Artificial Intelligence Acceptance in Educational Contexts
Summary
Artificial intelligence (AI) is rapidly reshaping educational landscapes by offering personalised learning pathways, intelligent tutoring systems and data-driven insights into student performance. Acceptance of AI in educational contexts hinges on educators’ and learners’ perceptions of usefulness, ease of use and associated risks. Educational stakeholders evaluate AI tools not only for their capacity to enhance pedagogical efficacy but also for their transparency, fairness and ethical alignment. Behavioural intention to adopt AI applications is influenced by technological factors such as system reliability and interface design, as well as by social factors including peer support and institutional encouragement. Variations in acceptance patterns appear across demographic groups, subject disciplines and levels of digital literacy. The growing body of research has extended classical models of technology acceptance—initially developed for business environments—to address the unique challenges of classroom integration, such as teacher preparedness, curricular fit and students’ cognitive engagement. Global case studies demonstrate that where AI tools are co-designed with end users, supported by professional development and integrated into broader pedagogical strategies, acceptance rates are substantially higher. This convergence of empirical findings underlines the importance of contextual factors, hybridised theoretical frameworks and iterative design in fostering sustainable uptake of AI in education.
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Artificial Intelligence Acceptance in Educational Contexts publication trend
The graph below shows the total number of articles in artificial intelligence acceptance in educational contexts across all publications each year (not limited to Nature Index journals).
Technical terms
Artificial Intelligence (AI): Computer systems capable of performing tasks that normally require human intelligence, such as learning, reasoning and problem-solving.
Technology Acceptance Model (TAM): A theoretical framework that explains users’ technology adoption decisions based on perceived usefulness and perceived ease of use.
Unified Theory of Acceptance and Use of Technology (UTAUT): An extension of TAM that incorporates additional determinants such as social influence and facilitating conditions to predict behavioural intention and usage behaviour.
Perceived Usefulness: The degree to which a person believes that using a particular technology will enhance their performance or outcomes.
Perceived Ease of Use: The degree to which a person believes that using a particular technology will be free of effort.
Behavioural Intention: A user’s expressed likelihood or plan to employ a given technology in the future.
References
- Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education (2023).
- Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies (2022).
- Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model. Sustainability (2023).
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