AI-driven assistive technologies in inclusive education: benefits, challenges, and policy recommendations
Bibliographic record
Abstract
This research examines the transformative role of AI-powered screen readers, voice assistants, and Natural Language Processing (NLP) interfaces in promoting inclusive education for students with visual, physical, and cognitive disabilities. The novelty of this study lies in its integrated, multi-modal exploration of assistive AI technologies across a variety of disabilities and use cases, including original case analyses that demonstrate real-world application and impact. Results reveal that AI-driven interfaces significantly improve autonomy, academic engagement, and content accessibility. Additionally, the paper highlights limitations related to accuracy, infrastructure needs, educator readiness, and ethical concerns such as data privacy and algorithmic bias. To address these challenges, the study proposes policy recommendations and practical strategies for equitable and responsible AI adoption in education, including targeted educator training, funding for inclusive infrastructure, and development of ethical and technical standards. By bridging theoretical analysis with applied insights, this paper offers a valuable contribution to the discourse on AI-driven inclusivity and serves as a foundation for future empirical validations and technical innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".