Enhancing Peer Exposure and Creating Opportunities for Bridging Social Capital Through an AI-Generated Summary-Driven Learning Design in Online Discussion Forums

Synthesis: This design-based research study by Hao and Cukurova evaluates an AI-generated summary-driven learning design (AI-SLD) in online collaborative discussion forums with 128 university students across three design iterations (baseline, Wizard-of-Oz, and full AI). Using social network analysis of viewing logs and thematic analysis of interviews, it shows that AI-generated discussion summaries significantly broadened students' exposure to peer contributions and strengthened network connectedness, functioning as navigational scaffolds that lowered the effort of locating meaningful posts and expanded opportunities for building the weak-tie connections social-capital theory terms bridging social capital. However, the support did not prevent a decline in viewing activity under rising academic workload, and the effect was moderated by discussion topic and topic familiarity.

Summary

Prior research on online discussion engagement has largely targeted posting and replying while overlooking the viewing activities that precede and enable them, and has focused on human-written rather than AI-generated summaries. This paper proposes and tests an AI-generated summary-driven learning design (AI-SLD) that uses Large Language Models (LLMs)-produced discussion summaries and example posts to lower the search cost of navigating large, poorly-titled forums and thereby broaden students' viewing breadth and access to heterogeneous peer perspectives (an opportunity condition for bridging social capital). Conducted via design-based research across three iterations with 128 higher-education students over an eight-week intervention, the study combines quantitative social network analysis of forum log data with qualitative thematic analysis of student interviews.

Key Findings

• AI-SLD broadened viewing breadth. Students in the AI-supported iterations (PSG, MSG) showed significantly higher standardized out-degree centrality than the baseline group (β = 0.547 and β = 0.438, both p < 0.001), indicating they viewed/interacted with more peers; the Wizard-of-Oz and full-AI versions performed comparably, showing AI-generated summaries can serve as a scalable alternative to human-written ones.
• Summaries acted as navigational scaffolds, not mere content. Interview data described the AI summaries as a "navigation map": rather than increasing the sheer number of posts viewed, they reduced the cognitive and temporal effort of finding relevant or interesting contributions buried in ill-named threads — a finding consistent with framing them as scaffolds rather than content substitutes.
• Viewing served selective attention and epistemic diversity. Students deliberately attended to posts from visible "active contributors," drawn not by friendship but by contrasting opinions and different backgrounds, suggesting viewing breadth may carry epistemic value relevant to forming the bridging (weak-tie) form of social capital.
• Support was moderated by topic familiarity and academic workload. When topics felt unfamiliar, students consulted more peers; familiar topics reduced engagement breadth. The AI-SLD did not prevent declining viewing activity in later weeks — a trend students attributed to draft-assignment deadlines and shifting priorities rather than dissatisfaction with the design.
• Network analysis distinguished peer-to-peer from AI-mediated viewing. Only the full-AI iteration (MSG) showed significantly higher raw-network density (p = 0.045) than baseline once AI-generated nodes were included, indicating the summaries and example posts contributed to the overall viewing network beyond direct peer interactions.

Implications for AI in Education

The study shifts attention from posting to the viewing/attentional behaviors that scaffold peer learning, arguing these are observable evidence of opportunities to access diverse perspectives (the conditions for developing bridging social capital). For designers of AI support in online learning, it suggests AI-generated summaries can be a low-cost, scalable alternative to instructor- or student-authored summaries that preserves engagement benefits. Yet it cautions that technological affordances alone do not solve engagement decline driven by broader academic pressures: AI-SLD should be paired with socio-pedagogical strategies to sustain participation across a semester. The authors also note that log-based network analysis cannot measure the depth or quality of viewing, urging future work that connects viewing behavior to discussion quality and cognitive engagement.

Connected Concepts

• Generative AI
• Online Teaching and Learning
• Collaborative Learning
• Scaffolding
• Student Engagement
• Learning Analytics
• Community of Inquiry
• Peer Assessment

Connected Articles

• Reconceptualizing Community of Inquiry in the Age of Generative Artificial Intelligence — Reconceptualizing Community of Inquiry in the Age of Generative AI
• Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning — Leveraging generative AI to Facilitate Peer Feedback in Collaborative Argumentation
• Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction — AI in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction
• A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade — A Systematic Review of AI-Powered Collaborative Learning in Higher Education

Citation

Hao, X., & Cukurova, M. (2026). Enhancing peer exposure and creating opportunities for bridging social capital through an AI-generated summary-driven learning design in online discussion forums. Journal of Computer Assisted Learning, 42(5), e70317.