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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. 

Geographical remoteness, limited resources, and socioeconomic disparities contribute to the migration of rural Alaska Native Elders to urban areas to access medical services and resources, improve economic situations, or reunite with family. We compared the discourse of 12 Elders in Anchorage who previously lived in four remote traditional villages in the Norton Sound region (ages 60–84) and 13 Elders in those villages (ages 48–80). Using Gee’s discourse analysis framework, two patterns emerged detailing cultural effects on identity and Eldership, illuminating differences in the self-evaluation of successful aging based on cultural influences and the role of contextual factors. 

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. 

Within the science education reform movement, there have been long standing calls initiated to attend to equity in the science classroom. These calls are sought to de-settle and advance the broad strokes of “equity for all” into deeper, more meaningful actions, considering the way we view equity and how equitable practices unfold in the classroom. Productive science discourse or productive science talk is just one instructional practice used and discussed which leverages students as sensemakers. This study seeks to better understand productive science talk as a practice of equitation instruction. In examining Ms. Savannah’s practice, a high school biology teacher, two major findings emerged around the use of productive talk: (1) pattern of moves to leverage student ideas and (2) timing of moves to stimulate interest or motivation. These talk moves and timing gave insight into talk as both having the ability to hinder and foster student ideas and provide an initial “on-ramp” for students’ voice to be heard, taken up and have accountability in the classroom. This work continues to sustain a call toward attention to equity and a need to evaluate the equity-aligned practices that are fore-fronted in PDs and workshops. 

Abstract Preparing preservice teachers (PSTs) to be able to notice, interpret, respond to and orchestrate student ideas—the core practices of responsive teaching—is a key goal for contemporary science and mathematics teacher education. This mixed‐methods study, employing a virtual reality (VR)‐supported simulation integrated with artificial intelligence (AI)‐powered virtual students, explored the frequent patterns of PSTs' talk moves as they attempted to orchestrate a responsive discussion, as well as the affordances and challenges of leveraging AI‐supported virtual simulation to enhance PSTs' responsive teaching skills. Sequential analysis of the talk moves of both PSTs (n = 24) and virtual students indicated that although PSTs did employ responsive talk moves, they encountered difficulties in transitioning from the authoritative, teacher‐centred teaching approach to a responsive way of teaching. The qualitative analysis with triangulated dialogue transcripts, observational field notes and semi‐structured interviews revealed participants' engagement in (1) orchestrating discussion by leveraging the design features of AI‐supported simulation, (2) iterative rehearsals through naturalistic and contextualized interactions and (3) exploring realism and boundaries in AI‐powered virtual students. The study findings provide insights into the potential of leveraging AI‐supported virtual simulation to improve PSTs' responsive teaching skills. The study also underscores the need for PSTs to engage in well‐designed pedagogical practices with adaptive and in situ support. Practitioner notesWhat is already known about this topicDeveloping the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach.Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills.Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching.What this paper addsAI‐supported simulation has the potential to support PSTs' responsive teaching skills.While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources.AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time.Implications for practice and/or policyPSTs should build a teaching repertoire with both basic and advanced responsive talk moves.The learning module should adapt to PSTs' prior experience and provide PSTs with in situ learning support to navigate challenging teaching scenarios.Integrating interaction features and AI‐based virtual students into the simulation can facilitate PSTs' active participation. 

Science learning is thought to be best supported when students are positioned as epistemic agents. Using a case study approach, we explore the experiences of one Black middle school girl and her epistemic efforts and the ways in which her group members’ responses to her efforts either supported or constrained her epistemic agency during small group work in two argumentation lessons. Our findings show that Jessie’s epistemic efforts were not often taken up by her peers in ways that support her epistemic agency, findings that have implications for student learning and engagement in terms of the epistemic work we ask students to engage in, and the instructional strategies that support this work.