(Institut Teknologi Sepuluh Nopember, Indonesia)(2) Rahmah Yasinta Rangkuti
(Institut Teknologi Sepuluh Nopember, Indonesia)(3) Abdul Karim
(Hallym University, Korea, Republic of)(4) Thosporn Sangsawang
(Rajamangala University of Technology Thanyaburi, Thailand)*corresponding author
AbstractThe prevalence of dyslexia, a common neurodevelopmental learning disorder, poses ongoing challenges for early detection and intervention. With the advancement of artificial intelligence (AI) technologies in the fields of healthcare and education, AI has emerged as a promising tool for supporting dyslexia screening and diagnosis. This systematic review aimed to identify recent developments in AI applications for dyslexia detection, focusing on the methods used, types of algorithms, datasets, and their performance outcomes. A comprehensive literature search was conducted in 2025 across databases including ScienceDirect, IEEE Xplore, and PubMed using a combination of relevant MeSH terms. The article selection process followed the PRISMA guidelines, resulting in the inclusion of 31 eligible studies. Data were extracted on AI approaches, algorithm types, dataset characteristics, and key performance metrics. The results revealed that machine learning (ML) was the most widely applied method (58.06%), followed by multi-method (22.58%), deep learning (16.13%), and large language models (3.23%). Among the ML algorithms, Random Forest and Decision Tree were the most commonly used due to their robustness and performance on structured datasets. In the deep learning category, CNN were the most frequently used models, especially for image-based and sequential input data. The datasets varied widely, including digital cognitive tasks, EEG, MRI, handwriting, and eye-tracking data, with several studies employing multimodal combinations. Ensemble and hybrid models demonstrated superior performance, with some achieving accuracy rates exceeding 98%. This review highlights that AI, particularly ML and multimodal ensemble methods, holds strong potential for improving the accuracy, scalability, and accessibility of dyslexia detection. Future research should prioritize large-scale, multimodal datasets, interpretable models, and adaptive learning systems to enhance real-world implementation.
KeywordsDyslexia Detection; Artificial Intelligence; Machine Learning; Deep Learning; Multimodal Data
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DOIhttps://doi.org/10.31763/ijrcs.v5i3.2057 |
Article metrics10.31763/ijrcs.v5i3.2057 Abstract views : 8 | PDF views : 11 |
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References
[1] S. K. Kim, “Recent update on reading disability (dyslexia) focused on neurobiology,” Clinical and Experimental Pediatrics, vol. 64, no. 10, pp. 497-503, 2021, https://doi.org/10.3345/cep.2020.01543.
[2] J. G. Elliott and E. L. Grigorenko, “Dyslexia in the twenty-first century: a commentary on the IDA definition of dyslexia,” Annals of Dyslexia, vol. 74, pp. 363-377, 2024, https://doi.org/10.1007/s11881-024-00311-0.
[3] L. M. D. Archibald, “On the many terms for developmental language and learning impairments,” Discover education, vol. 3, no. 33, 2024, https://doi.org/10.1007/s44217-024-00112-y.
[4] J. Miciak and J. M. Fletcher, “The Critical Role of Instructional Response for Identifying Dyslexia and Other Learning Disabilities,” Journal of Learning Disabilities, vol. 53, no. 5, pp. 343-353, 2020, https://doi.org/10.1177/0022219420906801.
[5] M. L.-Zamora, N. P.-Gozalbo, I. L.-C. García, and A. C.-Villagrasa, “Linguistic and Cognitive Abilities in Children with Dyslexia: A Comparative Analysis,” European Journal of Investigation in Health, Psychology and Education, vol. 15, no. 3, p. 37, 2025, https://doi.org/10.3390/ejihpe15030037.
[6] J. Schwarz, M. Lizarazu, M. Lallier, and A. K.-Gray, “Phonological deficits in dyslexia impede lexical processing of spoken words: Linking behavioural and MEG data,” Cortex, vol. 171, pp. 204-222, 2024, https://doi.org/10.1016/j.cortex.2023.10.003.
[7] M. Wolf et al., “Towards a dynamic, comprehensive conceptualization of dyslexia,” Annals of Dyslexia, vol. 74, pp. 303-324, 2024, https://doi.org/10.1007/s11881-023-00297-1.
[8] D. P. R. Chieffo et al., “Specific Learning Disorders (SLD) and Behavior Impairment: Comorbidity or Specific Profile?,” Children, vol. 10, no. 8, p. 1356, 2023, https://doi.org/10.3390/children10081356.
