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Initial Validation of the IMPACT Model: Technological Appropriation of ChatGPT by University Faculty

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Vydáno v:Education Sciences vol. 15, no. 11 (2025), p. 1520-1556
Hlavní autor: Pereira-González, Luz-M
Další autoři: Basantes-Andrade Andrea, Naranjo-Toro, Miguel, Guia-Pereira Mailevy
Vydáno:
MDPI AG
Témata:
Teaching
Generative artificial intelligence
Chatbots
Technology adoption
Higher education
University faculty
Retention (Psychology)
Professional Autonomy
Critical Thinking
Educational Practices
Factor Analysis
Intelligent Tutoring Systems
Influence of Technology
College Faculty
Academic Achievement
Educational Technology
Instructional Materials
Predictor Variables
Factor Structure
Ethics
College Students
Short Term Memory
Peer Influence
Intention
Privacy
Statistical Studies
Instructional Design
Artificial Intelligence
Social Influences
Learner Engagement
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Abstrakt:This study presents the initial validation of the IMPACT model, a psychometric tool developed to evaluate how university faculty adopt ChatGPT in higher education. It specifically addresses the existing gap in validated instruments designed for educators, as most prior research has focused on student-based adoption models. A total of 206 professors completed a 39-item Likert-scale questionnaire. Exploratory factor analysis using principal axis factoring with oblimin rotation identified the underlying structure of the instrument. Reliability and internal consistency were examined through Cronbach’s alpha and McDonald’s omega. The analysis revealed a five-factor structure comprising functional appropriation, ethical and academic concerns, cost and accessibility, facilitating conditions, and perceived reliability and trustworthiness. Intention to use and performance expectancy merged into a single factor, and social influence did not emerge as a determinant. The model demonstrated strong reliability and internal consistency across all dimensions. The IMPACT model offers a validated framework for understanding faculty adoption of ChatGPT, emphasizing functional, ethical, and infrastructural factors over social influence. These findings provide a foundation for confirmatory analyses and contribute to advancing theoretical and practical insights into AI integration in higher education teaching.
ISSN:2227-7102
2076-3344
DOI:10.3390/educsci15111520
Zdroj:Education Database