Journal Cover – Impact in Computics

Impact in Computics

Peer-Reviewed • Open Access e-ISSN: 3122-7341

Submit Your Manuscript
Review Open Access 25 Views | 4 Downloads | 1–14 | PDF

AI-Based Tutoring Systems for Deaf and Hard-of-Hearing Learners: A Systematic Review of Computational Architectures, Adaptive Mechanisms, and Meta-Analysis of Learning Outcomes

Stefanie Amiruzzaman 1 ORCID , Md Amiruzzaman 2 ORCID
1 Department of Languages and Cultures, West Chester University, West Chester, PA 19383, USA
2 Department of Computer Science, West Chester University, West Chester, PA 19383, USA
DOI: https://doi.org/10.65500/computics-2026-003
Received: 7 February 2026 | Revised: 20 March 2026 | Accepted: 20 April 2026 | Published: 5 May 2026

Abstract

Children who are Deaf or hard of hearing (DHH) often experience educational challenges that may affect their academic achievement. Artificial intelligence (AI)-based tutoring systems have been proposed to address some of these challenges, but evidence regarding their effectiveness remains limited. This study presents a systematic review and meta-analysis of AI-based tutoring systems developed for DHH learners. The review followed PRISMA 2020 guidelines and searched five databases: Web of Science, Scopus, ERIC, PsycINFO, and IEEE Xplore. A total of 3,121 records were identified. After screening, 18 studies met the eligibility criteria for qualitative synthesis, and 15 studies were included in the meta-analysis. Eligible studies incorporated AI techniques such as sign language recognition, learner modelling, and adaptive instructional support. Non-adaptive digital interventions were excluded. A random-effects model was used to estimate the pooled effect size using Hedges' g, and heterogeneity was assessed using the I² statistic. The findings showed a moderate positive effect of AI-based tutoring systems on learning outcomes among DHH learners (g = 0.48, 95% CI: 0.35–0.61), with substantial heterogeneity across studies (I² = 67%). The strongest effects were observed in interventions that combined sign language interpretation with adaptive support, particularly in reading and speech development.

Keywords: Deaf and hard-of-hearing learners; AI-based tutoring systems; sign language recognition; adaptive learning systems; educational technology; learning analytics

Acknowledgment

We used generative AI to assist with editing for grammar and sentence structure. All content was reviewed and approved by the authors.

