Why teachers do or don't rely on artificial intelligence in education: Impact, trust, and adoption factors
Abstract
The enhancement of Artificial Intelligence (AI) technologies in educational institutions has both presented opportunities and challenges to the teaching professionals on a scale never been witnessed before. In spite of the rapid rate of development of AI-driven educational technologies, their acceptance among teachers is not evenly spread, and there are complicated factors behind it that define their decision-making patterns. This paper fills in this critical gap on the necessity to comprehend why teachers accept or reject the use of AI by considering the interaction of trust machinery with the perceived impact and determinants of adoption in the teaching environment. We also conducted a survey of high education teachers working in various institutional contexts, with the help of complex mixed-methodology approaches to study the connections between the variables in the paradigm of technology acceptance, organizational support systems, and AI adoption behaviors. The analysis helps to discover that teacher confidence in AI-based educational technology acts as the main mediator between perceived usefulness and real adoption, and psychological safety and institutional support are high levels of moderation. Findings have indicated that 5 of the six core constructs, meaning perceived usefulness, ease of use, trust in AI systems, institutional infrastructure, and professional self-efficacy, explain 68.3 percent of adoption variance. More so, the research has discovered some of the key obstacles such as complex technology, ethical issues covered with data privacy and the fear of being displaced professionally. These results add to the theoretical improvement of the Technology Acceptance Model used in the educational settings as well as offering practical implications to the policymakers and developers of educational technologies aimed at achieving successful incorporation of AI meaningfully into the teaching processes.
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Authors
Copyright (c) 2026 Swapnil Malipatil, Jayesh Rane, Shrishail Chiniwalar, Swati Dhurve, Nitin Liladhar Rane (Author)
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Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) 
This work is licensed under a Creative Commons Attribution 4.0 International License.