A Team-Based Learning Activity with AI-Supported Reasoning for Teaching Molecular Dynamics

Abstract Molecular dynamics (MD) simulations are increasingly included in chemistry and life science curricula, yet students often struggle to interpret outputs and to integrate multiple descriptors into coherent explanations. This paper presents a Team-Based Learning (TBL) activity for a graduate-level physical chemistry course for biotechnology students designed to support this process. The activity emphasizes comparison and integration of protein and ligand root-mean-square deviation (RMSD) time series, regional RMSF values, key hydrogen-bond persistence and pocket occupancy. It also requires collective justification under interpretative ambiguity and uses a deliberately constrained AI-based tutor as a reflective scaffold to prompt explanation without providing authoritative answers. Team discussions during the activity typically involved comparing these indicators and developing collective explanations for alternative molecular scenarios. Student feedback was generally favorable toward the collaborative TBL structure, whereas perceptions of the AI tutor were mixed, indicating that its perceived usefulness varied among students.