Use of AI in teaching mathematics to persons living with disabilities: The Context of the United States
Bibliographic record
Abstract
This narrative literature review investigates the integration of Artificial Intelligence (AI) in mathematics education for students with learning disabilities (SLDs) in the United States. Employing a narrative literature review methodology, the study synthesises peer-reviewed articles, policy documents, and grey literature published between 2020 and 2025, sourced from academic databases such as Google Scholar, Science Direct, Emerald, ERIC, and JSTOR. The review is grounded in Universal Design for Learning (UDL) and Constructivist Learning Theory, providing a theoretical framework for evaluating the role of AI in inclusive education. The selection criteria prioritised studies focusing on AI applications that enhance accessibility, personalisation, and early detection of learning difficulties, with special attention to tools like Photomath, Mathway, Socratic, Century, and the MACS Curriculum. The findings highlight AI’s capacity to support differentiated instruction, automate feedback, and facilitate early screening for conditions such as dyscalculia and dysgraphia. The review also identifies persistent challenges, including inconsistent classroom adoption, infrastructural and policy limitations, ethical concerns, and gaps in accessibility for some AI platforms. Comparative insights from international contexts, notably China, further underscore the importance of supportive policy frameworks and teacher training. The study concludes that while AI holds significant promise for transforming mathematics instruction for students with disabilities, realising its full potential requires sustained investment in accessible technologies, ongoing research, and collaborative policy development. The review advocates for responsible, equitable, and pedagogically sound AI integration to ensure all learners benefit from technological advancements in education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".