Building Adult Capacity to Support Executive Functioning: A Universal Design for Learning Approach — A Literature Review
Bibliographic record
Abstract
This literature review examines how Universal Design for Learning (UDL) can inform capacity-building initiatives that strengthen adults’ understanding of executive functioning (EF) in neurodiverse populations. Drawing from current research in neuroscience, education, and adult learning, the paper explores EF development across the lifespan and its connection to well-being, mental health, and academic success. The review identifies four key stakeholder groups—neurodiverse adults, parents, educators, and pre-service teachers—and synthesizes strategies that foster reflection, self-regulation, and skill transfer within each. Integrating UDL principles with evidence-based adult learning and community engagement frameworks, such as Communities of Practice (CoPs), provides an inclusive model for developing EF capacity across diverse learners. This synthesis offers a foundation for an ongoing three-year study Building Capacity for Executive Functioning Support in Neurodiverse Communities and highlights opportunities to translate research into accessible, strengths-based practice. Keywords: executive functioning, neurodiversity, Universal Design for Learning, adult education, capacity building, community of practice
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".