AI-Supported Classroom Dialogue and Collaborative Discourse: Psychological Pathways to Higher-Order Thinking

Decades of research grounded in the Vygotskian tradition of social constructivism show that the quality of classroom dialogue and collaborative discourse shapes not only what learners know, but how they think. Frameworks such as Mercer's exploratory talk, Michaels and O'Connor's accountable talk, and Alexander's dialogic teaching converge on a central insight: high-quality discourse fosters higher-order thinking, including critical analysis, argumentation, creative problem-solving, and metacognitive monitoring.This tradition has long been constrained by the labor-intensive nature of discourse analysis, which limits most studies to small samples. Advances in natural language processing and large language models now allow AI to serve two roles in this space: as an analytical lens that identifies talk moves and discourse patterns at scale, and as a participant in interaction itself, functioning as a dialogue partner, tutor, or argumentation scaffold. Yet these two roles are rarely studied together, and the psychological mechanisms linking AI-supported discourse to higher-order thinking remain underexplored.This Research Topic brings together researchers from educational psychology, learning sciences, AIED, CSCL, and computational linguistics to examine the psychological pathways through which AI-supported classroom interaction and discourse contribute to higher-order thinking.We welcome empirical, theoretical, and methodological contributions on:- AI-powered analysis of classroom dialogue and collaborative discourse, and its relationship to cognitive development- Teachers' trust, cognitive load, and self-efficacy when using AI-generated discourse feedback- AI as a participant in dialogue and collaborative learning, and how it compares with human-human interaction- Psychological and motivational factors (trust, social presence, perceived agency) moderating AI-mediated discourse outcomes- Methodological advances for studying discourse-cognition relationships at scaleThis Research Topic requires classroom interaction, dialogue, or collaborative discourse as the unit of analysis, and a clear psychological process or outcome (e.g., cognition, metacognition, epistemic beliefs, self-efficacy, trust, motivation) as the explanatory focus. Manuscripts that address AI adoption, learning outcomes, or model performance without examining interaction or discourse processes fall outside the scope of this collection, as do studies limited to technical AI model development or system engineering without a psychological or educational lens. Topic editors: Deliang Wang, Cunling Bian, Jiahong Zhang Source: https://www.frontiersin.org/research-topics/85371/ai-supported-classroom-dialogue-and-collaborative-discourse-psychological-pathways-to-higher-order-thinking