DEVELOPING AN EVALUATION INSTRUMENT BASED ON THE TECHNOLOGY ACCEPTANCE MODEL FOR TEACHERS' ARTIFICIAL INTELLIGENCE READINESS - Nugraha, Hari Din; Gumelar, Agung; AK, Tri Bambang; Anggrainy, Rani; Nurahman, Muhamad Habib Alfin; Gunadi, Diandra Keisha (Universitas Pendidikan Indonesia, 2025)
In Indonesia, only 48% of vocational teachers demonstrate limited readiness to integrate Artificial Intelligence into learning. Addressing this issue requires a reliable instrument to evaluate prospective teachers' preparedness. This study developed and validated an instrument based on the Technology Acceptance Model to assess the digital competencies of Mechanical Engineering pre-service teachers in Indonesian vocational education. The research quantitatively evaluated four constructs: Perceived Usefulness, Perceived Ease of Use, Behavioral Intention, and Self-Efficacy. Involving 100 respondents from a community service program, the development followed three stages: preparation, expert content validation using Aiken’s V, and construct validation via Exploratory Factor Analysis. Results confirmed unidimensional structures for all constructs. Perceived Usefulness and Perceived Ease of Use showed strong validity with high eigenvalues (4.984 and 5.063) and explained variance (49.8% and 50.6%). The Kaiser-Meyer-Olkin measure confirmed sampling adequacy (0.833–0.909). Aiken’s V analysis indicated high content (0.83), construct (0.81), and linguistic validity (0.85). Key findings highlight deficiencies in Artificial Intelligence tool proficiency, emphasizing the need for curriculum alignment with Industry 4.0 demands. This study provides a validated tool to guide teacher training and policy, effectively addressing the digital divide in vocational education and bridging a critical gap between theory and practice.