ArticleFrontiers in psychology2026
Psychological responses and cognitive mechanisms of university teachers in using generative AI in teaching: a configurational path analysis based on the MOA framework.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The rapid integration of generative artificial intelligence (GenAI) into educational contexts has prompted significant attention to teachers' psychological responses and cognitive mechanisms as users of AI in teaching. While existing studies often focus on linear models that examine the net effects of single factors on technology acceptance, there is a lack of research exploring the complex psychological mechanisms behind teachers' GenAI use behaviors from a multi-factorial perspective. This study, based on the Motivation-Opportunity-Ability (MOA) framework, investigates the multi-path psychological drivers of university teachers' acceptance of GenAI in teaching. Methods: A survey was conducted with 258 teachers from Longyan University who have experience using GenAI in teaching. Using fuzzy-set qualitative comparative analysis (fsQCA), the study examined how motivational factors (hedonic motivation, performance expectancy), opportunity factors (social influence, facilitating conditions, and interactivity), and ability factors (AI literacy, technical self-efficacy) interact through different combinations to trigger high levels of GenAI acceptance in teaching. Results: The findings reveal that no single psychological or situational factor within the MOA framework independently explains high levels of GenAI adoption. Teachers' use of GenAI results from the synergistic interaction of multiple psychological responses and cognitive conditions. Six effective configurational paths were identified, categorized into three psychological models: performance expectancy-driven under technical self-efficacy, hedonic motivation-driven under ability support, and hedonic motivation-driven in high-interaction contexts. Conclusion: This study uncovers the multi-path psychological mechanisms behind university teachers' adoption of GenAI in teaching, expanding the explanatory power of the MOA framework in educational psychology and human-AI interaction research. The results offer empirical evidence for understanding teachers' psychological responses and decision-making processes in AI-supported teaching and provide theoretical insights for promoting healthy and effective AI usage behaviors among teachers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.