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摘要
阅读障碍是一种神经发育性学习障碍，尽管患者智力正常，但该障碍会显著影响儿童的读写能力，且若未能及时识别，可能会导致长期的学业和心理社会后果。现有的阅读障碍筛查方法主要依赖于专家主导的评估，这种方法不仅耗时、主观性强，而且在非临床环境中难以大规模推广。 尽管近期研究已探索了利用人工智能（AI）方法检测阅读障碍，但许多研究仍局限于单模态数据、离线分析或非移动端实现，从而限制了其在早期筛查中的实际应用。 本研究旨在开发一款基于人工智能的移动应用程序，用于阅读障碍的早期检测，该程序通过循环神经网络（RNN）架构（特别是门控循环单元（GRU））利用序列文本和语音数据。 本研究采用研发（R&D）方法论，涵盖需求分析、系统设计、GRU模型训练、基于Flutter的移动应用程序开发，以及与RESTful后端和MySQL数据库的系统集成。 GRU模型基于预处理后的阅读文本和语音录音进行训练，以捕捉与阅读障碍相关阅读行为的时间模式。实验结果表明，所提出的模型在识别阅读障碍相关模式方面实现了可靠的分类性能，同时该移动应用程序成功提供了实时筛查结果，并维护了纵向评估记录。 研究结果表明，将轻量级序列深度学习模型集成到移动平台中，为阅读障碍的早期筛查提供了一种可扩展且易于使用的解决方案，支持家长和教育工作者在临床环境之外独立使用。
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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 16参考
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