching and learning is to cultivate a self-motivated intellectual curiosity. It is not uncommon for educators to take their own intellectual curiosity for granted and even ascribe that same level of enthusiasm for learning to their students. Over the last decade or two there have been an increasing number of social, political, structural, and technical changes in the world that nudge people (faculty and students included) to prioritize frictionless experiences that feel good and avoid challenges to their identity. As a result, the intrinsic value of learning, especially difficult or time-consuming learning, may not be as deeply ingrained in or rewarding to as many students (and faculty) as in years past. In this workshop, we will explore approaches to education and its structures that center principles to advance a deeper commitment to intellectual curiosity. These more mindful approaches can also be applied to the administration of teaching and learning, as well as research activities. As computing professionals, we can use the Principles of the ACM Code of Ethics as a starting point. By centering the public good as the paramount concern, these principles are reflective of values held by many in the computing community and support a culture of creative, curious, and reflective practice.

As a more concrete example of our motivation, consider the impact of the public release of generative AI tools. While instructors have long had to adapt to changing technologies, these tools represent a fundamental shift. Prior evolutions, from the introduction of calculators to Wikipedia, gave learners powerful tools while still depending on the primacy of the user’s choices and engagement. In contrast, generative AI tools make it considerably easier for users to disengage, as the tools are designed to surmise the user’s goals with less explicit statements of intent. That is, generative AI makes it possible to lull students into a passive learning mode that avoids active engagement with difficult or frustrating experiences, while permitting the impression that whatever has occurred counts as “learning” because the assignment has been completed.

While generative AI tools provide a significant aspect of our motivation, this session is not focused solely on the question of how to teach with (or against) these tools. Our goal is to give space for discussing and reflecting on how we can choose to embody and model more intentional educational practices.