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Modeling Behavioral Intentions toward Artificial Intelligence Adoption among Students and Educators in Nepalese Secondary and Higher Education Institutions: An Extended Technology Acceptance Model Approach

Aug 2026 · Nepalese Journal of Management and Technology · Vol 4, pp. 1-32 · 0 citations · 22 references

TL;DR

Investigating the behavioral intentions toward AI adoption among students and educators in Nepalese secondary schools and higher education institutions reveals that perceived usefulness, perceived ease of use, and trust exert significant positive effects on behavioral intention, whereas perceived risk negatively influences AI adoption intentions.

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology with significant implications for teaching, learning, and academic administration. As AI-enabled tools become increasingly integrated into educational environments, understanding the factors that shape users’ intention to adopt these technologies has become imperative, particularly in developing countries where empirical evidence remains limited. Grounded in the Technology Acceptance Model (TAM), this study investigates the behavioral intentions toward AI adoption among students and educators in Nepalese secondary schools and higher education institutions. The proposed model extends TAM by incorporating trust and perceived risk as additional determinants of adoption intention. A quantitative research design was employed using a structured questionnaire administered to 321 respondents comprising secondary-level students, university students, school teachers, and faculty members from public and private educational institutions in Nepal. Data were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to examine the relationships among perceived usefulness, perceived ease of use, trust, perceived risk, and behavioral intention toward AI adoption. The findings reveal that perceived usefulness, perceived ease of use, and trust exert significant positive effects on behavioral intention, whereas perceived risk negatively influences AI adoption intentions. The results indicate growing acceptance of AI technologies among educational stakeholders, although concerns regarding privacy, ethical issues, academic integrity, and overdependence on AI remain substantial challenges. This study extends the applicability of TAM in the educational context of a developing economy. It offers valuable implications for policymakers, educational institutions, and technology developers to foster digital literacy, promote responsible AI use, and establish supportive regulatory frameworks for sustainable AI integration in education.

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