Analysis of the Readiness for Implementing Deep Learning Curriculum in Madrasah from the Perspective of Educators
DOI:
https://doi.org/10.54069/attadrib.v8i1.841Keywords:
Deep learning-based curriculum, Madrasah readiness, Educators’ perspective, Technological infrastructure, Professional developmentAbstract
This study explores the readiness of madrasahs to implement a deep learning-based curriculum from the perspective of educators. As technology advances and digital skills become increasingly important in education, integrating deep learning approaches is expected to improve learning quality in Islamic schools. Using a qualitative case study method, this research was conducted in several madrasahs that have begun incorporating technology into their teaching practices. Data were collected through in-depth interviews with educators and classroom observations. The findings show a strong interest among teachers in applying deep learning strategies. However, several challenges hinder effective implementation, including limited access to technology, lack of professional training, and insufficient understanding of deep learning concepts. Despite these issues, most teachers expressed willingness to adapt and learn, provided they received adequate support from schools and the government. The study concludes that three key factors determine the success of deep learning curriculum implementation in madrasahs: (1) continuous professional development for teachers, (2) sufficient technological infrastructure, and (3) a deeper understanding of the pedagogical value of deep learning. This study implies that successful implementation requires collaboration from all parties. The government should provide policies and funding for teacher training and technology access. Madrasahs must facilitate ongoing learning opportunities, while teachers should remain open to improving their digital and pedagogical skills. With strong collaboration, deep learning can be effectively integrated, enhancing the quality of Islamic education in the digital age.
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Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318. https://doi.org/10.1145/2976749.2978318
Abedi, E. A. (2024). Tensions between technology integration practices of teachers and ICT in education policy expectations: Implications for change in teacher knowledge, beliefs and teaching practices. Journal of Computers in Education, 11(4), 1215–1234. https://doi.org/10.1007/s40692-023-00296-6
Achille, A., & Soatto, S. (2018). Information Dropout: Learning Optimal Representations Through Noisy Computation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2897–2905. https://doi.org/10.1109/TPAMI.2017.2784440
Ahmad, J., Farman, H., & Jan, Z. (2019). Deep Learning Methods and Applications. In M. Khan, B. Jan, & H. Farman, Deep Learning: Convergence to Big Data Analytics (pp. 31–42). Springer Singapore. https://doi.org/10.1007/978-981-13-3459-7_3
Arifianto, R. (2020). Penerapan teknologi dalam pendidikan di Indonesia: Tantangan dan peluang. Jurnal Teknologi Pendidikan, 15(2), 112–125.
Ayman, A., Onsy, A., Attallah, O., Brooks, H., & Morsi, I. (2025). Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities. Measurement, 245, 116589. https://doi.org/10.1016/j.measurement.2024.116589
Beetham, H., S., R. (2013). Rethinking pedagogy for a digital age: Designing for 21st century learning (2nd ed). Routledge.
Cai, Q., Tao, R., Fang, X., Xie, X., & Liu, G. (2025). A deep reinforcement active learning method for multi-label image classification. Computer Vision and Image Understanding, 257, 104351. https://doi.org/10.1016/j.cviu.2025.104351
Dewantara, H. (2024). Membangun masa depan pendidikan: Inovasi dan tantangan dalam sertifikasi guru di Indonesia. PT Indonesia Delapan Kreasi Nusa.
Egistiani, S., Wibowo, D. V., Nurseha, A., & Kurnia, T. (2023). Strategi Guru Dalam Mendidik Anak Menuju Indonesia Emas 2045. Educatio, 17(2), 141. https://doi.org/10.29408/edc.v17i2.6859
Faridli, E. M., Abidin, N., Sutama, S., Sutopo, A., & Murtiyasa, B. (2024). Tantangan menuju pendidikan unggul: Membangkitkan produktivitas institusi pendidikan untuk kualitas pendidikan yang lebih baik di Indonesia. Jurnal EDUCATIO: Jurnal Pendidikan Indonesia, 10(1), 186. https://doi.org/10.29210/1202423797
Gonsalves, C. (2024). Dampak AI Generatif pada Pemikiran Kritis: Meninjau Kembali Taksonomi Bloom.
