AI-Driven Personalized Learning Paths: Opportunities and Challenges for Inclusive Education
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Artificial Intelligence, Inclusive Education, Learning Analytics
Abstract
Background. This study addresses the growing integration of artificial intelligence (AI) in education, particularly its potential to design personalized learning paths that respond to diverse learner needs within inclusive education contexts. Despite increasing adoption, critical questions remain regarding equity, accessibility, and pedagogical effectiveness when AI-driven systems are implemented across heterogeneous student populations
Purpose. The primary objective of this research is to examine both the opportunities and challenges associated with AI-driven personalized learning paths in supporting inclusive education.
Method. The study employs a mixed-methods approach, combining a systematic literature review with qualitative analysis of selected empirical case studies from primary, secondary, and higher education settings. Data were analyzed thematically to identify patterns related to personalization mechanisms, learner inclusion, ethical considerations, and institutional readiness.
Results. The findings indicate that AI-driven personalization can enhance learner engagement, adaptive pacing, and differentiated instruction, particularly for students with diverse abilities and learning profiles. However, significant challenges persist, including algorithmic bias, data privacy concerns, unequal access to digital infrastructure, and limited teacher capacity to critically mediate AI-supported learning.
Conclusion. The study concludes that while AI-driven personalized learning paths hold substantial promise for advancing inclusive education, their effectiveness depends on transparent algorithm design, strong ethical governance, and sustained professional development for educators to ensure technology serves pedagogical and social inclusion goals rather than exacerbating existing inequalities.
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References
Abdellatief, M., & Alotaibi, R. S. (2026). From Intelligence to Trust: Evaluating AI-Powered Service Quality for User Satisfaction and Continuance in mHealth. Statistics, Optimization and Information Computing, 15(1), 295–310. https://doi.org/10.19139/soic-2310-5070-3032
Albahri, O. S., Khaleel, Y. L., Habeeb, M. A., Al-Obaidi, J. R., Albahri, A. S., Alamoodi, A. H., Mahmoud, M. A., & Sharaf, I. M. (2026). Evaluating Vegetable Nutrition for Dental Health Maintenance Using an Orthopair Fuzzy Z-Number Decision-Making Model. Cognitive Computation, 18(1). https://doi.org/10.1007/s12559-026-10545-2
Aleo, G., Pagnucci, N., Walsh, N., Moriarty, F., Giardulli, B., Koutra, K., Przy??cki, P., Fitzgerald, C., & Illing, J. (2026). Identifying core teamwork competencies for community-based health and social care professionals: an e-Delphi study. BMC Medical Education, 26(1). https://doi.org/10.1186/s12909-025-08478-9
Alshehri, F. A., Alamri, A., AlMoslemany, M. A., Temsah, A. A., Temsah, M.-H., & Alshehri, M. (2026). An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review. BMC Oral Health, 26(1). https://doi.org/10.1186/s12903-026-07807-8
Anees, W., Silva, R., Khan, A., Murray, J., Scavassini, L., Angelakopoulos, N., Napimoga, M. H., Porto, L., Abade, A., & Franco, A. (2026). Maxillary sinus classification for sex and age using 23 artificial intelligence architectures. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-36112-1
Ashour, A., & Elkhawaga, G. O. (2026). Innovations in geriatric care: leveraging technology to meet the needs of an aging world, a narrative review. Journal of the Egyptian Public Health Association, 101(1). https://doi.org/10.1186/s42506-025-00203-0
Barragán Giraldo, D. F., Espinosa-Vega, M. C., Munevar-Vargas, S. L., & Torres Serrano, J. M. (2026). Artificial Intelligence and Theological Experience: voices of Friars from Two Mendicant Orders. Cuestiones Teologicas, 53(119). https://doi.org/10.18566/cueteo.v53n119.a06
Bennett, J. R., Buxton, R. T., Caufield, J. H., Hanna, D. E. L., & Alamenciak, T. (2026). Assessing the effectiveness of ontology-grounded AI term extraction using OntoGPT for environmental evidence synthesis. Environmental Evidence, 15(1). https://doi.org/10.1186/s13750-026-00381-0
Boufrikha, W., Sallem, A., Laabidi, B., Mallek, R., Slama, N., Ben Youssef, S. B., Majdoub, A., & Boukhris, S. (2026). Evaluation of three artificial intelligence chatbots for generating clinical hematology multiple choice questions for medical students. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-36839-x
Capozzi Lupi, A., & Dailisan, D. (2026). Beyond the tax haven: a graph analysis of business attraction in Swiss municipalities. EPJ Data Science, 15(1). https://doi.org/10.1140/epjds/s13688-026-00619-4
