Between Hope and Anxiety: An Exploratory Sequential Mixed Methods Study on the Psychology of AI Adoption in Education
Authors
I Kadek Satria Arsana1*, Putri Bintang Pratiwi Harahap2, Gilly Marlya Tiwow1, Herman Philips Dolonseda1, Allen A. Ch. Manongko1, Listriyanti Palangda1
1 Department of Economics Education, Faculty of Economics and Business, Manado State University, Tondano, Indonesia.
2 Japanese Language Education, Faculty of Languages and Arts, Manado State University, Tondano, Indonesia.
Abstract
This exploratory sequential mixed-methods study examines the psychological dynamics of artificial intelligence adoption among participants in educational contexts in Sulawesi, Indonesia. The qualitative phase identified four interrelated psychological constructs: utilitarian motivation, professional vulnerability, cognitive dissonance, and adaptive reorientation. The subsequent quantitative phase (N = 411), analyzed using PLS-SEM, validated the structural relationships among these constructs. The results showed that utilitarian motivation and professional vulnerability significantly predicted adaptive reorientation, which emerged as the strongest predictor of AI adoption behavior. Cognitive dissonance functioned as both a barrier and a catalyst, although its overall effect size was comparatively modest. Adaptive reorientation emerged as the mediating mechanism linking the three psychological predictor constructs to AI adoption behavior. These findings should be interpreted in light of the study’s contextual scope and exploratory sequential mixed-methods design, which prioritizes interpretive depth over broad generalization. Overall, the findings provide a context-sensitive extension of the Technology Acceptance Model by integrating emotional ambivalence and epistemic concern and proposing a framework that emphasizes human-centered AI literacy, ethical governance, and professional learning in collectivistic educational contexts.
Keywords: adaptive reorientation; Artificial Intelligence adoption; cognitive dissonance; professional vulnerability; Technology Acceptance Model
PAGES:223-234
doi:10.24193/cbb.2026.30.11
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