Grani?, A. (2022). Educational Technology Adoption: A systematic review. Education and Information Technologies, 27(7), 9725–9744. https://doi.org/10.1007/s10639-022-10951-7
Hariyadi, H. (2023). Tranformasi Digital Madrasah Untuk Peningkatan Mutu Layanan Pendidikan Di Mts Al Kaustar Kota Depok. Jurnal Minfo Polgan, 12(1), 42. https://doi.org/10.33395/jmp.v12i1.12314
Hart, R., Ivtzan, I., & Hart, D. (2013). Mind the Gap in Mindfulness Research: A Comparative Account of the Leading Schools of Thought. Review of General Psychology, 17(4), 453–466. https://doi.org/10.1037/a0035212
Hattie, J. (2008). Visible Learning (0 ed.). Routledge. https://doi.org/10.4324/9780203887332
Hu, L., Zhang ,Wenlan, & and Lin, P. (n.d.). Can the utilization of technology-enhanced learning spaces lead to improved learning outcomes? A meta-analysis based on 39 experimental and quasi-experimental studies. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2024.2436943
Huda, M., & Suwahyu, I. (2024). Peran Artificial Intelligence (Ai) Dalam Pembelajaran Pendidikan Agama Islam. Referensi Islamika Jurnal Studi Islam, 2(2), 53. https://doi.org/10.61220/ri.v2i2.005
Jie, A. L. X., & Kamrozzaman, N. A. (2024). The Challenges of Higher Education Students Face in Using Artificial Intelligence (AI) against Their Learning Experiences. Open Journal of Social Sciences, 12(10), Article 10. https://doi.org/10.4236/jss.2024.1210025
Khalil, M., Shakya, R., & Liu, Q. (2025). Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning (arXiv:2503.13550). arXiv. https://doi.org/10.48550/arXiv.2503.13550
Khan, S., Mazhar, T., Shahzad, T., Khan, M. A., Rehman, A. U., Saeed, M. M., & Hamam, H. (2025). Harnessing AI for sustainable higher education: Ethical considerations, operational efficiency, and future directions. Discover Sustainability, 6(1), 23. https://doi.org/10.1007/s43621-025-00809-6
Kova?, V. B., Nome ,D. Ø, Jensen ,A. R., & and Skreland, L. Lj. (n.d.). The why, what and how of deep learning: Critical analysis and additional concerns. Education Inquiry, 0(0), 1–17. https://doi.org/10.1080/20004508.2023.2194502
Kumar Dhaked, D., Narayanan, V. L., Gopal, R., Sharma, O., Bhattarai, S., & Dwivedy, S. K. (2025). Exploring deep learning methods for solar photovoltaic power output forecasting: A review. Renewable Energy Focus, 53, 100682. https://doi.org/10.1016/j.ref.2025.100682
Kurniawan, A. (2020). Penerapan teknologi informasi di madrasah: Analisis hambatan dan solusi. Jurnal Pendidikan Islam, 10(1), 45–58.
Maola, P. S., Handak, I. S. K., & Herlambang, Y. T. (2024). Penerapan Artificial Intelligence Dalam Pendidikan Di Era Revolusi Industri 4.0. Educatio, 19(1), 61. https://doi.org/10.29408/edc.v19i1.24772
McElhaney, K. W., Chang, H.-Y., Chiu, J. L., & Linn, M. C. (2015). Evidence for effective uses of dynamic visualisations in science curriculum materials. Studies in Science Education, 51(1), 49–85. https://doi.org/10.1080/03057267.2014.984506
Naila, I., Atmoko, A., Dewi, R. S. I., & Kusumajanti, W. (2023). Pengaruh Artificial Intelligence Tools terhadap Motivasi Belajar Siswa Ditinjau dari Teori Rogers. At-Thullab Jurnal Pendidikan Guru Madrasah Ibtidaiyah, 7(2), 150. https://doi.org/10.30736/atl.v7i2.1774
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Nguyen-Tat, T. B., Hung, T. Q., Nam, P. T., & Ngo, V. M. (2025). Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities. Alexandria Engineering Journal, 119, 558–586. https://doi.org/10.1016/j.aej.2025.01.090
Oktavianus, A. J. E., Naibaho, L., & Rantung, D. A. (2023). Pemanfaatan Artificial Intelligence pada Pembelajaran dan Asesmen di Era Digitalisasi. JURNAL KRIDATAMA SAINS DAN TEKNOLOGI, 5(2), 473. https://doi.org/10.53863/kst.v5i02.975
Pandya, D. V., Monani, D., Aahuja, D., & Chotai, U. (2024). Traditional vs. Modern Education: A Comparative Analysis (SSRN Scholarly Paper 4876084). Social Science Research Network. https://doi.org/10.2139/ssrn.4876084
Paramansyah, H. A., & SE, M. (2020). Manajemen pendidikan dalam menghadapi era digital. Arman Paramansyah.
Parwati, N. P. Y. & I Nyoman Bayu Pramartha. (2021). Strategi Guru Sejarah Dalam Menghadapi Tantangan Pendidikan Indonesia Di Era Society 5.0. https://doi.org/10.5281/ZENODO.4661256
Prince, S. J. (2023). Understanding deep learning. . MIT press.
