Enhancing Accessibility in Education Through Brain–Computer Interfaces: A Scoping Review on Inclusive Learning Approaches
Bibliographic record
Abstract
Brain–computer interfaces (BCIs) hold promise in enhancing accessibility in education by enabling students with physical disabilities to interact with digital learning environments without barriers. However, no comprehensive review has explored the landscape and role of BCIs in inclusive learning. Hence, this review sets out to identify relevant literature on BCI-based educational technologies, highlight their key themes, characteristics, and research methodologies, and identify research gaps. The secondary aim is to evaluate how these educational technologies contribute to inclusive learning frameworks by fostering communication, collaboration, engagement, and accessibility among students with disabilities. Overall, the reviewed studies demonstrate that BCIs can facilitate assistive communication among non-verbal students and provide motor control support for physically impaired persons. While these interventions show strong potential, challenges remain, including high implementation costs, user adaptability, and ethical concerns related to neural data privacy. Specifically, there is a need to (1) shift from experimental applications towards real-world classroom integration by developing user-friendly, cost-effective, and ethically sound BCI-based educational technologies, and (2) extend ongoing research efforts to include underserved populations to assess the generalizability of current and future BCI-based interventions. More importantly, future work should focus on enhancing BCI usability, improving adaptability for diverse learners, and establishing ethical guidelines for the development of socially responsible and inclusive neuro-educational technologies for all people with disabilities everywhere. This will go a long way in fostering the fourth and tenth United Nations Sustainable Development Goals of Quality Education and Reduced Inequalities, respectively.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".