2026/01/02 by Berkan Celik, BERKAN ÇELİK, Ferhan Şahin
Computer Science · #Cognition #E-Learning and Knowledge Management #Educational technology #Intelligent Tutoring Systems and Adaptive Learning #Open Education and E-Learning #Open educational resources #Peer learning #Social cognitive theory #Social influence #Structural equation modeling #Trustworthiness
paper · doi:10.1080/01587919.2025.2609578
crossref issued 2026/01/02 · crossref published 2026/01/02 · crossref published-print 2026/01/02 · openalex publication_date 2026/01/02 · crossref published-online 2026/01/11 · crossref created 2026/01/11 · openalex created_date 2026/01/13 · crossref deposited 2026/03/25 · crossref indexed 2026/08/03 · openalex updated_date 2026/08/03
The integration of artificial intelligence (AI) into open educational resource (OER) development presents new opportunities for enhancing educational experiences. However, the factors shaping pre-service teachers’ adoption of AI-assisted OER development still unexplored. This study examines the key determinants of pre-service teachers’ (psts) behavioral intentions to develop and use AI-assisted OER, drawing from Uses and Gratifications Theory and related constructs. Data were collected from 240 psts. Participants received 10 weeks training on AI-supported OER development. Data analysis was conducted performing structural equation modeling and bootstrapping. The findings reveal that compatibility, enjoyment, task efficiency, and information seeking significantly influence both trust and behavioral intention. However, trust did not. Social factors yielded mixed results. While social influence positively affected behavioral intention, it did not significantly impact compatibility or trust. Similarly, social interaction fostered trust but did not directly influence behavioral intention. While peer and instructor opinions encourage adoption, they do not necessarily enhance AI’s trustworthiness or suitability for educational tasks. The study contributes to the theoretical understanding of AI acceptance in education by refining existing adoption models with social and cognitive factors. Findings suggest that training programs should emphasize AI’s alignment with teaching tasks and focus on enhancing user enjoyment to drive adoption.