Penilaian Kinerja Berbasis Kecerdasan Buatan dalam Pendidikan: Menyeimbangkan Efisiensi Algoritmik dan Evaluasi Humanistik
Keywords:
Efisiensi Algoritma, Penilaian Kinerja Guru, Evaluasi Humanistik, Model Evaluasi Hibrida, Manajemen Kinerja PendidikanAbstract
Integrasi Artificial Intelligence (AI) dalam mengevaluasi kinerja guru menawarkan efisiensi tinggi, namun berisiko mengikis nilai-nilai kemanusiaan dalam pendidikan. Penelitian ini menganalisis integrasi sistem penilaian kinerja berbasis AI di Satuan Pendidikan Muadalah Ulya Blokagung Banyuwangi, dengan fokus pada penyelarasan antara efisiensi algoritma dan evaluasi humanistik. Menggunakan desain studi kasus kualitatif, penelitian ini melibatkan delapan guru di sekolah menengah atas. Data dikumpulkan melalui wawancara mendalam, observasi partisipatif, dan analisis dokumen reflektif, yang kemudian keabsahan datanya dipastikan melalui triangulasi sebelum dianalisis menggunakan teknik analisis tematik interaktif. Temuan menunjukkan bahwa AI efektif dalam mengoptimalkan pemrosesan data, mereduksi bias subjektif, dan memberikan feedback seketika. Namun, sistem algoritma ini gagal menangkap aspek kontekstual yang tersembunyi, seperti kecerdasan emosional, dinamika kelas, dan komitmen afektif guru. Sebagai solusi, penelitian ini mengusulkan model evaluasi hybrid yang menempatkan AI sebagai instrumen analitis pendukung, sementara tindakan verifikasi, penilaian kualitatif, dan keputusan final tetap berada di tangan manusia. Model ini memberikan kerangka kerja bagi pembuat kebijakan untuk merancang sistem manajemen kinerja masa depan yang canggih secara teknis namun tetap etis, adil, dan humanis.
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References
Aaltonen, A., & Stelmaszak, M. (2024). The performative production of trace data in knowledge work. Information Systems Research, 35(3), 1448–1462. https://doi.org/https://doi.org/10.1287/isre.2019.0357
Alghamdi, L. H., & Alghizzi, T. M. (2025). Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions. Frontiers in Education, 10, 1704820. https://doi.org/https://doi.org/10.3389/feduc.2025.1704820
Astratov, V. N., Sahel, Y. Ben, Eldar, Y. C., Huang, L., Ozcan, A., Zheludev, N., Zhao, J., Burns, Z., Liu, Z., & Narimanov, E. (2023). Roadmap on label‐free super‐resolution imaging. Laser & Photonics Reviews, 17(12), 2200029. https://doi.org/https://doi.org/10.1002/lpor.202200029
Bahari, A. (2026). Leveraging AI-enhanced interventions for targeted EFL teacher development: advancing professional growth, digital literacy, and rapport-building in higher education. Interactive Learning Environments, 1–38. https://doi.org/https://doi.org/10.1080/10494820.2026.2614081
Bornmann, L., & Marewski, J. N. (2024). Opium in science and society: numbers and other quantifications. Scientometrics, 129(9), 5313–5346.
