Penguatan Kompetensi Guru SMK PGRI Kota Palembang Melalui Pemanfaatan Artificial Intelligence Dalam Perencanaan Pembelajaran

  • Ahmad Sanmorino Universitas Indo Global Mandiri
  • Hendra Di Kesuma Universitas Indo Global Mandiri
  • Indah Pratiwi Putri Universitas Indo Global Mandiri
  • Lastri Widya Astuti Universitas Indo Global Mandiri
  • Imelda Saluza Universitas Indo Global Mandiri
  • Tasmi Universitas Indo Global Mandiri
  • Nining Ariati Universitas Indo Global Mandiri
  • Dhamayanti Universitas Indo Global Mandiri
  • Faradillah Universitas Indo Global Mandiri
  • Fery Antony Universitas Indo Global Mandiri
  • Dona Marcelina Universitas Indo Global Mandiri
  • Rudi Heriansyah Universitas Indo Global Mandiri
Keywords: artificial intelligence, lesson planning, vocational school teachers

Abstract

Abstract: The development of artificial intelligence (AI) technology presents new opportunities in education, particularly in lesson planning. However, most vocational high school teachers in Palembang City, including those at SMK PGRI 2, still have limited knowledge and skills in utilizing AI. This problem is the background to the implementation of community service activities (PkM) with the aim of improving teacher competency in using Large Language Models (LLM) such as Gemini and ChatGPT to develop Lesson Implementation Plans (RPP). The methods used included needs surveys, interactive workshops, hands-on practice, and evaluation through post-tests and participant feedback. The results of the activity showed a significant increase, where teacher knowledge increased from 20% to 80% and the application of AI in lesson plans increased from 10% to 65%. The contribution of this activity lies in improving teachers' ability to utilize AI to develop lesson plans more effectively and providing a scientific basis for the application of LLM in lesson planning in vocational education.

Keywords: artificial intelligence, lesson planning, vocational school teachers

 

Abstrak: Perkembangan teknologi kecerdasan buatan (Artificial Intelligence) menghadirkan peluang baru dalam dunia pendidikan, khususnya dalam perencanaan pembelajaran. Namun, sebagian besar guru SMK di Kota Palembang, termasuk di SMK PGRI 2, masih memiliki keterbatasan dalam pengetahuan dan keterampilan pemanfaatan AI. Permasalahan ini melatarbelakangi dilaksanakannya kegiatan pengabdian kepada masyarakat (PkM) dengan tujuan meningkatkan kompetensi guru dalam menggunakan Large Language Models (LLM) seperti Gemini dan ChatGPT untuk menyusun Rencana Pelaksanaan Pembelajaran (RPP). Metode yang digunakan meliputi survei kebutuhan, workshop interaktif, praktik langsung, serta evaluasi melalui post-test dan umpan balik peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan, di mana pengetahuan guru meningkat dari 20% menjadi 80% dan penerapan AI dalam RPP naik dari 10% menjadi 65%. Kontribusi kegiatan ini terletak pada peningkatan kemampuan guru dalam memanfaatkan AI untuk menyusun RPP secara lebih efektif serta penyediaan dasar ilmiah bagi penerapan LLM dalam perencanaan pembelajaran di pendidikan vokasi.

Kata kunci: artificial intelligence, guru SMK, perencanaan pembelajaran

References

Adabor, E. S., Addy, E., Assyne, N., & Antwi-Boasiako, E. (2025). En-hancing sustainable academic course delivery using AI in tech-nical universities: An empirical analysis using adaptive learning theory. Sustainable Futures, 10, 100828. https://doi.org/10.1016/j.sftr.2025.100828

Bagherimajd, K., & Khajedad, K. (2025). Designing a model of sustainable education based on artificial intelli-gence in higher education. Com-puters and Education: Artificial In-telligence, 9, 100439. https://doi.org/10.1016/j.caeai.2025.100439

