Partnering with AI to create joy-centered personalized learning in a post-pandemic world.

STEM instructors are teaching in classrooms shaped by wide variation in students' prior preparation, learning experiences, and well-being, much of which has been intensified by the COVID-19 pandemic. At the same time, artificial intelligence (AI) tools are becoming increasingly available in higher education, prompting questions about how to use them productively and responsibly to support student learning. Personalized learning has been proposed as one approach for addressing these challenges, yet it remains difficult to implement at scale without increasing instructor workload or burnout. This Tips & Tools article presents a practical approach for partnering with AI to support joy-centered, personalized learning in STEM classrooms. Drawing on principles from joy-centered pedagogy, the procedure demonstrates how instructors can intentionally frame large language models (LLMs) to support student learning by fostering mattering, resilience, and agency through structured prompts. The approach formalizes instructional strategies implemented across lower- and upper-division undergraduate biology courses to support self-directed learning and growth-oriented revision, illustrating how AI can extend personalized learning opportunities at scale. The article introduces three prompt-based joy-centered pedagogy themes: normalizing uncertainty, promoting growth over perfection, and flattening classroom hierarchy. Each theme is accompanied by student-ready prompts and AI literacy guidance that instructors can readily integrate into courses. By positioning LLMs as pedagogical partners, this approach offers an accessible strategy for supporting diverse learners while preserving instructor bandwidth in an increasingly complex learning world.