Artificial Intelligence to Strengthen Pedagogical Support for Students with Learning Disorders
Bibliographic record
Abstract
Learning disorders present persistent challenges to educational systems, often requiring differentiated support mechanisms that exceed the capacity of traditional pedagogical models. Recent advances in artificial intelligence (AI) have introduced transformative possibilities for individualized, adaptive, and evidence-based interventions. This article examines how AI can enhance pedagogical accompaniment for students with learning disorders by integrating diagnostic precision, personalized learning trajectories, and continuous monitoring. Through a systematic review of current literature and analysis of applied case studies, the study highlights the potential of AI-driven tools such as intelligent tutoring systems, natural language processing, and predictive analytics. Findings suggest that AI not only complements the role of educators but also fosters inclusion, engagement, and academic growth in learners with dyslexia, dyscalculia, attention-deficit/hyperactivity disorder (ADHD), and other cognitive challenges. Recommendations are provided to guide future educational policies and practices in leveraging AI for inclusive pedagogy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".