community-of-inquiry
Filtering by topic community-of-inquiry(3)Clear all filters
- PaperOpenAlex — TESOL research22 Jul 2026
AI in language education
Jake Cummings
The chapter examines AI in second language acquisition through the interpretive frameworks of Community of Inquiry (CoI) and Sociocultural Theory of Second Language Learning (SL2), finding that the educational value of AI depends more on teacher preparedness than technical innovation. Three themes emerge: expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that teacher digital competence is a mediating construct between technological capability and educational value.
Original abstract
AI tools are increasingly embedded in second language acquisition (SLA), yet their integration raises unresolved tensions between technological potential and pedagogical practice. While AI applications promise efficiency, personalization, and extended opportunities for interaction, these affordances are often overstated or disconnected from authentic learning, cultural exchange, and equity. At the same time, teacher preparation programs have been slow to adapt, leaving teachers underprepared to critically evaluate AI-mediated language learning environments. This chapter examines AI in SLA through the interpretive frameworks of the CoI and SL2, positioning teacher digital competence as a mediating construct between technological capability and educational value. Traditional SLA approaches provide historical context, while adaptive systems, chatbots, and GenAI are analyzed in terms of presence, interaction, authenticity, and cultural depth. Three themes emerge: Expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that the educational value of AI depends less on technical innovation than on the preparedness of teachers to use it critically, ethically, and inclusively to design and mediate AI-supported language learning experiences.
- PaperComputers & Education1 Jul 2026
A community of inquiry perspective on human–AI co-facilitation within micro-blended learning
Zilong Pan, Zilu Jiang, Shen Ba, Kui Xie
Applies the Community of Inquiry framework to explore human–AI co-facilitation in micro-blended learning contexts. Findings likely highlight how AI can augment instructor presence and support learner engagement within short, blended modules.
- PaperComputers & Education26 Jun 2026
Searching for predictors of presence, academic achievement and satisfaction in online adult education
Stefan Hrastinski, Stefan Stenbom, Helena Colliander, Andreas Fejes
Using data from 672 adult learners in Sweden, this study identified predictors of presence, academic achievement, and satisfaction in online upper secondary education. The Community of Inquiry framework was adapted, with peer interaction and teacher guidance loading onto social presence. Cognitive presence was the strongest predictor of achievement, while teaching presence best predicted satisfaction.
Original abstract
Little attention has been paid to adults pursuing online education at the upper secondary level. The aim of this article is to identify and describe predictors of presence, academic achievement, and satisfaction in online upper secondary education for adults. The study is grounded in the Community of Inquiry (CoI) framework, which is based on three types of presence: teaching presence (TP), social presence (SP), and cognitive presence (CP). A survey was administered to adults who had completed, or were close to completing, an online upper secondary-level course in Sweden, yielding 672 responses. An exploratory factor analysis showed that items from the CP and TP constructs related to peer interaction and teacher guidance loaded onto the SP construct instead. Accordingly, the factors were labeled as individual TP (iTP), extended SP (eSP), and individual CP (iCP) to describe how the CoI framework is manifested in this context, characterized by substantial individual study and varying opportunities for social interaction. Multiple regression models explained a substantial proportion of the variance in iTP (adjusted R 2 = .58), eSP (adjusted R 2 = .68), and satisfaction (adjusted R 2 = .53), a more moderate proportion in iCP (adjusted R 2 = .36) and only a small proportion for academic achievement (adjusted R 2 = .13). Academic achievement was primarily associated with iCP ( β = .38), whereas student satisfaction was primarily associated with iTP ( β = .58). Key predictors included course introductions related to studying and technology, tutoring, synchronous online teaching, and collaborative course design.