Human-AI Collaborative Teaching: Generative Artificial Intelligence (Gen-AI) as Co-Teacher

This article theorizes Human-AI Collaborative Teaching as a co-teacher paradigm grounded in joint cognitive systems and reliability-first sociotechnical design, where instructional quality emerges from coupling, constraint and accountable orchestration rather than model fluency. The synthesis reframes teaching as real-time regulation under bounded rationality, linking distributed cognition and situated cognition to role-bearing AI participation across planning, enactment, assessment and reflection. It specifies a governance-ready architecture in which teacher authority is preserved through decision-rights partitioning, mixed-initiative interaction protocols, calibrated uncertainty signalling, and an abstain-escalate safety regime. Epistemic robustness is operationalized through provenance discipline, evidence-anchored feedback and contestability pathways that protect epistemic dignity, participation equity and multilingual-accessibility rights under high-stakes accountability. The article integrates constructs from learning sciences, human-computer interaction, resilience engineering, implementation science and public governance to produce five compact design instruments, a theoretical lens map, role-ecology contracts, interaction protocol patterns, a governance risk register, and an institutional maturity model for scalable adoption. The resulting framework offers concrete, globally portable implementation logic for policy makers, workforce development leaders, and educational technologists seeking audit-ready co-teaching infrastructures that enhance teacher noticing, strengthen formative inference and sustain assessment validity without privacy erosion, surveillance creep or de-skilling.