Generative AI-driven personalization of the community of inquiry model: enhancing individualized learning experiences in digital classrooms
Abstract
Purpose This paper explores the integration of generative artificial intelligence (GenAI) into the Community of Inquiry (CoI) framework, focusing on how GenAI can dynamically personalize online learning environments. The study aims to examine how GenAI can enhance social, cognitive and teaching presence, thus meeting the diverse needs of individual learners and improving engagement in digital classrooms. Design/methodology/approach The paper employs a conceptual approach, building on existing literature about the CoI framework and GenAI. It proposes a theoretical model that illustrates how GenAI can personalize social, cognitive and teaching presence in real-time, using engagement patterns, performance data and feedback mechanisms to adapt learning pathways for individual students. Findings The study finds that GenAI can significantly enhance personalized learning by dynamically adjusting the CoI framework’s elements. GenAI-driven interactions improve student engagement through personalized prompts and adaptive content delivery, while AI-generated feedback provides timely and individualized support, fostering a more responsive and student-centered learning experience. Practical implications For educators, the integration of GenAI into the CoI framework offers scalable solutions for personalized instruction and feedback. Institutions can leverage AI-driven insights to create more adaptive, learner-centered environments that improve learning outcomes, satisfaction and engagemen…
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GenAI-supported MOOCs learning environments have the ability to implement learning analytics, recommender systems, adaptive learning, intelligent assessment, and more, and can provide students with a richer learning experience.
“GenAI-supported MOOCs learning environments have the ability to implement learning analytics, recommender systems, adaptive learning, intelligent assessment, and more, can provide students with a richer learning experience (Anderson et al., 2025).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Anderson et al., 2025
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Lv, P., Wang, X., Nie, Y., Wu, R., & Wang, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1633686 · Cited by 5 · see citing works
Flipped design thinking with generative AI for digital literacy in ODL
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- Authors
- Anderson, J. E., Nguyen, C. A., & Moreira, G.
- Affiliations
- University of Tampa (US)
- California State University Los Angeles (US)
- New Mexico Highlands University (US)
- Venue
- The International Journal of Information and Learning Technology
- Publisher
- Emerald
- Published
- 2025-05-01
- Volume
- 42
- Issue
- 3
- Pages
- 296-310
- ISSN
- 2056-4880 (ISSN-L)
- 2056-4899
- OpenAlex
- W4409977772
- Cited by
- 33
- References
- 15
- Citations by year
- 2026: 22 · 2025: 11
- FWCI
- 34.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Information and Learning Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, community of inquiry, personalized learning, online learning, social presence, cognitive presence, teaching presence, student engagement, AI-generated feedback, adaptive learning pathways, digital classroom, data privacy, algorithmic bias, digital divide, educational equity
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