[9] A. F. Aldakhil, M. T. Ebrahim, and H. F. Gadelrab, “Diagnostic survey of dyslexia and accompanying behavioral indicators in primary school students in Saudi Arabia,” Research in Developmental Disabilities, vol. 134, p. 104424, 2023, https://doi.org/10.1016/j.ridd.2023.104424.
[10] M. E. Uzun, Y. Ko?an, and H. ?irin, “Abuse and Neglect of Children With Specific Learning Disorders in Türkiye: A Case–Control Study,” Clinical psychology & psychotherapy/Clinical psychology and psychotherapy, vol. 31, no. 3, p. e2986, 2024, https://doi.org/10.1002/cpp.2986.
[11] W. Feng, R. Chotipanvithayakul, and H. Liu, “Prevalence of dyslexia related to mental health problems and character strengths among primary school students in northwest China,” Australian Journal of Psychology, vol. 76, no. 1, 2024, https://doi.org/10.1080/00049530.2024.2399114.
[12] N. S. Adi, A. Othman, H. S. Kuay, and Q. M. Mustafa, “A study on the psychological functioning of children with specific learning difficulties and typically developing children,” BMC Psychology, vol. 12, no. 1, 2024, https://doi.org/10.1186/s40359-024-02151-4.
[13] C. Casalini and C. Pecini, “Telerehabilitation of Developmental Dyslexia: Critical Considerations on Intervention Methods and Their Effectiveness,” Brain Sciences, vol. 14, no. 8, p. 793, 2024, https://doi.org/10.3390/brainsci14080793.
[14] Y. Alkhurayyif and A. R. W. Sait, “A Review of Artificial Intelligence-Based Dyslexia Detection Techniques,” Diagnostics, vol. 14, no. 21, p. 2362, 2024, https://doi.org/10.3390/diagnostics14212362.
[15] Z. Li, X. Niu, P. C. M. Wong, H. Zhang, and L. Wang, “Factors influencing timely diagnosis of autism in China: an application of Andersen’s behavioral model of health services use,” BMC Psychiatry, vol. 25, no. 1, 2025, https://doi.org/10.1186/s12888-025-06590-0.
[16] Z. Gomolka, E. Zeslawska, B. Czuba, and Y. Kondratenko, “Diagnosing Dyslexia in Early School-Aged Children Using the LSTM Network and Eye Tracking Technology,” Applied Sciences, vol. 14, no. 17, p. 8004, 2024, https://doi.org/10.3390/app14178004.
[17] N. Mather and D. Schneider, “The Use of Cognitive Tests in the Assessment of Dyslexia,” Journal of Intelligence, vol. 11, no. 5, p. 79, 2023, https://doi.org/10.3390/jintelligence11050079.
[18] S. Bhushan, S. Arunkumar, T. A. E. Eisa, M. Nasser, A. K. Singh, and P. Kumar, “AI-Enhanced Dyscalculia Screening: A Survey of Methods and Applications for Children,” Diagnostics, vol. 14, no. 13, p. 1441, 2024, https://doi.org/10.3390/diagnostics14131441.
[19] J. R. Yap, T. Aruthanan and M. Chin, “Artificial Intelligence in Dyslexia Research and Education: A Scoping Review,” IEEE Access, vol. 13, pp. 7123-7134, 2025, https://doi.org/10.1109/access.2025.3526189.
[20] A. Zingoni, J. Taborri, and G. Calabrò, “A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies,” Scientific Reports, vol. 14, no. 1, p. 273, 2024, https://doi.org/10.1038/s41598-023-50879-7.
[21] K. Dabaghi, Stefano D’Urso, and F. Sciarrone, “Artificial Intelligence and Learning of Students with Dyslexia: A Brief Review,” Lecture notes in computer science, pp. 155-169, 2024, https://doi.org/10.1007/978-981-97-4243-1_13.
[22] S. Rangasrinivasan, M. S. Sumi Suresh, A. Olszewski, S. Setlur, B. Jayaraman, and V. Govindaraju, “AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia,” SN Computer Science, vol. 6, no. 5, 2025, https://doi.org/10.1007/s42979-025-03927-0.
[23] J. Sedmidubsky, N. Dostalova, R. Svaricek, and W. Culemann, “ETDD70: Eye-Tracking Dataset for Classification of Dyslexia Using AI-Based Methods,” Lecture Notes in Computer Science, pp. 34-48, 2024, https://doi.org/10.1007/978-3-031-75823-2_3.
