Abstract

Abstract Asynchronous online discussions (AODs) are central to graduate online education, yet online students' social presence perceptions decrease over time, and learners with weaker peer-interaction experience the sharpest declines (Castellanos-Reyes, Richardson, & Maeda, 2024; Dong et al., 2025). Originally proposed as one of three co-equal presences in the Community of Inquiry framework (Garrison et al., 2000), social presence has since been repositioned as a pervasive construct that embeds itself in teaching and cognitive presence and anchors the online learning experience (Armellini & De Stefani, 2016). Existing AI interventions in AODs cluster into three patterns, each with a structural limit: instructor-facing dashboards do not scale, generic reminders create surveillance rather than autonomy, and virtual peer systems weaken the human connection they are meant to enrich (Fan et al., 2025). What is absent is a learner-facing AI design that uses AI to facilitate, rather than substitute for, peer-to-peer engagement. This TREO Talk presents the ECHO Framework, a design science research (DSR) artifact that treats Self-Determination Theory’s relatedness need (Deci & Ryan, 2000) and the Community of Inquiry’s social presence as convergent constructs. ECHO specifies a three-layer Observe–Curate–Humanize workflow with a generative-AI backend: Observe identifies weak ties, Curate maps peer-connection opportunities, and Humanize delivers an SDT-driven weekly digest with an orienting summary, peer-routing nudges, and an autonomy footer framing each suggestion as optional. A dual-constraint proposition governs the artifact: AI builds the bridge but never crosses it—routing learners toward peers, not substituting for them. What ECHO preserves is what AI cannot manufacture: peer engagement, connection, humanity, and learner ownership. Evaluation will follow a three-arm field experiment in a graduate online course in Fall 2026 (N ≈ 48–60): full ECHO, a generic-message active control isolating personalization from attention effects, and a business-as-usual control. Five-wave measurement (W1, W4, W7, W10, W15) draws on validated CoI, self-determination, and technology-acceptance instruments, triangulated with semi-structured interviews. ECHO offers three contributions. Theoretically, ECHO advances an SDT-driven design principle for learner-facing AI in AODs, integrating CoI’s social presence with SDT’s three needs as a unified target transferable beyond AODs. Methodologically, the three-arm active-control design isolates personalization and peer-routing from generic attention artifacts—a template most GenAI-in-education studies omit. Practically, ECHO offers IS researchers and educators a reusable design pattern for AI-mediated AOD interventions deployable across learning environments without added instructor workload.

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