Bergdahl, N., & Sjöberg, J. (2025). Atti-tudes, perceptions and AI self-efficacy in K-12 education. Com-puters and Education: Artificial In-telligence, 8, 100358. https://doi.org/10.1016/j.caeai.2024.100358

Chan, J. H. M., Ho, K. H. M., & Dias, J. M. (2025). Strategies to incorporate generative artificial intelligence in simulation-based education among undergraduate students of healthcare professions: A scoping review. Clinical Simulation in Nursing, 106, 101795. https://doi.org/10.1016/j.ecns.2025.101795

Dawson, M. G., Deer, R., & Bo-guslawski, S. (2025). Cognitive dissonance in programming educa-tion: A qualitative exploration of the impact of generative AI on ap-plication-directed learning. Com-puters in Human Behavior Reports, 19, 100724. https://doi.org/10.1016/j.chbr.2025.100724

Heo, S., & Na, S. (2025). Ready for de-parture: Factors to adopt large lan-guage model (LLM)-based artificial intelligence (AI) technology in the architecture, engineering and construction (AEC) industry. Re-sults in Engineering, 25, 104325. https://doi.org/10.1016/j.rineng.2025.104325

Lin, X., Luo, Z., Du-Ikonen, L., Lin, X., Mao, Y., Jiang, H., Wang, S., Yu-an, C., Zhong, W., & Yu, Z. (2025). Generative artificial intelli-gence: Pioneering a new paradigm for research and education in smart energy systems. Energy and AI, 22, 100610. https://doi.org/10.1016/j.egyai.2025.100610

Otto, S., Lavi, R., & Brogaard Bertel, L. (2025). Human-GenAI interaction for active learning in STEM educa-tion: State-of-the-art and future di-rections. Computers & Education, 239, 105444. https://doi.org/10.1016/j.compedu.2025.105444

Pasquadibisceglie, V., Appice, A., Malerba, D., & Fiameni, G. (2025). Leveraging a large language model (LLM) to predict hospital admis-sions of emergency department pa-tients. Expert Systems with Appli-cations, 287, 128224. https://doi.org/10.1016/j.eswa.2025.128224

Petzel, Z. W., & Sowerby, L. (2025). Prejudiced interactions with large language models (LLMs) reduce trustworthiness and behavioral in-tentions among members of stigma-tized groups. Computers in Human Behavior, 165, 108563. https://doi.org/10.1016/j.chb.2025.108563

Pino Tarragó, J. C., Domínguez Gálvez, D. L., Regalado Jalca, J. J., & Vil-lavicencio Cedeño, E. G. (2025). Artificial intelligence and soft skills in civil engineering education: A Latin American curriculum gap with global implications. Research in Globalization, 11, 100307. https://doi.org/10.1016/j.resglo.2025.100307

Prasetya, F., Fortuna, A., Samala, A. D., Latifa, D. K., Andriani, W., Gusti, U. A., Raihan, M., Criollo-C, S., Kaya, D., & Cabanillas García, J. L. (2025). Harnessing artificial in-telligence to revolutionize voca-tional education: Emerging trends, challenges, and contributions to SDGs 2030. Social Sciences & Humanities Open, 11, 101401. https://doi.org/10.1016/j.ssaho.2025.101401

Theuner, K., Elmgren, T. M., Götling, A., May, M. C., & Akay, H. (2025). Weaving Knowledge Graphs and Large Language Models (LLMs): Leveraging Semantics for Contextualized Design Knowledge Retrieval. Procedia CIRP, 134, 1125–1130. https://doi.org/10.1016/j.procir.2025.03.073

Wang, J. (2025). EDCEW-LLM: Error detection and correction in English writing: A large language model-based approach. Alexandria Engi-neering Journal, 129, 1153–1164. https://doi.org/10.1016/j.aej.2025.08.005

Youssef, L., Elhoussaine, Z., Soufiane, N., & Noureddine, M. (2025). En-hancing Arabic aspect category de-tection using large language models (LLMs). Results in Engineering, 26, 105049. https://doi.org/10.1016/j.rineng.2025.105049

Published
2026-01-10