[24] M. Zaree, M. Mohebbi, and R. Rostami, “An ensemble-based Machine learning technique for dyslexia detection during a visual continuous performance task,” Biomedical Signal Processing and Control, vol. 86, p. 105224, 2023, https://doi.org/10.1016/j.bspc.2023.105224.
[25] E. I. Toki, “Using Eye-Tracking to Assess Dyslexia: A Systematic Review of Emerging Evidence,” Education Sciences, vol. 14, no. 11, p. 1256, 2024, https://doi.org/10.3390/educsci14111256.
[26] G. A. Bäck, E. Lindeblad, C. Elmqvist, and I. Svensson, “Dyslexic students’ experiences in using assistive technology to support written language skills: a five-year follow-up,” Disability and Rehabilitation: Assistive Technology, vol. 19, no. 4, pp. 1217-1227, 2023, https://doi.org/10.1080/17483107.2022.2161647.
[27] H. Taylor and M. D. Vestergaard, “Developmental Dyslexia: Disorder or Specialization in Exploration?,” Frontiers in Psychology, vol. 13, 2022, https://doi.org/10.3389/fpsyg.2022.889245.
[28] R. Wang and H.-Y. Bi, “A predictive model for chinese children with developmental dyslexia—Based on a genetic algorithm optimized back-propagation neural network,” Expert Systems with Applications, vol. 187, p. 115949, 2022, https://doi.org/10.1016/j.eswa.2021.115949.
[29] A. Alrubaian, “Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis,” Clinical Psychology & Psychotherapy, vol. 32, no. 3, p. e70077, 2025, https://doi.org/10.1002/cpp.70077.
[30] N. D. Alqahtani, B. Alzahrani, and M. S. Ramzan, “Deep Learning Applications for Dyslexia Prediction,” Applied Sciences, vol. 13, no. 5, p. 2804, 2023, https://doi.org/10.3390/app13052804.
[31] O. L. Usman, R. C. Muniyandi, K. Omar, and M. Mohamad, “Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges,” IEEE Access, vol. 9, pp. 36879-36897, 2021, https://doi.org/10.1109/access.2021.3062709.
[32] S. M. H. Motlagh, M. H. Rezvani, and M. Khounsiavash, “AI methods for personality traits recognition: A systematic review,” Neurocomputing, vol. 640, p. 130301, 2025, https://doi.org/10.1016/j.neucom.2025.130301.
[33] A. Rana, A. Dumka, R. Singh, M. K. Panda, and N. Priyadarshi, “A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives,” Diagnostics, vol. 12, no. 11, p. 2708, 2022, https://doi.org/10.3390/diagnostics12112708.
[34] E. M. G. Younis, S. Mohsen, E. H. Houssein, and O. Ali S. Ibrahim, “Machine learning for human emotion recognition: a comprehensive review,” Neural computing & applications, vol. 36, pp. 8901-8947, 2024, https://doi.org/10.1007/s00521-024-09426-2.
[35] A. R. Dargazany, P. Stegagno, and K. Mankodiya, “WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics-Concept, Literature, and Future,” Mobile Information Systems, vol. 2018, no. 1, pp. 1-20, 2018, https://doi.org/10.1155/2018/8125126.
[36] A. Farizal, A. D. Wibawa, Y. Pamungkas, M. Pratiwi and A. Mas, “Classifying Known/Unknown Information in The Brain using Electroencephalography (EEG) Signal Analysis,” 2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), pp. 362-367, 2022, https://doi.org/10.1109/EECCIS54468.2022.9902928.
[37] B. Martins, I. A. B. Verrone, M. M. I. Sakamoto, M. Y. Baba, M. E. Yvata, K. Lukasova, and M. P. Nucci, “Resting-State Functional MRI in Dyslexia: A Systematic Review,” Biomedicines, vol. 13, no. 5, p. 1210, 2025, https://doi.org/10.3390/biomedicines13051210.
[38] M. T. Sqalli, B. Aslonov, M. Gafurov, N. Mukhammadiev, and Y. S. Houssaini, “Eye tracking technology in medical practice: a perspective on its diverse applications,” Frontiers in medical technology, vol. 5, 2023, https://doi.org/10.3389/fmedt.2023.1253001.
[39] A. Shahini et al., “A systematic review for artificial intelligence-driven assistive technologies to support children with neurodevelopmental disorders,” Information Fusion, vol. 124, p. 103441, 2025, https://doi.org/10.1016/j.inffus.2025.103441.
