Generative AI and the community of inquiry framework: A scoping review of teaching, social, and cognitive presence

Min Young Doo; Yeonjeong Park

doi:10.24059/olj.v30iSI2.5880

Authors

Min Young Doo Kangwon National University
Yeonjeong Park Jeonbuk National Univ.

DOI:

https://doi.org/10.24059/olj.v30iSI2.5880

Keywords:

Generative AI, Community of Inquiry, cognitive presence, social presence, teaching presence, learning presence, scoping review

Abstract

This scoping review synthesizes empirical studies examining how generative AI is associated with teaching, cognitive, and social presence within the Community of Inquiry (CoI) framework. Although generative AI has rapidly entered educational practice, its impact on teaching, cognitive, and social presence remains conceptually fragmented. To address this gap, we systematically searched for eligible literature following PRISMA guidelines and reviewed 21 peer-reviewed empirical studies published after the emergence of large language model-based AI systems (between November 2022 and July 2026) to identify research trends, AI roles in teaching and learning, and patterns of presence activation related to learning experiences. Findings indicate that first, generative AI has evolved beyond conversational agents to include pedagogical agents, AI-generated instructors, collaborative cognitive tools, and AI-supported learning environments, while teaching presence remains comparatively underexplored as an independent construct. Second, the distribution of presence dimensions is uneven: cognitive presence was most frequently associated with AI-supported exploration and integration processes, whereas resolution and independent examinations of teaching presence remained comparatively limited. Third, the reviewed studies suggested that presence-related outcomes varied by instructional approaches. Discussion and task-based activity were more often associated with reported indicators of cognitive presence, whereas pedagogical agents and socially expressive AI designs more frequently supported perceived social presence. The findings suggest that generative AI should not be regarded as a universal solution for strengthening the CoI framework. Rather, its educational value depends on how it is pedagogically configured within specific instructional contexts. The review contributes to a more nuanced understanding of AI-mediated learning by positioning generative AI as a design-sensitive pedagogical mediator and identifying directions for future theoretical and empirical research.

Author Biography

Min Young Doo, Kangwon National University

This systematic literature review synthesizes empirical studies examining how generative AI influences learning presence within the Community of Inquiry (CoI) framework. Although generative AI has rapidly entered educational practice, its impact on teaching, cognitive, and social presence remains conceptually fragmented. To address this gap, we reviewed 14 peer-reviewed empirical studies published after the emergence of large language model–based AI systems to identify research trends, AI roles in teaching and learning, and patterns of presence activation related to learning presence. Findings indicate that first, generative AI has predominantly been positioned as a conversational agent or instructional mediator, with limited attention to its role in supporting teaching presence as an orchestrating function. Second, the distribution of learning presence dimensions is uneven: cognitive presence—particularly exploration and integration—was most frequently enhanced, whereas resolution phases and independent examinations of teaching presence were comparatively underrepresented. Third, the activation of presence was not uniform but contingent upon instructional approaches. Discussion- and task-based pedagogies tended to amplify cognitive presence, while anthropomorphic and socially expressive interactional cues strengthened social presence through perceived connectivity. These findings suggest that generative AI does not automatically compensate for structural limitations of the CoI framework. Rather, its influence depends on pedagogical configuration and instructional design. The review contributes to a more nuanced understanding of AI-mediated learning presence and highlights directions for future theoretical and empirical research.

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