ARCHITECTURE OF AI-AUGMENTED VIRTUAL LEARNING COMMUNITIES BASED ON COMMUNITY MATURITY AND THE 4A KNOWLEDGE CO-CREATION FRAMEWORK

Ihor Katernyak, Kiril Fedotov, Viktoriya Loboda

Abstract


Background. Virtual learning communities (VLCs) are increasingly used for professional development, project-based learning, and knowledge co-creation. In such environments, the quality of peer assessment and the emotional climate of participation are important for refining project concepts and sustaining engagement. This paper examines the VLC Innovation Spring and presents the pedagogical rationale and software architecture of an AI-augmented facilitation layer designed to support peer assessment and formative feedback.

Materials and Methods. The study draws on data from three annual VLC Innovation Spring cycles with a cumulative sample of 126 participants. The pedagogical design is grounded in a VLC maturity model and the 4A eLearning approach. The platform combines H5P-based interactivity, peer review, and emotional-state monitoring informed by Csikszentmihalyi’s flow model. To address variability in the quality of peer reviews, an asynchronous AI facilitation architecture was designed as an external microservice integrated with the VLC platform.

Results and Discussion. The findings confirm the viability of peer learning in the VLC environment. Emotional-state monitoring conducted during one VLC cycle showed that project concept preparation was associated with flow (33.3%) and high arousal (79.4%), whereas peer assessment was associated with a particularly strong sense of control (79.7%). Combined with participants’ positive evaluations of the usefulness of the sequence “project concept preparation → peer assessment,” these results suggest that the VLC provided an engaging and supportive environment for co-creation. At the same time, substantial variability in the quality of peer reviews emerged as a key limitation.

Conclusion. The proposed asynchronous AI-assisted facilitation architecture extends the VLC with a scalable mechanism for refining project concepts and providing structured formative feedback, without overloading the core platform. The study contributes both pedagogically, by demonstrating the role of emotional-state monitoring in designing a productive co-creation environment, and technically, by proposing an event-driven architecture for AI-supported peer learning in VLCs.

Keywords: virtual learning community, knowledge co-creation, AI facilitation, peer assessment, 4A model, emotional state monitoring.


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References


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DOI: http://dx.doi.org/10.30970/eli.35.1

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