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난독증은 지능이 정상임에도 불구하고 아동의 읽기 및 쓰기 능력에 상당한 영향을 미치는 신경발달성 학습 장애이며, 조기 발견이 지연될 경우 장기적인 학업적 및 심리사회적 결과를 초래할 수 있습니다. 기존의 난독증 선별 방법은 전문가 주도의 평가에 크게 의존하고 있는데, 이는 시간이 많이 소요되고 주관적이며 비임상 환경에서 확대 적용하기 어렵습니다. 최근 연구에서 난독증 탐지를 위한 인공지능(AI) 접근법을 탐구해 왔으나, 상당수는 단일 모달리티 데이터, 오프라인 분석 또는 비모바일 구현에 국한되어 있어 조기 선별 검사에 대한 실질적인 적용성이 제한적이다. 본 연구는 재귀 신경망(RNN) 아키텍처, 특히 게이트 재귀 유닛(GRU)을 통해 순차적 텍스트 및 음성 데이터를 활용하여 난독증 조기 탐지를 위한 AI 기반 모바일 애플리케이션을 개발하는 것을 목표로 하였다. 본 연구에서는 요구 사항 분석, 시스템 설계, GRU 모델 훈련, Flutter를 활용한 모바일 애플리케이션 개발, RESTful 백엔드 및 MySQL 데이터베이스와의 시스템 통합을 포괄하는 연구 개발(R&D) 방법론을 채택했다. GRU 모델은 난독증 관련 읽기 행동과 연관된 시간적 패턴을 포착하기 위해 전처리된 읽기 텍스트 및 음성 녹음 자료를 바탕으로 훈련되었다. 실험 결과에 따르면, 제안된 모델은 난독증 관련 패턴을 식별하는 데 있어 신뢰할 수 있는 분류 성능을 보였으며, 모바일 애플리케이션은 실시간 선별 검사 결과를 성공적으로 제공함과 동시에 종단적 평가 기록을 유지했다. 이번 연구 결과는 경량 순차적 딥러닝 모델을 모바일 플랫폼에 통합하는 것이 난독증 조기 선별을 위한 확장 가능하고 접근성이 높은 솔루션을 제공하며, 임상 환경 밖에서도 학부모와 교육자가 독립적으로 활용할 수 있음을 입증한다.
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Author Contributions
Muhamad Fathur Rahman: Conceptualization, Methodology, Software, Data Curation, Investigation, Writing – Original Draft, Writing – Review & Editing. Resmi Darni: Supervision, Resources, Validation, Writing – Review & Editing. Dony Novaliendry: Validation, Writing – Review & Editing. Khairi Budayawan: Supervision, Validation, Writing – Review & Editing. All authors have read and approved the final version of this manuscript.
Acknowledgments
The authors would like to thank all individuals, institutions, and external collaborators who provided invaluable guidance, technical support, and insights throughout the completion of this research. Their expertise significantly contributed to the rigor, quality, and practical relevance of this study.
View statement in PDF · PDF page 15Ethics Approval
This study did not involve direct experimental interventions on human or animal subjects. The research focused on application development and system testing. Data were collected through interviews with relevant participants after obtaining informed consent from parents or teachers. All data were processed anonymously to ensure privacy and confidentiality, and the study was conducted in accordance with applicable ethical research standards.
View statement in PDF · PDF page 15Informed Consent
Informed consent was obtained verbally from all participants involved in this study. For participants who were minors, consent was obtained from their parents, teachers, and the school authorities. All data collected was anonymized to protect the privacy and confidentiality of the participants.
View statement in PDF · PDF page 16Funding
The authors declare that this research was conducted independently and did not receive any external funding or financial support. All stages of the study, including system design, development, testing, and manuscript preparation, were carried out without external grants or sponsorships.
View statement in PDF · PDF page 16Data Availability
The data supporting the findings of this study are not publicly available due to privacy and confidentiality concerns related to the participants. Summary data are included in the manuscript, and additional information may be provided by the corresponding author upon reasonable request under strict confidentiality conditions.
View statement in PDF · PDF page 16Competing Interests
The authors confirm that there are no conflicts of interest, financial or otherwise, that could have influenced the research or the outcomes reported in this study.
View statement in PDF · PDF page 16Generative AI Disclosure
During the preparation of this manuscript, the author used generative AI and AI-assisted tools, including ChatGPT and Grammarly, to support language editing and proofreading. All content generated or assisted by these tools was carefully reviewed, revised, and validated by the author to ensure accuracy, originality, and academic integrity. The author takes full responsibility for the content of the manuscript and confirms that the use of these tools did not affect the study's scientific validity.
View statement in PDF · PDF page 16References
27 References[1] M. J. Snowling, C. Hulme, and K. Nation, “Defining and understanding dyslexia: Past, present and future,” Oxford Review of Education, vol. 46, no. 4, pp. 501–513, Aug. 2020, doi: https://doi.org/10.1080/03054985.2020.1765756
[2] Y. Alkhurayyif and A. R. W. Sait, “A review of artificial intelligence-based dyslexia detection techniques,” Diagnostics, vol. 14, no. 21, Art. no. 2362, 2024, doi: https://doi.org/10.3390/diagnostics14212362
[3] G. Swami and Y. K. M., “Early detection of dyslexia using multimodal analysis of behavioral, neurophysiological, and linguistic markers,” in Proc. Int. Conf. Future Technologies (INCOFT), 2025, pp. 338–345, doi: https://doi.org/10.5220/0013615600004664
[4] Y. Alkhurayyif and A. R. W. Sait, “Deep learning-driven dyslexia detection model using multi-modality data,” PeerJ Computer Science, vol. 10, pp. 1–22, 2024, doi: https://doi.org/10.7717/peerj-cs.2077.
