Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing

Yang et al. (2026) tackle a fundamental tension in AI-augmented classrooms: how to balance teacher orchestration with student agency during dynamic transitions between individual and collaborative work. Using participatory speed dating with teachers and students, the study maps a three-stage design space (before, during, and after pairing) and proposes a hybrid-control framework for analytic-based orchestration tools.

The core recommendation — structured teacher guidance initially, with progressively increasing student autonomy — provides an actionable design principle for Intelligent Tutoring systems that manage classroom-level coordination. This graduated autonomy model connects to Scaffolding theory and extends it from individual learning support to social-organizational support. The work also contributes to the Teacher Role literature by formalizing how human-AI co-orchestration can distribute agency without undermining either party's effectiveness.

By situating the research within real K-12 classroom dynamics and publishing at CSCW, the paper bridges the Collaborative Learning and Human In The Loop AI communities that have often addressed these questions separately. The three-stage framework (before/during/after) offers a structured lens for evaluating other orchestration tools, complementing the AI Tutor Behavioral Evaluation focus on individual tutor performance with classroom-level coordination metrics. For K 12 practitioners, the study validates concerns about AI systems that optimize for efficiency at the cost of student Self Regulated Learning and teacher professional judgment.

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Kexin Bella Yang, Menghan Liu, Liyi Xu, Nikol Rummel, Vincent Aleven (2026). Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing. arXiv:2605.18761. arXiv:2605.18761 [cs.HC] — Accepted at CSCW 2026, to appear in PACM HCI.