From Prompting to Scaffolding: Divergent Human–AI Interaction Pathways of Pre-Service Teachers in Multi-Agent Micro-Teaching Simulation (MAMS)

Traditional micro-teaching offers limited opportunities for pre-service teachers to rehearse classroom contingencies in repeatable, standardized ways. This study developed a multi-agent micro-teaching simulation (MAMS), informed by professional noticing, to expand practice-based learning opportunities for pre-service mathematics teachers (PSTs). MAMS integrates differentiated virtual students, standardized challenge events, and Dean Agent prompts to create comparable classroom contingencies. Sixty-four PSTs completed two simulated micro-teaching sessions and were analytically classified into higher and lower baseline mathematics pedagogical content knowledge (MPCK) profiles. Outcomes included teacher self-efficacy, MPCK performance, and Community of Inquiry presence; interaction logs were analyzed using event-anchored lag sequential analysis. Under the standardized MAMS condition, outcome analyses showed observed pre–post changes and modest baseline-adjusted profile differences. The higher baseline MPCK profile showed stronger MPCK response-strategy performance and higher Teaching Presence; Social and Cognitive Presence showed no reliable profile differences. Log analyses revealed divergent feedback-to-action pathways: higher baseline MPCK PSTs more often moved from Dean Agent prompts to diagnostic questioning and scaffolding, followed by substantive student responses, whereas lower baseline MPCK PSTs more often shifted to direct instruction or process regulation, followed by continued low participation. These findings suggest a need for prior-knowledge-responsive scaffolding in AI-supported teacher education.