Goh, D. H. (2024). “He looks very real”: Media, knowledge, and search‐based strategies for deepfake identification. Journal of the Association for Information Science and Technology, 75(6), 643–654. https://doi.org/https://doi.org/10.1002/asi.24867
Hassen, M. Z. (2025). Effort vs. Automation: The Core Conflict of AI in Education. Innovation, 6(3), 99–111. https://doi.org/https://doi.org/10.11648/j.innov.20250603.17
Heemsbergen, L., Krebs, S., Gorur, R., & Maddox, A. (2024). Algorithmic performance management in higher education: viva! 365 ways of surveillance. Surveillance & Society, 22(2), 73–87. https://doi.org/https://doi.org/10.24908/ss.v22i2.15776
Jillepalli, A. (2025). Effects of Neoliberalism on Computing Education Research and Practice. ACM Transactions on Computing Education, 25(4), 1–13. https://doi.org/https://doi.org/10.1145/3766901
Jungman, H., Apperley, T., Nylund, N., & Sotamaa, O. (2026). Sidetalking: Domesticating Nokia’s playful designed identity. Mobile Media & Communication, 14(2), 411–430. https://doi.org/https://doi.org/10.1177/20501579251384606
Kassen, M. (2025). Blockchain and public sector innovations: understanding decentralized models of technology adoption in e-government. Innovation: The European Journal of Social Science Research, 1–33. https://doi.org/https://doi.org/10.1080/13511610.2025.2591031
Küçükuncular, A., & Ertugan, A. (2026). Teaching in today’s world: a Marxian critique of AI in education. Journal of Academic Ethics, 24(1), 18. https://doi.org/https://link.springer.com/article/10.1007/s10805-025-09692-2
Lehto, O., & Paniagua, P. (2025). Designing freedom: Allende, Pinochet and the twin experiments in cyber-socialism and neoliberalism. Economy and Society, 54(2), 334–358. https://doi.org/https://doi.org/10.1080/03085147.2025.2513800
Nazari, M., Ghorbani, B. D., Karimpour, S., & Hu, G. (2025). Purpose‐Based Emotion Labor: An Exploratory Heuristic for Expanding Research on Teacher Emotion (s). International Journal of Applied Linguistics, 35(4), 2215–2225. https://doi.org/https://doi.org/10.1111/ijal.12766
Nguyen, N. N., & Barbieri, W. (2026). Generative AI in work‐integrated learning: Supporting pre‐service teachers’ emotional labour and self‐management in Australian initial teacher education. British Journal of Educational Technology. https://doi.org/https://doi.org/10.1111/bjet.70043
Pakarinen, P., & Huising, R. (2025). Relational expertise: What machines can’t know. Journal of Management Studies, 62(5), 2053–2082. https://doi.org/https://doi.org/10.1111/joms.12915
Qi, L., Zhao, L., Qi, C., Bing, S., Dang, M., & Chen, J. (2026). Drivers and pathways of AI academic mentor acceptance: An SEM‐fsQCA study integrating cognitive appraisal theory and the AIDUA model. British Educational Research Journal. https://doi.org/https://doi.org/10.1002/berj.70147
Ramadhani, K. (2024). Peluang dan tantangan penggunaan artificial intelligence (AI) dalam proses pembelajaran. HIKMAH: Jurnal Pendidikan Islam, 1(2), 105-115.
Retzlaff, C. O., Das, S., Wayllace, C., Mousavi, P., Afshari, M., Yang, T., Saranti, A., Angerschmid, A., Taylor, M. E., & Holzinger, A. (2024). Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities. Journal of Artificial Intelligence Research, 79, 359–415. https://doi.org/https://doi.org/10.1613/jair.1.15348
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023). A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspectives on Psychological Science, 18(5), 1062–1096. https://doi.org/https://doi.org/10.1177/17456916221134490
Udeozor, C., Abegão, F. R., & Glassey, J. (2024). Measuring learning in digital games: Applying a game‐based assessment framework. British Journal of Educational Technology, 55(3), 957–991. https://doi.org/https://doi.org/10.1111/bjet.13407
Wang, C., Chen, Y., Hu, Z., Li, Y., & Gu, X. (2025). The journey of challenges and victories: Exploring the transformation action framework in the GenAI era from multifaceted policies: The journey of challenges and victories: Exploring the transformation action framework. Educational Technology Research and Development, 1–43.
Wijethilake, C., Upadhaya, B., Adhikari, P., Soobaroyen, T., & Jayasinghe, K. (2026). Culturally and politically embedded management controls in innovation transitions of PPPs: comparative cases from a developing economy. Qualitative Research in Accounting & Management, 23(3), 308–345. https://doi.org/https://doi.org/10.1108/QRAM-10-2024-0207
Wu, C., Zhang, W., Hu, L., & Li, M. (2025). Research on middle school teachers’ technostress empowered by artificial intelligence. Frontiers in Artificial Intelligence, 8, 1732088. https://doi.org/https://doi.org/10.3389/frai.2025.1732088
Zhang, M. M., Cooke, F. L., Ahlstrom, D., & McNeil, N. (2025). The rise of algorithmic management and implications for work and organisations. New Technology, Work and Employment, 40(3), 659–671. https://doi.org/https://doi.org/10.1111/ntwe.12343

