Designing Human-GenAI Interaction for cMOOC Discussion Facilitation: Effects of a Collaborative AI-in-the-Loop Workflow on Social and Cognitive Presence

TL;DR

This study explores how a collaborative human-AI workflow can enhance discussion facilitation in cMOOCs, improving social and cognitive presence through specific interaction designs and moderation standards.

Contribution

It introduces a novel AI-in-the-loop workflow with discourse-adaptive roles and human review, demonstrating its positive effects on social and cognitive engagement in online learning.

Findings

AI participation increased social presence and network cohesion.

Direct learner-AI interaction correlated with higher cognitive indicators.

Effective discussion depends on reciprocal exchange and collaborative moderation.

Abstract

Connectivist MOOCs (cMOOCs) rely on learner-driven interaction, yet their intentionally light facilitation makes it difficult to design generative AI participation that is both scalable and educationally productive. This design-based research study examined how human-AI interaction can be designed for discussion facilitation through a collaborative AI-in-the-loop workflow. Across two iterations in a five-week cMOOC (N = 606), we designed, deployed, and evaluated a facilitation system that combined network-structure-driven target selection, discourse-adaptive response roles, and mandatory human review before AI participation became visible in the community. Iteration 1 (Weeks 1-2) focused on refining the interaction design, showing that the most sustainable facilitation patterns were Guide (70.4%) and Amplifier (28.5%) responses and yielding explicit moderation standards for publishable AI participation. Iteration 2 (Weeks 3-5) examined how different forms of AI-mediated interaction related to social and cognitive presence. AI participation selectively enhanced Open Communication (r = 0.188, p = 0.006), Networked Cohesion (r = 0.274, p < 0.001), and overall social presence (r = 0.162, p = 0.015), while cognitive presence showed no overall improvement. More importantly, direct learner-agent interaction was associated with significantly higher social presence (r = 0.186, p = 0.004) and higher-order cognitive indicators-Integration (r = 0.206, p = 0.001) and Resolution/Creation (r = 0.350, p < 0.001)-than mere co-presence in AI-involved threads. The findings suggest that effective GenAI-supported discussion depends less on AI presence alone than on interaction design: reciprocal exchange, discourse-adaptive facilitation roles, and collaborative human review appear to be key conditions for productive AI participation in online learning communities.