Name: Designing AI to Support Learning, Not Bypass It: A CLT-Grounded MOOC Case from NTHU
Start: 2026-10-08T15:00:00-0400
End: 2026-10-08T15:30:00-0400

All sessions are available online except round tables, special activities, and workshops.

Designing AI to Support Learning, Not Bypass It: A CLT-Grounded MOOC Case from NTHU

Thursday October 8, 2026 3:00pm - 3:30pm EDT

6 DR4

ID: 33853

The rapid integration of generative AI (GenAI) into higher education has produced a complex and often contradictory evidence base. A three-level meta-analysis found that GenAI significantly promotes higher-order thinking (g = 0.851) but shows no significant effect on creativity or reflective capacity (Wang & Fan, 2025). A broader synthesis of 68 experimental studies found a moderate positive effect on learning outcomes (SMD = 0.45), yet with extremely high heterogeneity (I² = 95%), indicating that AI's impact varies enormously depending on how, when, and for whom it is deployed (Han et al., 2025). These findings compel open education practitioners to move beyond the question of whetherto adopt AI, toward the more consequential question of how to design AI tools that reliably produce learning gains rather than cognitive by-pass.This session reflects on the experience of National Tsing Hua University (NTHU), Taiwan, in integrating five GenAI-powered features into its open MOOC platform through the principled application of Cognitive Load Theory (CLT). We contend that CLT, with its precise account of working memory constraints, element interactivity, and the distinction between intrinsic and extraneous cognitive load, offers the most rigorous and actionable theoretical foundation available for AI tool design in large-scale open education. This contention is itself a reflective one: having experimented with broader, multi-theoretical frameworks, we found that CLT's specificity is precisely what makes it generative for design practice.Two empirically grounded risks frame this design challenge and inform our reflections throughout. The first is cognitive offloading (Skulmowski, 2023): when learners over-delegate memory and reasoning to external tools, they tend to retain only gist-level representations rather than the richly organized long-term memory schemas that support transfer and genuine expertise development. The second is the AI placebo effect (Skulmowski, 2024): learners systematically overestimate their own contributions when using AI, producing an illusion of competence that circumvents the productive cognitive struggle necessary for schema formation. Taken together, these risks reveal that AI tools designed without explicit attention to cognitive architecture may perform well on surface-level engagement metrics while undermining the deeper learning they are meant to support.Against this backdrop, the session presents five AI features developed on the platform, each designed to address specific CLT mechanisms. The AI Panda chatbot applies Load Reduction Instruction (Martin & Collie, 2025) through Socratic dialogic scaffolding. AI Integrative Questions maintain productive intrinsic load for schema construction at the close of each course chapter. AI Mind Maps address the split-attention and transient information effects characteristic of video-based MOOC delivery. AI Notes operationalize the worked example effect by reducing the extraneous burden of concurrent note-taking. AI Practice and Open-Ended Questions dynamically calibrate task demands in response to the expertise reversal effect, while leveraging retrieval practice to consolidate long-term retention.We close by reflecting critically on the institutional, administrative, and instructional design tensions encountered during implementation, and by sharing early outcomes from the deployment of these features in NTHU's Pre-AP program. We invite attendees to interrogate whether CLT offers a transferable design language for open education institutions navigating the pressures of AI adoption without sacrificing pedagogical integrity.

Speakers

Tonny Menglun Kuo

Division Director, Division of Learning Support and Research Planning, Center for Teaching and Learning Development, National Tsing Hua University, Taiwan

Tonny Menglun Kuo, Ph.D. is Assistant Professor in the Interdisciplinary Program of Management and Technology (IPMT) at the College of Management and Technology, National Tsing Hua University (NTHU), Taiwan. He concurrently serves as Division Director of Learning Support and Research... Read More →

Thursday October 8, 2026 3:00pm - 3:30pm EDT
6 DR4 MIT Samberg Conference Center, 50 Memorial Drive, Cambridge MA 02139 USA

Presentation

Speaker Modality In-person
Track Innovating Open Content to Democratize Knowledge

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OEGlobal 2026

Tonny Menglun Kuo

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Tonny Menglun Kuo

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