[40] S. Zahia, B. G.-Zapirain, I. Saralegui, and B. F.-Ruanova, “Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging,” Computer Methods and Programs in Biomedicine, vol. 197, p. 105726, 2020, https://doi.org/10.1016/j.cmpb.2020.105726.
[41] D. Sobnath, T. Kaduk, I. U. Rehman, and O. Isiaq, “Feature Selection for UK Disabled Students’ Engagement Post Higher Education: A Machine Learning Approach for a Predictive Employment Model,” IEEE Access, vol. 8, pp. 159530-159541, 2020, https://doi.org/10.1109/ACCESS.2020.3018663.
[42] L. Rello, R. B.-Yates, A. Ali, J. P. Bigham, and M. Serra, “Predicting risk of dyslexia with an online gamified test,” PLOS One, vol. 15, no. 12, p. e0241687, 2020, https://doi.org/10.1371/journal.pone.0241687.
[43] B. Nerušil, J. Polec, J. Škunda, and J. Ka?ur, “Eye tracking based dyslexia detection using a holistic approach,” Scientific Reports, vol. 11, p. 15687, 2021, https://doi.org/10.1038/s41598-021-95275-1.
[44] O. L. Usman, Ravie C. Muniyandi, K. Omar, and M. Mohamad, “Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism,” PloS One, vol. 16, no. 2, p. e0245579, 2021, https://doi.org/10.1371/journal.pone.0245579.
[45] G. L. Bosco, G. Pilato, and D. Schicchi, “DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages,” Array, vol. 12, p. 100097, 2021, https://doi.org/10.1016/j.array.2021.100097.
[46] A.-C. Paola et al., “GlyphReader App: A support game for the application of the Orton- Gillingham Method with DataMining Techniques.,” Procedia Computer Science, vol. 191, pp. 373-378, 2021, https://doi.org/10.1016/j.procs.2021.07.071.
[47] P. Raatikainen, J. Hautala, O. Loberg, T. Kärkkäinen, P. Leppänen, and P. Nieminen, “Detection of developmental dyslexia with machine learning using eye movement data,” Array, vol. 12, p. 100087, 2021, https://doi.org/10.1016/j.array.2021.100087.
[48] G. Singer, M. Golan, R. Shiff and D. Kleper, “Evaluating the Effectiveness of Accommodations Given to Students With Learning Impairments: Ordinal and Interpretable Machine-Learning-Based Methodology,” IEEE Transactions on Learning Technologies, vol. 15, no. 6, pp. 736-746, 2022, https://doi.org/10.1109/tlt.2022.3214537.
[49] N. J. G.-Molina, A. Ortiz, F. J. M.-Murcia, M. A. Formoso, and A. Giménez, “Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis,” Knowledge-Based Systems, vol. 240, p. 108098, 2022, https://doi.org/10.1016/j.knosys.2021.108098.
[50] J. L. Zeema and F. Xavier, “Evolving Optimized Neutrosophic C means clustering using Behavioral Inspiration of Artificial Bacterial Foraging (ONCMC-ABF) in the Prediction of Dyslexia,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1748-1754, 2022, https://doi.org/10.1016/j.jksuci.2019.09.008.
[51] S. Kaisar and A. Chowdhury, “Integrating oversampling and ensemble-based machine learning techniques for an imbalanced dataset in dyslexia screening tests,” ICT Express, vol. 8, no. 4, pp. 563-568, 2022, https://doi.org/10.1016/j.icte.2022.02.011.
[52] N. Deveau, P. Washington, E. Leblanc, A. Husic, K. Dunlap, Y. Penev, A. Kline, O. C. Mutlu, and D. P. Wall, “Machine learning models using mobile game play accurately classify children with autism,” Intelligence-Based Medicine, vol. 6, p. 100057, 2022, https://doi.org/10.1016/j.ibmed.2022.100057.
[53] D. Carioti et al., “The ReadFree tool for the identification of poor readers: a validation study based on a machine learning approach in monolingual and minority-language children,” Annals of Dyslexia, vol. 73, pp. 356-392, 2023, https://doi.org/10.1007/s11881-023-00287-3.
[54] S. Parmar and C. Paunwala, “A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals,” Healthcare Analytics, vol. 3, p. 100194, 2023, https://doi.org/10.1016/j.health.2023.100194.
[55] F. Joshi, J. Z. Wang, K. I. Vaden, and M. A. Eckert, “Deep learning classification of reading disability with regional brain volume features,” NeuroImage, vol. 273, p. 120075, 2023, https://doi.org/10.1016/j.neuroimage.2023.120075.
