Review Article
The Inclusive Algorithm: A Systematic Review of AI and Machine Learning in Supporting Learners with Disabilities
Habtamu Debasu Belay*
,
Simachew Alamneh
Issue:
Volume 2, Issue 1, March 2026
Pages:
1-14
Received:
12 January 2026
Accepted:
31 January 2026
Published:
11 February 2026
Abstract: Inclusive education has emerged as a global priority, emphasizing equitable access, participation, and learning outcomes for learners with disabilities. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have introduced new opportunities to address diverse learner needs through adaptive, personalized, and accessible educational technologies. This systematic review synthesizes empirical evidence on the effectiveness of AI- and ML-based interventions for learners with disabilities across educational contexts. Guided by PRISMA 2020 standards, a comprehensive literature search was conducted across Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar, identifying 245 peer-reviewed studies published between 2015 and December 2025. Following duplicate removal, screening, eligibility assessment, and quality appraisal, 19 studies met all methodological and thematic inclusion criteria and were included in the final thematic narrative synthesis. The review examined types of AI/ML technologies, disability categories (learning, sensory, physical, and psychosocial), educational and inclusion-related outcomes, and ethical and accessibility considerations. The included studies employed quantitative (47.4%), qualitative (31.6%), and mixed-methods (21.0%) designs. AI-driven interventions, such as intelligent tutoring systems, natural language processing applications, assistive technologies, and learning analytics, demonstrated positive effects on academic achievement, accessibility, learner autonomy, engagement, psychosocial outcomes, and social inclusion, with particularly strong evidence for learners with learning and sensory disabilities. However, evidence for institutional-level impact and long-term outcomes remains limited. Key challenges identified include algorithmic bias, data privacy risks, uneven accessibility compliance, and persistent inequities between high-income and low-resource contexts. Overall, the findings indicate that AI and ML can meaningfully support inclusive education when grounded in Universal Design for Learning (UDL) principles and rights-based frameworks. The review underscores the need for more methodologically rigorous, geographically diverse, and longitudinal research to determine which technologies are most effective for specific disability groups and to ensure that AI-enabled education advances inclusion rather than reinforcing existing inequities.
Abstract: Inclusive education has emerged as a global priority, emphasizing equitable access, participation, and learning outcomes for learners with disabilities. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have introduced new opportunities to address diverse learner needs through adaptive, personalized, and accessible education...
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Research Article
Athletic Participation and Matriculation Outcomes Among Collegiate Student-Athletes Applying to Medical School
Issue:
Volume 2, Issue 1, March 2026
Pages:
15-27
Received:
11 February 2026
Accepted:
25 February 2026
Published:
10 March 2026
DOI:
10.11648/j.mededu.20260201.12
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Views:
Abstract: Collegiate student-athletes represent a distinct medical school applicant population balancing intensive athletic training with academic and extracurricular preparation. The objective of this retrospective cross-sectional study was to examine how collegiate athletic participation and intensity relate to academic performance, experiential engagement, and medical school matriculation. We analyzed 276,858 applicants from the 2018–2022 American Medical College Application Service cycles using deidentified national data. Applicants were classified as athletes using a conservative threshold of collegiate athletic hours. Academic metrics, experiential profiles, and admissions outcomes were compared between student-athletes and non-student-athletes and between matriculated and non-matriculated student-athletes. To evaluate time-allocation tradeoffs, zero-inflated negative binomial models distinguished factors associated with athletic participation from those associated with athletic intensity. Student-athletes comprised 10% of applicants and demonstrated higher acceptance and matriculation rates than non-athletes, with comparable MCAT scores and only slightly lower grade point averages. Among student-athletes, matriculation was associated with stronger academic performance and greater engagement in research and medical community service. Modeling demonstrated that broad academic and service involvement was associated with athletic participation, whereas greater research, teaching, service, and non-medical employment involvement was associated with lower athletic intensity, reflecting measurable time-allocation tradeoffs. Paid medical employment was uniquely compatible with sustained athletic involvement. These findings suggest that differences in student-athlete application profiles arise from structural time constraints inherent to collegiate sport training environments rather than deficits in motivation or ability, highlighting implications for trainee workload, wellbeing, and equitable preparation pathways.
Abstract: Collegiate student-athletes represent a distinct medical school applicant population balancing intensive athletic training with academic and extracurricular preparation. The objective of this retrospective cross-sectional study was to examine how collegiate athletic participation and intensity relate to academic performance, experiential engagement...
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