<![CDATA[The integration of digital technology in education has significantly transformed evaluation practices and teacher professional development. However, many educational institutions still rely on conventional evaluation systems that emphasize administrative reporting rather than providing meaningful feedback for improving instructional quality. This study examines the integration of artificial intelligence (AI) in educational evaluation to support reflective teaching and data-driven professional development. A mixed-method sequential explanatory design was employed, involving 120 teachers and school administrators from junior high schools in West Nusa Tenggara, Indonesia. Quantitative data were collected through structured questionnaires and analyzed using descriptive statistics, while qualitative data from interviews and document analysis were examined using thematic analysis. The findings reveal a high level of teacher acceptance of AI-based evaluation systems, with an overall mean score of 4.28 (very positive category). The highest-rated aspect was continuous feedback provision (M = 4.36), followed by comprehensive evaluation data (M = 4.32). Qualitative findings indicate that AI-based evaluation enhances teachers’ understanding of classroom interaction patterns, student engagement, and instructional effectiveness. These findings demonstrate that AI-assisted evaluation systems contribute to more effective monitoring of teaching performance and strengthen evidence-based instructional decision-making. This study provides empirical evidence for the development of AI-integrated evaluation frameworks that support sustainable improvement in teaching quality and teacher professional competence.]]>
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