In-Depth Learning for Students with Deafness in Special Schools: A Literature Study
DOI:
https://doi.org/10.37680/qalamuna.v18i1.8419Keywords:
Deep Learning, Deaf Children, Inclusive Education, ConstructivismAbstract
The Deep Learning paradigm offers the potential to improve learning quality through conceptual understanding and metacognitive reflection. However, studies of its application in deaf education in Indonesia are very limited. This study aims to conduct a systematic literature review to map the opportunities, challenges, and conceptual models of deep learning relevant to deaf students. This study uses a qualitative literature review with a systematic integrative review design, which is analyzed using thematic content analysis across the stages of open coding, axial coding, and selective coding. The research identified three key opportunities for implementing Deep Learning: optimizing visual and kinesthetic modalities through interactive media, developing self-regulated learning through visual reflection, and strengthening social collaboration through community learning. These findings resulted in recommendations for project-based visual-reflective learning models, interactive media, and portfolio assessments. This research encourages teachers to design learning that stimulates deep thinking; schools to strengthen professional collaboration and provide visual learning environments; and the government to improve teacher training and inclusive technology facilities. Further research is recommended to test the effectiveness of this model through experiments and field implementation in various special needs schools (SLB).
References
Aftab, M. J., Amjad, F., Chaudhry, H., & Author, C. (2024). Exploring the Role of AI-Driven Speech Recognition Systems in Supporting Inclusive Education for Hearing Impaired Students. 5(3).
Ahmed, S., Rahman, S., & Kaiser, M. S. (2025). Advancing Personalized and Inclusive Education for Students with Disability Through Artificial Intelligence : Perspectives, Challenges, and Opportunities. 1–30.
Alam, I., Hameed, A., & Ziar, R. A. (2024). Exploring Sign Language Detection on Smartphones : A Systematic Review of Machine and Deep Learning Approaches. 2024. https://doi.org/10.1155/2024/1487500
Anwar, C., Komariyah, L., Aznem, A., Payung, L. T., & Heri, A. (n.d.). Evaluasi Kebijakan Pendidikan Inklusif di Indonesia : Pendekatan CIPP dan Perspektif Keadilan Sosial. 0738(3), 739–750.
Areeb, Q. M., Nadeem, M., Alroobaea, R., & Anwer, F. (2022). Helping Hearing-Impaired in Emergency Situations : A Deep Learning-Based Approach. 10.
Aulia, R., Bunga, R., Firdauzy, K., & Hunaida, L. (2025). Deep Learning Approach in Inclusive Islamic Education for Children with Special Needs.
Autonomous, E., & Nādu, T. (2022). Educating and communicating with deaf learners using the CNN-based Sign Language Prediction System, SreeVidyanikethan Department of Computer Science, Rathinam Technical Campus, Assistant Professor, Department of Psychology, Women. 14(02), 2624–2629. https://doi.org/10.9756/INT-JECSE/V14I2.245
Azzubair, M., Ridha, M. R., & Fathiyatul, N. (2025). Analisis Kebijakan Pendidikan Inklusif di Madrasah Melalui Studi Literatur. 02, 1388–1398.
Berrezueta-Guzmán, S., & Wagner, S. (2025). Virtual reality in sign language education : opportunities, challenges, and the road ahead. September 1–12. https://doi.org/10.3389/frvir.2025.1625910
Cha, Y., Ali, R., Lewis, J., & Büyük, O. (2024). Automation in Construction: Deep learning-based structural health monitoring. 161(August 2023). https://doi.org/10.1016/j.autcon.2024.105328
Chan, R. Y., Member, S., Man, C., Wong, V., & Yum, Y. E. N. N. A. (2023). Predicting Behavior Change in Students With Special Education Needs Using Multimodal Learning Analytics. 11(May).
Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., Jiang, Y., Liu, B., Liu, H., Li, X., & Lian, X. (2025). Deep learning-based software engineering : progress, challenges, and opportunities † (Vol. 68, Issue January).
