Integrating Deep Learning Into School Curriculum: Challenges, Strategies, and Future Directions
DOI:
https://doi.org/10.62007/joupi.v3i1.415Keywords:
deep learning, integrasi kurikulum, kecerdasan buatan dalam pendidikan, pelatihan guru, tantangan etikaAbstract
Integrasi deep learning (DL) ke dalam kurikulum sekolah menawarkan peluang besar untuk meningkatkan kualitas pendidikan melalui pembelajaran personalisasi, penilaian otomatis, dan pengambilan keputusan berbasis data. Studi ini mengidentifikasi empat tantangan utama dalam implementasinya: ketidakselarasan pedagogis antara metode pengajaran tradisional dengan pendekatan analitis DL (32% studi), keterbatasan infrastruktur termasuk perangkat keras dan akses internet (45%), masalah etika terkait privasi data siswa dan bias algoritma (28%), serta kesiapan guru yang belum memadai (40%). Melalui tinjauan literatur sistematis terhadap 45 artikel peer-reviewed, penelitian ini mengusulkan solusi strategis berupa desain kurikulum berbasis proyek, adopsi komputasi awan, program pelatihan guru komprehensif, dan kerangka etika yang kuat. Studi kasus komparatif dari Singapura, Finlandia, dan Indonesia menunjukkan bahwa integrasi DL yang sukses membutuhkan dukungan kebijakan, aksesibilitas teknologi, dan adaptasi pedagogis. Penelitian merekomendasikan model implementasi bertahap yang disesuaikan dengan konteks lokal, dengan penekanan pada kolaborasi multipihak antara pendidik, pembuat kebijakan, dan penyedia teknologi. Temuan ini berkontribusi pada pengembangan kebijakan pendidikan AI yang inklusif dan berkelanjutan untuk mengatasi tantangan teknologi dan sumber daya manusia di berbagai setting pendidikan.
References
Ahmad, K., Iqbal, W., El-Hassan, A., Qadir, J., Benhaddou, D., Ayyash, M., & Al-Fuqaha, A. (2023). Data-driven artificial intelligence in education: A comprehensive review. IEEE Transactions on Learning Technologies, 17, 12–31.
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440.
Chen, C., Liu, J., Tan, H., Li, X., Wang, K. I.-K., Li, P., Sakurai, K., & Dou, D. (2025). Trustworthy federated learning: Privacy, security, and beyond. Knowledge and Information Systems, 67(3), 2321–2356.
Cheng, E. C. K., & Wang, T. (2023). Leading digital transformation and eliminating barriers for teachers to incorporate artificial intelligence in basic education in Hong Kong. Computers and Education: Artificial Intelligence, 5, 100171.
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022). Distributed artificial intelligence empowered by end-edge-cloud computing: A survey. IEEE Communications Surveys & Tutorials, 25(1), 591–624.
El Asmar, W. (2022). The effectiveness of AI-powered digital educational platforms: Students’ attainment and teachers’ teaching strategies in a private high school in Dubai. The British University in Dubai.
Fedele, A., Punzi, C., & Tramacere, S. (2024). The ALTAI checklist as a tool to assess ethical and legal implications for a trustworthy AI development in education. Computer Law & Security Review, 53, 105986.
Hussain, T., Yu, L., Asim, M., Ahmed, A., & Wani, M. A. (2024). Enhancing e-learning adaptability with automated learning style identification and sentiment analysis: a hybrid deep learning approach for smart education. Information, 15(5), 277.
Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in technological innovations in higher education (pp. 103–117). CRC Press.
Kovari, A. (2025). A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade. Social Sciences & Humanities Open, 11, 101335.
Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359.
Matthew, U. O., Kazaure, J. S., & Okafor, N. U. (2021). Contemporary development in E-Learning education, cloud computing technology & internet of things. EAI Endorsed Trans. Cloud Syst., 7(20), e3.
Muliawan, P. (2024). Analisis Penerapan Kurikulum Merdeka Dalam Pengajaran Bahasa Indonesia: Tinjauan Literatur Terhadap Isu Dan Tantangan Terkini. Jurnal Intelek Dan Cendikiawan Nusantara, 1(5), 7932–7942.
Okoye, K., Hussein, H., Arrona-Palacios, A., Quintero, H. N., Ortega, L. O. P., Sanchez, A. L., Ortiz, E. A., Escamilla, J., & Hosseini, S. (2023). Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks. Education and Information Technologies, 28(2), 2291–2360.
Padovano, A., & Cardamone, M. (2024). Towards human-AI collaboration in the competency-based curriculum development process: The case of industrial engineering and management education. Computers and Education: Artificial Intelligence, 7, 100256.
Posekany, A. (2024). Project-Based Learning Connecting Robotics and Artificial Intelligence. International Conference on Interactive Collaborative Learning, 319–326.
Roshanaei, M. (2024). Towards best practices for mitigating artificial intelligence implicit bias in shaping diversity, inclusion and equity in higher education. Education and Information Technologies, 29(14), 18959–18984.
Salla, S., Pasumarthy, A., Tadikonda, D., Parsha, T., & Mandal, S. K. (2025). Private AI in Education: Critical Challenges and Aspects of Enhancement Strategies. In Sustainable Development Using Private AI (pp. 109–131). CRC Press.
Salloum, S. A. (2024). AI perils in education: Exploring ethical concerns. Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom, 669–675.
Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072.
Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 1–20.
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2024). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development.
Štuikys, V., & Burbait, R. (2024). Evolution of STEM-Driven Computer Science Education. Cham, Switzerland: Springer, 368.
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572.
Tlili, A., Zhang, J., Papamitsiou, Z., Manske, S., Huang, R., Kinshuk, & Hoppe, H. U. (2021). Towards utilising emerging technologies to address the challenges of using Open Educational Resources: a vision of the future. Educational Technology Research and Development, 69, 515–532.
Yang, Y., Sun, W., Sun, D., & Salas-Pilco, S. Z. (2024). Navigating the AI-Enhanced STEM education landscape: a decade of insights, trends, and opportunities. Research in Science & Technological Education, 1–25.
Zaman, B., Sharma, A., Ram, C., Kushwah, R., Muradia, R., Warjri, A., Lyngdoh, D. K., & Lyngdoh, M. K. (2024). Modeling education impact: a machine learning-based approach for improving the quality of school education. Journal of Computers in Education, 11(4), 1181–1214.
Zhao, Y., Zhao, M., & Shi, F. (2024). Integrating moral education and educational information technology: A strategic approach to enhance rural teacher training in universities. Journal of the Knowledge Economy, 15(3), 15053–15093.