[5] R. Kolinsky and M. Tossonian, “Phonological and orthographic processing in basic literacy adults and dyslexic children,” Reading and Writing, vol. 36, no. 7, pp. 1683–1706, Jul. 2023, doi: https://doi.org/10.1007/s11145-022-10347-6.
[6] M. Rauschenberger, R. Baeza-Yates, and L. Rello, “A universal screening tool for dyslexia by a web-game and machine learning,” Frontiers in Computer Science, vol. 3, Art. no. 628634, 2022, doi: https://doi.org/10.3389/fcomp.2021.628634
[7] S. Mohsen, “Recognition of human activity using GRU deep learning algorithm,” Multimedia Tools and Applications, vol. 82, no. 30, pp. 47733–47749, 2023, doi: https://doi.org/10.1007/s11042-023-15571-y
[8] X. Zhang, J. Yang, and Y. Liu, “A review on deep learning for intelligent speech recognition,” Neurocomputing, vol. 443, pp. 1–17, Jun. 2021, doi: https://doi.org/10.1016/j.neucom.2021.02.080
[9] Y. Alharbi and N. Alsubaie, “Deep recurrent neural network for sequence modeling: A review,” Expert Systems, vol. 39, no. 8, Art. no. e12939, 2022, doi: https://doi.org/10.1111/exsy.12939
[10] W. Yin, K. Kann, M. Yu, and H. Schütze, “Comparative study of CNN and RNN for natural language processing,” Neurocomputing, vol. 366, pp. 43–52, 2020, doi: https://doi.org/10.1016/j.neucom.2019.07.067
[11] K. Cho et al., “On the properties of neural machine translation: Encoder–decoder approaches,” Neural Computing and Applications, vol. 32, pp. 12265–12280, 2020, doi: https://doi.org/10.1007/s00521-020-04874-2
[12] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education—Where are the educators?,” International Journal of Educational Technology in Higher Education, vol. 17, pp. 1–27, 2020.
[13] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intelligence Review, vol. 53, pp. 5455–5516, 2020, doi: https://doi.org/10.1007/s10462-020-09825-6
[14] S. A. Kinari, N. Funabiki, S. T. Aung, K. H. Wai, M. Mentari, and P. Puspitaningayu, “An independent learning system for Flutter cross-platform mobile programming with code modification problems,” Information, vol. 15, no. 10, Art. no. 614, 2024, doi: https://doi.org/10.3390/info15100614
[15] V. Garousi, M. Felderer, and M. V. Mäntylä, “Guidelines for including grey literature and conducting multivocal literature reviews in software engineering,” Information and Software Technology, vol. 106, pp. 101–121, 2020, doi: https://doi.org/10.1016/j.infsof.2018.09.006
[16] M. Akour, M. Alenezi, and M. Alshraideh, “Software testing techniques: A systematic mapping study,” IEEE Access, vol. 8, pp. 168772–168799, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3023812
[17] A. I. Putri and Y. Syarif, “Implementation of gated recurrent unit, long short-term memory, and derivatives for gold price prediction,” vol. 2, pp. 68–80, 2025.
[18] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015, doi: https://doi.org/10.1016/j.neunet.2014.09.003
[19] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint, 2014. [Online]. Available: http://arxiv.org/abs/1412.3555.
[20] C. Liu and C. Chan, “Deep learning-based fall detection algorithm using ensemble model of coarse-fine CNN and GRU networks,” unpublished.
[21] R. Niu, L. Ni, and F. Zhu, “Emerging technologies and neuroscience-based approaches in dyslexia: A narrative review toward integrative and personalized solutions,” Frontiers in Human Neuroscience, vol. 19, 2025, doi: https://doi.org/10.3389/fnhum.2025.1683924
[22] D. Corrochano, E. Ferrari, M. A. López-Luengo, and V. Ortega-Quevedo, “Educational gardens and climate change education: An analysis of Spanish preservice teachers’ perceptions,” Education Sciences, vol. 12, no. 4, 2022, doi: https://doi.org/10.3390/educsci12040275
[23] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education—Where are the educators?,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, 2019, doi: https://doi.org/10.1186/s41239-019-0171-0
[24] S.-J. Kim, M.-W. Park, H.-J. Choi, and J.-H. Lee, “Mobile health applications powered by AI,” KAIST, South Korea, Dec. 2023.
[25] A. S. Irawan et al., “Beyond the interface: Benchmarking pediatric mobile health applications for monitoring child growth using the Mobile App Rating Scale,” Frontiers in Digital Health, vol. 7, pp. 1–13, 2025, doi: https://doi.org/10.3389/fdgth.2025.1621293
[26] O. Bulut and M. Beiting-Parrish, “The rise of artificial intelligence in educational measurement: Opportunities and ethical challenges,” Chinese/English Journal of Educational Measurement and Evaluation, vol. 5, no. 3, pp. 1–59, 2024, doi: https://doi.org/10.59863/miql7785
[27] M. Perkins, J. Roe, and L. Furze, “The AI assessment scale revisited: A framework for educational assessment,” arXiv preprint, 2024. [Online]. Available: http://arxiv.org/abs/2412.09029
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Muhamad Fathur Rahman, Resmi Darni, Dony Novaliendry, Khairi Budayawan