References

  1. K. Alasim, "Inclusion and d/Deaf and hard of hearing students: A qualitative meta-analysis," International Journal of Disability, Development and Education, vol. 70, no. 6, pp. 1120-1146, 2021. https://doi.org/10.1080/1034912X.2021.1931818
  2. M. Marschark, E. J. Machmer and C. Convertino, "Understanding language in the real world," The Oxford Handbook of Deaf Studies in Language, pp. 431-452, 2016. https://doi.org/10.1093/oxfordhb/9780190241414.013.29
  3. R. Nkambou, J. Bourdeau and R. Mizoguchi, Advances in intelligent tutoring systems, vol. 308, Springer, 2010. https://doi.org/10.1007/978-3-642-14363-2
  4. W. Ma, O. O. Adesope, J. C. Nesbit and Q. Liu, "Intelligent tutoring systems and learning outcomes: A meta-analysis," Journal of Educational Psychology, vol. 106, no. 4, pp. 901-918, 2014. https://doi.org/10.1037/a0037123
  5. M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsulaiman, M. A. Bencherif and M. A. Mekhtiche, "Hand gesture recognition for sign language using 3DCNN," IEEE Access, vol. 8, p. 79491–79509, 2020. https://doi.org/10.1109/ACCESS.2020.2990434
  6. K. Papadimitriou and G. Potamianos, "Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition," in Interspeech, 2023. https://doi.org/10.21437/Interspeech.2023-2198
  7. A. Barr, E. A. Feigenbaum and P. R. Cohen, The handbook of artificial intelligence, HeurisTech Press, 1981.
  8. A. T. Corbett and J. R. Anderson, "Knowledge tracing: Modeling the acquisition of procedural knowledge.," User modeling and user-adapted interaction, vol. 4, pp. 253-278, 1994. https://doi.org/10.1007/BF01099821
  9. M. Marschark and P. C. Hauser, Deaf cognition: Foundations and outcomes, Oxford University Press, 2008. https://doi.org/10.1093/acprof:oso/9780195368673.001.0001
  10. J. V. Kahn, "A comparison of sign and verbal language training with nonverbal retarded children," Journal of Speech, Language, and Hearing Research, vol. 24, no. 1, pp. 113-119, 1981. https://doi.org/10.1044/jshr.2401.113
  11. S. Pabis and J. Catalano, "Explicit and contextualized math vocabulary instruction with deaf and hard-of-hearing students," The Journal of Deaf Studies and Deaf Education, vol. 28, no. 4, pp. 424-425, 2023. https://doi.org/10.1093/deafed/enad012
  12. R. J. Hoffmeister, "A piece of the puzzle: ASL and reading comprehension in deaf children," in Language acquisition by eye, Psychology Press, 1999, pp. 143-163. https://doi.org/10.4324/9781410601766-11
  13. K. Pechilis and S. J. Raj, South Asian religions: Tradition and today, Routledge, 2012. https://doi.org/10.4324/9780203079935
  14. N. Pope, J. Kahila, J. Laru, H. Vartiainen, T. Roos and M. Tedre, "An educational tool for learning about social media tracking, profiling, and recommendation," in 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), 2024. https://doi.org/10.1109/ICALT61570.2024.00038
  15. K. Yin, A. Moryossef, J. Hochgesang, Y. Goldberg and M. Alikhani, "Including signed languages in natural language processing," in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021. https://doi.org/10.18653/v1/2021.acl-long.570
  16. R. Rastgoo, K. Kiani and S. Escalera, "Sign language recognition: A deep survey," Expert systems with applications, vol. 164, p. 113794, 2021. https://doi.org/10.1016/j.eswa.2020.113794
  17. N. Adaloglou, T. Chatzis, I. Papastratis, A. Stergioulas, G. T. Papadopoulos, V. Zacharopoulou, G. Xydopoulos, K. Atzakas, D. Papazachariou and P. Daras, "A comprehensive study on deep learning-based methods for sign language recognition," IEEE Transactions on Multimedia, vol. 24, pp. 1750-1762, 2021. https://doi.org/10.1109/TMM.2021.3070438
  18. N. C. Camgoz, O. Koller, S. Hadfield and R. Bowden, "Sign language transformers: Joint end-to-end sign language recognition and translation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020. https://doi.org/10.1109/CVPR42600.2020.01004
  19. L. Liu, Z. Yang, Y. Liu, X. Zhang and K. Yang, "A Sign Language Recognition Based on Optimized Transformer Target Detection Model," in International Conference on Multimedia Technology and Enhanced Learning, 2024. https://doi.org/10.1007/978-3-031-50580-5_16
  20. R. Rastgoo, K. Kiani and S. Escalera, "Multi-modal deep hand sign language recognition in still images using restricted Boltzmann machine," Entropy, vol. 20, no. 11, p. 809, 2018. https://doi.org/10.3390/e20110809
  21. K. Cagıltay, H. Cakır, N. Karasu, O. F. Islım and F. Cıcek, "Use of educational technology in special education: Perceptions of teachers," Participatory Educational Research, vol. 6, no. 2, pp. 189-205, 2019. https://doi.org/10.17275/per.19.21.6.2
  22. E. Holmer, M. Heimann and M. Rudner, "Computerized sign language-based literacy training for deaf and hard-of-hearing children," The Journal of Deaf Studies and Deaf Education, vol. 22, no. 4, pp. 404-421, 2017. https://academic.oup.com/jdsde/article/22/4/404/4098239
  23. H. Cheng, S. Chen, C. Perdriau, S. Mokkapati and Y. Huang, "LLM-powered AI tutors with personas for d/Deaf and hard-of-hearing online learners," arXiv preprint, vol. 1, 2024. https://doi.org/10.48550/arXiv.2411.09873
  24. J. L. Luckner, A. M. Sebald, J. Cooney, J. Young and S. G. Muir, "An examination of the evidence-based literacy research in deaf education," American Annals of the Deaf, vol. 150, no. 5, pp. 443-456, 2005. https://doi.org/10.1353/aad.2006.0008
  25. S. Steenbergen-Hu and H. Cooper, "A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning.," Journal of Educational Psychology, vol. 105, no. 4, pp. 970-987, 2013. https://doi.org/10.1037/a0032447
  26. P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Kuttler, M. Lewis, W. Yih, T. Rocktaschel, S. Riedel and D. Kiela, "Retrieval-augmented generation for knowledge-intensive NLP tasks," Advances in Neural Information Processing Systems, vol. 33, p. 9459–9474, 2020. https://doi.org/10.48550/arXiv.2005.11401
  27. L. Yan, L. Sha, L. Zhao, Y. Li, R. Martinez‐Maldonado, G. Chen, X. Li, Y. Jin and D. Gašević, "Practical and ethical challenges of large language models in education: A systematic scoping review," British Journal of Educational Technology, vol. 55, no. 1, pp. 90-112, 2023. https://doi.org/10.1111/bjet.13370
  28. J. P. T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. J. Page and V. A. Welch, Cochrane handbook for systematic reviews of interventions, Wiley, 2019. https://doi.org/10.1002/9781119536604
  29. M. Egger, G. D. Smith, M. Schneider and C. Minder, "Bias in meta-analysis detected by a simple, graphical test," BMJ, vol. 315, no. 7109, pp. 629-634, 1997. https://doi.org/10.1136/bmj.315.7109.629
  30. M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hrobjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting and D. Moher, "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews," BMJ, vol. 372, 2021. https://doi.org/10.1136/bmj.n71
  31. K. N. Alasim, "Recognition and comprehension of multiple-meaning words: Examining a vocabulary intervention with hard of hearing students," American Annals of the Deaf, vol. 166, no. 3, pp. 262-283, 2021. https://doi.org/10.1353/aad.2021.0030
  32. J. Cohen, Statistical power analysis for the behavioral sciences, Routledge, 1988. https://doi.org/10.4324/9780203771587
  33. R. Luckin, S. Puntambekar, P. Goodyear, B. L. Grabowski, J. Underwood and N. Winters, Handbook of Design in Educational Technology, Routledge, 2013. https://doi.org/10.4324/9780203075227
© 2026 by the authors. Published by Impaxon Publishing.
This article is licensed under the Creative Commons Attribution (CC BY) License .
You are free to share and adapt the material as long as appropriate credit is given.
Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of Impaxon Publishing or the journal editors. The publisher remains neutral with regard to jurisdictional claims in institutional affiliations.