Dignan, C., Perez, E., Ahmad, I., Huber, M., & Clark, A. (2022). An AI-based Approach for Improved Sign Language Recognition using Multiple Videos. Multimedia Tools and Applications, 34525–34546. https://doi.org/10.1007/s11042-021-11830-y
Filali, H., Riffi, J., Boulealam, C., Mahraz, M. A., & Tairi, H. (2022). Multimodal Emotional Classification Based on Meaningful Learning.
Fitas, R. (2025). Inclusive education with AI : supporting special needs and tackling language barriers. 5729–5757.
Gao, Y. (2025). Computers and Education : Artificial Intelligence, Deep learning-based strategies for evaluating and enhancing university teaching quality. 8(September 2024).
Isnaini, N., Safitri, W. A., & Fitria, Z. I. (2025). Pendidikan Inklusif sebagai Upaya Pemenuhan Hak-Hak Anak Berkebutuhan Khusus di Sekolah Reguler. 24(70). https://doi.org/10.30595/pssh.v24i.1591
Juntak, N. S., Rynaldi, A., Sukmawati, E., & Arafah, M. (2023). Mewujudkan Pendidikan Untuk Semua : Studi Implementasi Pendidikan Inklusif di Indonesia. 5(2), 205–214.
Kooli, C., & Chakraoui, R. (2025). AI-driven assistive technologies in inclusive education : benefits, challenges, and policy recommendations. Sustainable Futures, 10(July), 101042. https://doi.org/10.1016/j.sftr.2025.101042
Kovač, V. B., Nome, D. Ø., Jensen, A. R., Skreland, L. L., Nome, D. Ø., Jensen, A. R., Skreland, L. L., & The, M. (2023). The why, what, and how of deep learning : critical analysis and additional concerns. Education Inquiry, 00(00), 1–17. https://doi.org/10.1080/20004508.2023.2194502
Kumar, R. M. S., & Biji, C. L. (2024). Explainable Machine Learning Prediction for the Academic Performance of Deaf Scholars. IEEE Access, 12(January), 23595–23612. https://doi.org/10.1109/ACCESS.2024.3363634
Manoharan, A. (2024). Multimodal Engagement Recognition From Image Traits Using Deep Learning Techniques. IEEE Access, 12(January), 25228–25244. https://doi.org/10.1109/ACCESS.2024.3353053
Mcleod, B. S. (2024). Expository Teaching : Ausubel's Theory Of Learning. 1–6.
Mere, K., & Malang, U. W. (2025). JKIP : Jurnal Kajian Ilmu Pendidikan Strategies for Understanding and Implementing Deep Learning Approaches to Improve Learning Quality at Frater Maumere Junior High School Strategi Memahami dan Mengimplementasikan Pendekatan Deep Learning untuk Meningkatkan Kualitas Pembelajaran di Tingkat SMPK Frater Maumere. 6(4), 1843–1849.
Muhammad, J., Liu, W., Yu, Z., Kamran, M., Aminu, S., Uwaisu, A., Wang, C., & Li, J. (2023). International Journal of Applied Earth Observation and Geoinformation Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing : A survey. International Journal of Applied Earth Observation and Geoinformation, 121(May), 103365. https://doi.org/10.1016/j.jag.2023.103365
Mulyani, M., Widaningsih, S., Wiyati, R., & Novianti, A. (2025). Sosialisasi Proses Implementasi Deep Learning dalam Pembelajaran Bahasa : Mewujudkan Pengalaman belajar yang. 4(3), 699–708. https://doi.org/10.60004/komunita.v4i3.246
Nabijonovna, A. N. (2025). Prospects for The Use of Artificial Intelligence Technologies in The Inclusive Education Process. 68, 68–70. https://doi.org/10.55640/eijp-05-04-16
Nomor, V., Hatima, Y., & Saputra, E. E. (2025). Transformasi Pembelajaran Bahasa Indonesia di Sekolah Dasar melalui Pendekatan Deep Learning. 1, 46–57.