Ravi, V. K., & Cheruku, S. R. (2022). AI and Machine Learning in Predictive Data Architecture (SSRN Scholarly Paper 5068532). Social Science Research Network. https://doi.org/10.56726/IRJMETS19990
Reza Bagus Anugerah. (2023). Transformasi Madrasah dalam Menghadapi Tantangan di Era Society 5.0. At-Tarbawi: Jurnal Kajian Kependidikan Islam, 8(2), 153–170. https://doi.org/10.22515/attarbawi.v8i2.7889
Rifky, S. (2024). Dampak Penggunaan Artificial Intelligence Bagi Pendidikan Tinggi. Indonesian Journal of Multidisciplinary on Social and Technology, 2(1), 37. https://doi.org/10.31004/ijmst.v2i1.287
RITHVIK GUJJULA & Kamaljeet Sanghera. (2023). Ethical Considerations and Data Privacy in AI Education. Journal of Student-Scientists’ Research, Vol. 5 (2023). https://doi.org/10.13021/JSSR2023.3958
Rofiq, M. H., Fahmi, Q., Rokhman, M., & Khamim, N. (2025). Pendidikan Karakter di Madrasah Berbasis Pesantren: Analisis Implementasi dan Evaluasi. Attadrib: Jurnal Pendidikan Guru Madrasah Ibtidaiyah, 7(2), 192–203. https://doi.org/10.54069/attadrib.v7i2.837
Ronsumbre, S., Rukmawati, T., Sumarsono, A., & Waremra, R. S. (2023). Pembelajaran Digital Dengan Kecerdasan Buatan (AI): Korelasi AI Terhadap Motivasi Belajar Siswa. Jurnal Educatio FKIP UNMA, 9(3), 1464. https://doi.org/10.31949/educatio.v9i3.5761
ROZIQIN, A. (2024). Mengatur Adopsi Kecerdasan Buatan di Perguruan Tinggi.
Shin, Y., Seo, S., & Koo, C. (2025). Deep learning-based automated method for enhancing excavator activity recognition in far-field construction site surveillance videos. Automation in Construction, 173, 106099. https://doi.org/10.1016/j.autcon.2025.106099
Sucipto, S., Sukri, M., Patras, Y. E., & Novita, L. (2024). Tantangan Implementasi Kurikulum Merdeka di Sekolah Dasar: Systematic Literature Review. Kalam Cendekia Jurnal Ilmiah Kependidikan, 12(1). https://doi.org/10.20961/jkc.v12i1.84353
Supardi, S., & Hakim, M. V. F. (2021). Investigation the Digital Competence of Madrasah Teachers During the Covid-19 Pandemic. AL-ISHLAH Jurnal Pendidikan, 13(3), 2335. https://doi.org/10.35445/alishlah.v13i3.1246
Suwandi, Putri, R., & Sulastri. (2024). Inovasi Pendidikan dengan Menggunakan Model Deep Learning di Indonesia. Jurnal Pendidikan Kewarganegaraan Dan Politik, 2(2), 69–77. https://doi.org/10.61476/186hvh28
Talaei Khoei, T., Ould Slimane, H., & Kaabouch, N. (2023). Deep learning: Systematic review, models, challenges, and research directions. Neural Computing and Applications, 35(31), 23103–23124. https://doi.org/10.1007/s00521-023-08957-4
Ulul Albab, Fina Mawadah, Ferdian Nawawi, Alif Tito, & Ahmad Ta’rifin. (2023). Analisis Implementasi Kurikulum Merdeka Dalam Proses Pembelajaran Di Mts Ribattulmuta’alimin: Peluang Dan Tantangan. El-FAKHRU, 3(1), 1–19. https://doi.org/10.46870/elfakhru.v3i1.773
Wang, L., Zhang, L., Feng, L., Chen, T., & Qin, H. (2025). A novel deep transfer learning method based on explainable feature extraction and domain reconstruction. Neural Networks, 187, 107401. https://doi.org/10.1016/j.neunet.2025.107401
Widodo, Y. B., Sibuea, S., & Narji, M. (2024). Kecerdasan Buatan dalam Pendidikan: Meningkatkan Pembelajaran Personalisasi. Jurnal Teknologi Informatika Dan Komputer, 10(2), 602. https://doi.org/10.37012/jtik.v10i2.2324
Wu, B., Hou, L., Wang, S., Bu, X., & Xiang, C. (2025). Model reconstruction and update method for dynamic prediction of loader loading resistance using deep incremental learning. Engineering Applications of Artificial Intelligence, 153, 110910. https://doi.org/10.1016/j.engappai.2025.110910
Wu, X.-Y. (2024). Exploring the effects of digital technology on deep learning: A meta-analysis. Education and Information Technologies, 29(1), 425–458. https://doi.org/10.1007/s10639-023-12307-1
Yang, H., Wang, Z., Xu, M., Yang, D., & Zhao, Z. (2025). Improved deep transfer learning and transmission error based method for gearbox fault diagnosis with limited test samples. Mechanical Systems and Signal Processing, 230, 112593. https://doi.org/10.1016/j.ymssp.2025.112593
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