[56] J. Kunhoth, S. A. Maadeed, M. Saleh, and Y. Akbari, “Exploration and analysis of On-Surface and In-Air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods,” Biomedical Signal Processing and Control, vol. 83, p. 104715, 2023, https://doi.org/10.1016/j.bspc.2023.104715.
[57] M. Orsoni et al., “Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile,” Heliyon, vol. 9, no. 3, p. e14506, 2023, https://doi.org/10.1016/j.heliyon.2023.e14506.
[58] Y. K. Meena, H. Cecotti, B. Bhushan, A. Dutta and G. Prasad, “Detection of Dyslexic Children Using Machine Learning and Multimodal Hindi Language Eye-Gaze-Assisted Learning System,” IEEE Transactions on Human-Machine Systems, vol. 53, no. 1, pp. 122-131, 2023, https://doi.org/10.1109/thms.2022.3221848.
[59] I. A. Vajs, G. S. Kvascev, T. M. Papic, and M. M. Jankovic, “Eye-Tracking Image Encoding: Autoencoders for the Crossing of Language Boundaries in Developmental Dyslexia Detection,” IEEE Access, vol. 11, pp. 3024-3033, 2023, https://doi.org/10.1109/access.2023.3234438.
[60] G. Seshadri, B. K. Singh, and R. B. Pachori, “EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task,” IEEE transactions on neural systems and rehabilitation engineering, vol. 31, pp. 4672-4682, 2023, https://doi.org/10.1109/tnsre.2023.3335806.
[61] V. Shravya, Y. Revilla, S. Neha, and M. Supriya, “Air writing with Effective Communication Enhancement for Dyslexic Learners,” Procedia Computer Science, vol. 235, pp. 2056-2068, 2024, https://doi.org/10.1016/j.procs.2024.04.195.
[62] T. Zaibi and H. Bezine, “Early Detection of Learning Disabilities through Handwriting Analysis and Machine Learning,” Procedia Computer Science, vol. 246, pp. 3702-3712, 2024, https://doi.org/10.1016/j.procs.2024.09.186.
[63] D. C.-Barnes, N. J. G.-Molina, M. A. Formoso, A. Ortiz, P. Figueiredo, and J. L. Luque, “Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis,” Biocybernetics and Biomedical Engineering, vol. 44, no. 4, pp. 814-823, 2024, https://doi.org/10.1016/j.bbe.2024.09.003.
[64] F. Sbiaa, S. Kotel, R. Mghirbi, and A. G. Blaeich, “Revolutionizing Dyslexia Diagnosis: An Intelligent Model Featuring Machine Learning and Fuzzyfication,” Procedia Computer Science, vol. 246, pp. 3624-3633, 2024, https://doi.org/10.1016/j.procs.2024.09.195.
[65] A. Remadi, K. E. Hage, Y. Hobeika, and F. Bugiotti, “To prompt or not to prompt: Navigating the use of large language models for integrating and modeling heterogeneous data,” Data & knowledge engineering, vol. 152, p. 102313, 2024, https://doi.org/10.1016/j.datak.2024.102313.
[66] R. Vaitheeshwari et al., “Dyslexia Analysis and Diagnosis Based on Eye Movement,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 4109-4119, 2024, https://doi.org/10.1109/tnsre.2024.3496087.
[67] K. Gasmi, L. Ben Ammar, M. Krichen, M. A. Alamro, A. Mihoub and M. Mrabet, “Optimal Ensemble Learning Model for Dyslexia Prediction Based on an Adaptive Genetic Algorithm,” IEEE Access, vol. 12, pp. 64754-64764, 2024, https://doi.org/10.1109/access.2024.3395803.
[68] S. Sellamuthu and S. Rose, “Enhanced Special Needs Assessment: A Multimodal Approach for Autism Prediction,” IEEE Access, vol. 12, pp. 121688-121699, 2024, https://doi.org/10.1109/access.2024.3453440.
[69] Y. Alkhurayyif and A. Rahaman Wahab Sait, “Multi-Modal Dyslexia Detection Model via SWIN Transformer With Closed-Form Continuous Time Networks,” IEEE Access, vol. 12, pp. 127580-127591, 2024, https://doi.org/10.1109/access.2024.3454795.