Nurfadhilah, A. A., Astutiningsih, F., & Lukitoaji, B. D. (2025). Analisis Penerapan Pendidikan Inklusif terhadap Akses Kesetaraan Siswa. 1(1).
Nurmala, D., Muslim, U., Medan, N., Dewanti, R., & Jakarta, U. N. (2024). Jurnal PEBSAS Volume 2 No 3 Tahun 2024 ISSN : 3025-0463 PEBSAS : JURNAL PENDIDIKAN BAHASA DAN SASTRA Volume 2 No 3 Tahun 2024 Enhancing English Language Learning through Deep Learning Approaches in MTS Al-Amin Kampung Pajak. 2(3), 46–55.
Patel, P., Pampaniya, S., Ghosh, A., Raj, R., Deepa, K., & Kandasamy, S. (2025). Enhancing Accessibility Through Machine Learning : A Review on Visual and Hearing Impairment Technologies. IEEE Access, 13(February), 33286–33307. https://doi.org/10.1109/ACCESS.2025.3539081
Pathirana, A., Rajakaruna, D. K., Kasthurirathna, D., & Atukorale, A. (2024). Journal of Future Artificial Intelligence: A Reinforcement Learning-Based Approach for Promoting Mental Health Using Multimodal Emotion Recognition.
Purwanto, J., Faizah, U., Rifki, I., & Permataningtyas, D. (2025). Pengembangan Model Pembelajaran Berbasis Deep Learning untuk Peningkatan Keterampilan Berbicara Peserta Didik SMP Muhammadiyah Purworejo. 4(April).
Rilci, A., & Nugraha, B. T. (2024). Pendidikan Inklusif : Mengakui Keberagaman dan Membangun Kesetaraan. 01(02), 41–43.
Rohalia, Z., Guru, P., & Ibtidaiyah, M. (2025). Strategi pendidikan inklusif dan penguatan mutu dalam mewujudkan kesetaraan pendidikan nasional. 1(1), 31–38.
Sathishkumar, V. E., Cho, J., & Subramanian, M. (2023). Forest fire and smoke detection using deep learning ‑ based learning without forgetting. Fire Ecology. https://doi.org/10.1186/s42408-022-00165-0
Sharifani, K., & Amini, M. (2023). Machine Learning and Deep Learning : A Review of Methods and Applications. 10(07).
Shen, Z., & Zhao, S. (2022). Legal Instructional Design by Deep Learning Theory Under the Background of Educational Psychology. 13(July), 1–10. https://doi.org/10.3389/fpsyg.2022.917174
Shlezinger, N., Eldar, Y. C., & Boyd, S. P. (2022). Model-Based Deep Learning : On the Intersection of Deep Learning and Optimization. IEEE Access, 10(November), 115384–115398. https://doi.org/10.1109/ACCESS.2022.3218802
Weng, C., Chen, C., & Ai, X. (2023). A pedagogical study on promoting students ’ deep learning through design-based learning. International Journal of Technology and Design Education, 33(4), 1653–1674. https://doi.org/10.1007/s10798-022-09789-4
Zaineldin, H., Gamel, S. A., Talaat, F. M., & Aljohani, M. (2024). Silent no more : a comprehensive review of artificial intelligence, deep learning, and machine learning. In Artificial Intelligence Review (Vol. 57, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10462-024-10816-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ila Kamila, Oom Sitti Homdijah, Budi Susetyo, Iding Tarsidi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who submit manuscript retain its copyright and grant publisher right of first publication licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) that allows others to access (search, read, download, and cite), share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon any material) the work for any lawful purpose, even commercially with an acknowledgement of the work's authorship and initial publication in Qalamuna: Jurnal Pendidikan, Sosial, dan Agama.