Cong, C., Brownson-Smith, R., Milne-Ives, M., & Meinert, E. (2026). Assessment of mental and behavioural non-motor symptoms of Parkinson’s Disease using Artificial Intelligence (AI): a systematic review. Communications Medicine, 6(1). https://doi.org/10.1038/s43856-025-01304-9
Dundar Sari, M. B., & Sezer, B. (2026). Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam. BMC Medical Education, 26(1). https://doi.org/10.1186/s12909-026-08621-0
Esfandiari, A., Abedi Pahnehkolaei, S. M., & Nasiri, N. (2026). Complex-order Bat-inspired algorithm applied to neural network optimization for medical datasets. International Journal of Data Science and Analytics, 22(1). https://doi.org/10.1007/s41060-025-00972-z
Gupta, K., Latinovich, M. F., Latinovich, M. F., Singh, K., Patlas, M. N., & Jajodia, A. (2026). Generative Artificial Intelligence for Medical Image Creation in Health Professions Education: a Scoping Review. Journal of Medical Systems, 50(1). https://doi.org/10.1007/s10916-026-02350-z
Khotimah, K., Budiarto, M. K., Amarulloh, A., Diningrat, S. W. M., Carreza, A. N., & Son, J. H. (2026). Fostering Creativity Through Meta Virtual Project-Based Networked Learning: An In-Depth Examination. Electronic Journal of E-Learning, 24(1), 93–108. https://doi.org/10.34190/ejel.24.1.4302
Mahecha-Beltrán, G. A., & Pérez-Vargas, J. J. (2026). Experience of the sacred, spirituality, and meaning of life through the use of Artificial Intelligence. Cuestiones Teologicas, 53(119), 1–27. https://doi.org/10.18566/cueteo.v53n119.a04
Masud, S. B., Sozib, H. M., Mishu, K. P., Bellal, R. B., Ahmed, M. T., Jony, A. M., Tabassum, S., & Billah, M. M. U. A. M. S. (2026). AI-Driven Predictive Maintenance in Infrastructure and Facilities Management. EAI Endorsed Transactions on AI and Robotics, 5. https://doi.org/10.4108/airo.9975
Muda, N., & Sulaiman, M. H. (2026). Adapting quality function deployment to translate patient feedback into prioritized technical requirements for healthcare artificial intelligence. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-36550-x
Nasrollahizadeh, A., Rahmati, S., Chahkand, M. S. G., Moallem, F. E., Azarm, E., Dadkhah, P. A., Karimi, M., Amini-Salehi, E., & Karimi, M. A. (2026). Autophagy in Rheumatoid Arthritis: Molecular Mechanisms, Diagnostic Biomarkers, and Emerging Therapeutic Strategies. Inflammation, 49(1). https://doi.org/10.1007/s10753-025-02406-1
Pavlyuk, D., & Alomar, I. (2026). Artificial Intelligence Technologies for Aircraft Maintenance: A Systematic Literature Review. International Journal of Prognostics and Health Management, 17(1). https://doi.org/10.36001/ijphm.2026.v17i1.4567
Polyzou, M., & Baraliakos, X. (2026). Artificial Intelligence (AI) in rheumatology: a comparative evaluation of the ChatGPT and DeepSeek application. BMC Rheumatology, 10(1). https://doi.org/10.1186/s41927-026-00618-y
Rahimi Mamaghani, K. R., & Parvin, N. (2026). Cold sintering for in-space materials fabrication: a science, engineering, and future directions. Multiscale and Multidisciplinary Modeling, Experiments and Design, 9(1). https://doi.org/10.1007/s41939-026-01182-1
Sánchez, A., Herrera, L., Teixeira, A., Galvao Neto, M., Rodriguez, O., Brattain, L., Caycedo-Marulanda, A., Alvarez, J., & Aguirre, L. (2026). Artificial intelligence-based waste detection in robotic surgery: a pilot validation study of computer vision technology. Journal of Robotic Surgery, 20(1). https://doi.org/10.1007/s11701-026-03202-1
Sharma, G. D., Kharbanda, A., Parihar, J. S., Dawar, G., & Taheri, B. (2026). Navigating the robotics revolution: a review of research on service automation in shaping the future of tourism and hospitality. International Journal of Contemporary Hospitality Management, 38(13), 49–69. https://doi.org/10.1108/IJCHM-03-2025-0362
Smit, I., Adasme, M. F., Manners, E., Corbett, S., Bosc, N., Do, H.-M.-A., Leach, A. R., O’Boyle, N. M., & Zdrazil, B. (2026). Integrating artificial intelligence and manual curation to enhance bioassay annotations in ChEMBL. Journal of Cheminformatics, 18(1). https://doi.org/10.1186/s13321-026-01165-x
Tirkkonen, O., Tiensuu, H., Väyrynen, E., Suutala, J., Vuollo, V., Laitala, M.-L., & Karki, S. (2026). An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study. BMC Oral Health, 26(1). https://doi.org/10.1186/s12903-026-07660-9
Yoshida, M., Duangchinda, V., & Ruangrit, N. (2026). AI-Supported Learning in Online Discussion Forums: A Scoping Review. Electronic Journal of E-Learning, 24(1), 19–35. https://doi.org/10.34190/ejel.24.1.4409
Zayier, Y., Yalikun, Y., Cheng, Y., & Yang, D. (2026). Integrating physics and machine learning for unified seismic forward modeling and reservoir property inversion. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-36501-6