[70] J. Xiong, H. Yin and M. Pan, “Application of Image Classification Based on Improved LSTM in Internet Reading Therapy Platform,” IEEE Access, vol. 12, pp. 1660-1671, 2024, https://doi.org/10.1109/access.2023.3347346.
[71] A. Farizal, A. D. Wibawa, D. P. Wulandari and Y. Pamungkas, “Investigation of Human Brain Waves (EEG) to Recognize Familiar and Unfamiliar Objects Based on Power Spectral Density Features,” 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 77-82, 2023, https://doi.org/10.1109/ISITIA59021.2023.10221052.
[72] M. Q. Pérez et al., “Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges,” Information Fusion, vol. 105, p. 102231, 2024, https://doi.org/10.1016/j.inffus.2024.102231.
[73] C. Halkiopoulos, E. Gkintoni, A. Aroutzidis, and H. Antonopoulou, “Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations,” Diagnostics, vol. 15, no. 4, p. 456, 2025, https://doi.org/10.3390/diagnostics15040456.
[74] L. Reinhart et al., “Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance,” Intelligence-Based Medicine, vol. 9, p. 100134, 2024, https://doi.org/10.1016/j.ibmed.2024.100134.
[75] O. Ali, P. A. Murray, M. Momin, Y. K. Dwivedi, and F. T. Malik, “The effects of artificial intelligence applications in educational settings: Challenges and strategies,” Technological Forecasting and Social Change, vol. 199, p. 123076, 2024, https://doi.org/10.1016/j.techfore.2023.123076.
[76] S. Ahmed, Md. S. Rahman, M. S. Kaiser, and A. S. M. S. Hosen, “Advancing Personalized and Inclusive Education for Students with Disability Through Artificial Intelligence: Perspectives, Challenges, and Opportunities,” Digital, vol. 5, no. 2, p. 11, 2025, https://doi.org/10.3390/digital5020011.
[77] T. Eche, L. H. Schwartz, F.-Z. Mokrane, and L. Dercle, “Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification,” Radiology: Artificial Intelligence, vol. 3, no. 6, pp. 1-9, 2021, https://doi.org/10.1148/ryai.2021210097.
[78] M. L. Lorusso and A. Toraldo, “Revisiting Multifactor Models of Dyslexia: Do They Fit Empirical Data and What Are Their Implications for Intervention?,” Brain Sciences, vol. 13, no. 2, p. 328, 2023, https://doi.org/10.3390/brainsci13020328.
[79] S. A. Hudu, A. S. Alshrari, E. J. I. A.-Shoura, A. Osman, and A. O. Jimoh, “A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis,” Interdisciplinary Perspectives on Infectious Diseases, vol. 2025, no. 1, pp. 1-14, 2025, https://doi.org/10.1155/ipid/6816002.
[80] M. Setzu, R. Guidotti, A. Monreale, F. Turini, D. Pedreschi, and F. Giannotti, “GLocalX - From Local to Global Explanations of Black Box AI Models,” Artificial Intelligence, vol. 294, p. 103457, 2021, https://doi.org/10.1016/j.artint.2021.103457.
[81] S. Ma, M. Zhang, W. Sun, Y. Gao, M. Jing, L. Gao, and Z. Wu, “Artificial intelligence and medical-engineering integration in diabetes management: Advances, opportunities, and challenges,” Healthcare and Rehabilitation, vol. 1, no. 1, p. 100006, 2025, https://doi.org/10.1016/j.hcr.2024.100006.
[82] T. Shaik, X. Tao, L. Li, H. Xie, and J. D. Velásquez, “A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom,” Information Fusion, vol. 102, p. 102040, 2024, https://doi.org/10.1016/j.inffus.2023.102040.
[83] L. Lim et al., “Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge,” EClinicalMedicine, vol. 81, p. 103112, 2025, https://doi.org/10.1016/j.eclinm.2025.103112.
[84] S. M. Goodman et al., “LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia,” The 24th International ACM SIGACCESS Conference on Computers and Accessibility, no. 24, pp. 1-18, 2022, https://doi.org/10.1145/3517428.3544819.
[85] J. Samuel, R. Kashyap, Y. Samuel, and A. Pelaez, “Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations,” International Journal of Information Management, vol. 65, p. 102505, 2022, https://doi.org/10.1016/j.ijinfomgt.2022.102505.
[86] A. Gertsovski, O. Guri, and M. Ahissar, “Reduced categorical learning of faces in dyslexia,” Cortex, vol. 173, pp. 80-95, 2024, https://doi.org/10.1016/j.cortex.2024.01.005.
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