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All Journal Jurnal Ilmiah Mandala Education (JIME) JISIP: Jurnal Ilmu Sosial dan Pendidikan JUPE : Jurnal Pendidikan Mandala Abdi Masyarakat Business Management Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya Pendidikan Bahasa dan Sastra Journal of Educational Studies
Fandir, A
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Agriculture, Biological Sciences & Forestry Arts Humanities Decision Sciences, Operations Research & Management Education Energy Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Journal : Journal of Educational Studies

 1 
Integrating Artificial Intelligence and Educational Evaluation for Improving Teacher Professional Competence Syahrir; Fandir, A.; Nurfidah
Journal of Educational Studies Vol. 4 No. 1 (2026): April
Publisher : Lembaga Bale Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58218/jes.v4i1.2413

Abstract

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.
A Data-Driven Policy–Governance Alignment Model for Lecturer Performance Management in Vocational Higher Education Fandir, A.; Permana, Johar; Aedi, Nur; Triatna, Cepi
Journal of Educational Studies Vol. 4 No. 1 (2026): April
Publisher : Lembaga Bale Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58218/jes.v4i1.2419

Abstract

The persistent misalignment between national higher education policy mandates and institutional governance practices in Indonesian vocational higher education institutions (VHEIs) continues to obstruct effective lecturer performance management. This study develops and validates the Data-Driven Policy–Governance Alignment Model (DPGAM), a theoretically integrated framework that synthesizes policy alignment theory, institutional analytics theory, and organizational learning theory to diagnose and bridge the policy–governance gap in VHEIs. Employing a sequential explanatory mixed-methods design with a comparison group longitudinal component, this study aimed to analyze data from 4,424 lecturers across 142 VHEIs between 2022 and 2024. A 48-item validated instrument, the Governance Alignment Assessment Tool (GAAT), measured six governance dimensions. Common method bias was assessed using Harman's single-factor test (variance explained = 22.7%, below the 50% threshold) and procedural remedies. Structural equation modeling (SEM) identified Policy Compliance Index (? = 0.421, p < .001), Governance Transparency Score (? = 0.318, p < .001), and Data Infrastructure Quality (? = 0.287, p = .003) as the strongest predictors of the Policy Alignment Score (PAS). Post-DPGAM implementation demonstrated a statistically significant improvement in the Composite Lecturer Performance Index (CLPI) from 63.5 to 76.8 (Cohen's d = 1.32, p < .001; difference-in-differences = 11.4 points). Unexpectedly, private polytechnics showed disproportionately lower transparency compliance than community colleges, challenging assumptions about institutional size and governance capacity. The model demonstrated strong construct validity (Cronbach's ? = 0.74–0.87) and acceptable model fit (CFI = .962, RMSEA = .048). These findings offer a theoretically grounded, empirically validated, and reproducible governance framework for policymakers and administrators in vocational higher education
Co-Authors Al farisi, Moh. Salman Andi Pranata, Andi Auliya, Indana Zahratul Cepi Triatna, Cepi I Putu Buda Yasa Johar Permana Junaedi Junaedi Lila Alwyah Mahfuz, Muhammad Nur Aedi, Nur Nurfidah Nurfidah, Nurfidah Nurrfidah, Nurrfidah Raden Sumiadi Rahmatika, Qais Ainu Ramdhani, Suci Rosdiyanti, Evi supriadin supriadin Syahrir Syahrir Syahrir