Artificial Intelligence in Classroom Teaching: Prospects, Challenges and Framework for Responsibly Orchestrated Mediation

This narrative review develops a conceptual-theoretical architecture for interpreting Artificial Intelligence in classroom teaching as orchestrated epistemic mediation rather than discretionary tool adoption. It reframes AI as a multi-layer socio-technical stack whose capability, system, infrastructure and practice layers jointly re-configure instructional design, interactional order, formative feedback ecologies and assessment legitimacy. The analysis specifies how probabilistic generation and recommendation logics perturb classroom epistemics by amplifying fluency, accelerating artifact production and re-shaping attention allocation, while simultaneously intensifying warrant collapse, automation bias, proxy drift, dependency formation and equity drift. Drawing on cross-disciplinary constructs spanning cognitive load theory, universal design for learning, self-regulated learning, dialogic participation, validity-as-argument, contextual integrity, and contestable accountability, the review maps the prospects as conditional opportunity structures that require verification pedagogy, scaffold-with-fading routines, dialogic integrity protections and validity-preserving assessment re-design. It also formulates a structurally grounded challenge typology that treats privacy, dignity and procedural fairness as first-order constraints under classroom power asymmetries and platform political economy. The synthesis yields an actionable framework of responsible classroom AI as high-reliability orchestration, operationalizing governance minima, documentation artifacts and role-based autonomy boundaries that preserve teacher professional authority and student agency. The paper is designed to support academics, policymakers, workforce development professionals and learning technologists in specifying mechanisms, boundary conditions and implementation commitments without relying on tool-specific claims.