Enhancing Teacher Competence in Deep Learning–Oriented Instructional Design: A ChatGPT–Supported Training at SMPN 5 Indralaya Utara in the Era of Artificial Intelligence
DOI:
10.29303/ujcs.v7i1.1324Published:
2026-03-31Downloads
Abstract
The integration of Artificial Intelligence (AI) in education presents opportunities to enhance teacher competence and promote Deep Learning–oriented pedagogy. This study reports on a community service program conducted at SMP Negeri 5 Indralaya Utara, aimed at improving teachers’ skills in designing lesson plans that foster higher-order thinking and meaningful learning experiences with the support of ChatGPT. Ten teachers participated in a structured training program, which included pre-assessment, theoretical orientation, hands-on workshops, collaborative lesson plan development, mentoring, and post-assessment. Teachers’ understanding and application of Deep Learning principles were evaluated using pretest and posttest instruments, and the magnitude of improvement was measured using Normalized Gain (N-Gain). Results indicated an average N-Gain of 0.53, categorized as medium, demonstrating significant enhancement in formulating higher-order learning objectives, designing reflective and analytical activities, and utilizing AI to generate contextualized instructional materials. The findings highlight the effectiveness of practice-based, collaborative, and mentored training in improving both technological literacy and pedagogical skills. Moreover, teachers began transitioning from traditional knowledge-transfer approaches to constructivist, student-centered practices, positioning AI as a reflective pedagogical partner. This study provides evidence that structured professional development integrating AI can foster innovation, adaptability, and reflective instructional design, supporting Education 5.0 objectives and enhancing the quality of classroom learning experiences.
Keywords:
Artificial Intelligence Deep Learning ChatGPT Teacher Training Lesson Plan Professional DevelopmentReferences
Garcia Lopez, E., & Martinez, R. (2022). Generative AI in teacher education: Creativity, assessment, and engagement. Journal of Educational Technology, 19(3), 214–230.
Hake, R. R. (1999). Analyzing change/gain scores. Journal of Physics Education Research, 7(3), 1–7.
Johnson, P., Liu, Y., & Nguyen, H. (2021). Deep Learning–oriented pedagogy: A conceptual framework for meaningful learning. Educational Research Review, 15, 33–47.
Lee, S., & Kim, J. (2021). Digital literacy and challenges to AI adoption in schools: Perspectives of K–12 educators. Computers & Education, 160, 104039.
Miller, T., Roberts, C., & Zhang, L. (2021). Barriers to AI integration in classroom instruction: A mixed methods study. Journal of Teacher Education, 72(4), 459–473.
Nurdin, L. (2020). Pendekatan kolaboratif dalam pengembangan profesionalisme guru untuk inovasi pembelajaran berkelanjutan. Jurnal Profesi Kependidikan, 5(2), 89–102.
Prasetyo, A. (2023). Integrasi kecerdasan buatan dalam strategi pembelajaran adaptif dan reflektif. Jurnal Pedagogi Digital, 8(2), 77–90.
Rahmawati, S. (2024). Pemanfaatan ChatGPT dalam meningkatkan kreativitas guru dalam penyusunan perangkat ajar. Jurnal Inovasi Teknologi Pendidikan, 12(1), 45–58.
Smith, A., & Brown, B. (2022). Using ChatGPT to support lesson planning: Implications for teacher workload and creativity. International Journal of Artificial Intelligence in Education, 32(2), 150–167.
UNESCO. (2020). Artificial intelligence and education: Guidance for policy-makers. UNESCO Publishing.
Wang, L., Zhao, Q., & Chen, M. (2023). Professional development with AI: Effects on teacher self-efficacy and instructional innovation. Journal of Learning Analytics, 10(1), 45–62.
Yusuf, M. (2021). Efektivitas pelatihan berbasis praktik dalam meningkatkan kompetensi digital guru. Jurnal Pendidikan dan Teknologi, 6(3), 101–115.
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Copyright (c) 2026 Ismet Ismet, Ahmad Fitra Ritonga, Imam Arif Pribadi, Frendi Ihwan Syamsudin

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