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Responsive teaching, a pedagogical approach that foregrounds and builds instruction on student ideas, requires teachers to attend to and build on student resources. However, teachers’ interpretations of student resources, especially during live teaching, remain understudied. In this study, we examined in-the-moment interpretations, teachers’ real-time sense-making of and reflection on students’ epistemic and emotional resources, and explored how teachers’ in-themoment interpretations can support their responsive teaching talk moves and knowledge. Employing a convergent mixed-methods research design, we designed and implemented a generative artificial intelligence (AI)-supported virtual simulation as a pedagogical sandbox for 40 preservice teachers (PSTs) to practice teaching with virtual students, interpret student resources, and act on these interpretations in real time. Linear regression analysis was conducted and found that PSTs’ in-the-moment interpretations are significant predictors of their responsive teaching talk moves and knowledge. Qualitative thematic analysis identified themes that corroborated and extended the findings of the quantitative component. Implications for teacher education and simulation design are discussed. 

Engagement is a multifaceted construct that can influence learning outcomes. In this ex post facto study, we examined the correlations between different engagement constructs, as well as the impact of engagement on preservice teachers’ teaching knowledge and skills. We collected and analyzed video data from fourteen individual sessions with a coding scheme for engagement in VR-based teaching practices using AI-powered virtual students, as well as a teaching knowledge and skills posttest. The preliminary findings suggested that preservice teachers’ VR movement may correlate with disengagement, while emotional engagement, particularly confusion, can support learning opportunities. Engagement observed in the current study significantly predicted teaching knowledge and skills development. 

This mixed-methods study examined an AI-supported virtual simulation and explored the role of preservice teachers’ (PSTs) in-the-moment interpretations of virtual students’ resources (both epistemic and emotional) in their responsive teaching practices. Thirty-three preservice science and mathematics teachers participated in the study. Linear regression analysis results revealed that PSTs’ interpretative acts can significantly predict their responsive teaching practices. Qualitative analysis corroborated that purposive interpretation enabled PSTs to estimate students’ learning states and teaching scenarios as a whole. It was also found that PSTs struggled to enact interpretations, pointing to future research directions. 

This case study reports on the perceptions and dialogic behaviors of 15 preservice K-12 teachers engaging in simulation-based teaching practice with AI-powered student agents. Data included transcripts of text-based classroom dialogue, interviews, observations, and conversation logs. Using mixed-methods analyses and a framework of ambitious science teaching, we identified two key findings that are important to Human-AI interaction researchers and teacher trainers. First, AI-powered student agents exhibit naturalistic discourse behavior, with ambitious talk moves leading to more rigorous student contributions and conservative talk moves leading to low rigor contributions. And second, preservice teachers’ dialogue was responsive to the AI-powered students’ contributions. 

This poster reports on an exploratory comparison of middle school science classroom discourse from AI-powered virtual student agents and human students. Transcripts from both simulated science classes with preservice teachers and AI students and recordings of real science classes were coded using a framework of student science talk moves. Results suggest that the AI and human discourse is mostly similar, although the AI tended to ask questions much more frequently than human students did. 

New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of teacher training simulations, but little is known about how preservice teachers interact with AI-powered student agents. This study analyzed the discourse behavior of 15 preservice teachers who undertook simulation-based training with AI-powered student agents. Using a framework of ambitious science teaching, we conducted a pattern analysis of teacher and student talk moves, looking for evidence of academically productive discourse. Comparisons are made with patterns found in real classrooms with professionally trained science teachers. Results indicated that preservice teachers generated academically productive discourse with AI-powered students by using ambitious talk moves. The pattern analysis also revealed coachable moments where preservice teachers succumbed to cycles of unproductive discourse. This study highlights the utility of analyzing classroom discourse to understand human-AI communication in simulation-based teacher training.