Categorizing Interactive Digital Learning Tools to Support Neurodivergent Students in Engineering Education
Bibliographic record
Abstract
Neurodivergent (ND) students face significant challenges in higher education due to traditional academic structures that often fail to accommodate their diverse cognitive, sensory, and executive functioning needs. To address these barriers, our study systematically categorizes digital tools that support neurodivergent students, aligning them with Universal Design for Learning (UDL) principles to enhance awareness and promote inclusive educational practices. The research categorizes digital tools based on their functionality, focusing on three UDL dimensions: (1) Multiple Means of Engagement, (2) Multiple Means of Representation, and (3) Multiple Means of Action and Expression. By structuring these tools according to UDL, we aim to provide educators, administrators, and students with a clear framework for integrating accessible technologies into teaching and learning environments. Key categories include: 1) Executive Function and Time Management Tools: (e.g., Goblin Tools, Tiimo) to assist ND students with task planning, organization, and routine-building, 2) Alternative Learning and Representation Tools: (e.g., NaturalReader, OpenDyslexic, Read&Write) that support text-to-speech conversion, alternative reading styles, and enhanced accessibility for dyslexic learners, 3) Social and Communication Support Tools: (e.g., Emergency Chat, SpeechStream) designed to help students with non-verbal communication, tone modulation, and emotional regulation. By categorizing and mapping these tools to UDL principles, our study aims to increase institutional awareness of the importance of technology-enhanced inclusivity in education. This structured approach provides a practical guide for educators and administrators to adopt digital tools that support ND students equitably, ensuring that learning environments are accessible, flexible, and responsive to diverse needs.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".