Redefining Neuro-Inclusive, Stakeholder-Informed Language Assessment in UK Higher Education
Bibliographic record
Abstract
This chapter argues for a stakeholder-informed Universal Design for Learning (UDL) framework to reform language assessment in UK higher education, ensuring equity for both neurodivergent and neurotypical learners. Through a hybrid systematic–narrative review (2010–2024), we interrogate UDL-aligned alternatives—multimodal tasks, iterative portfolios, formative feedback cycles, and culturally responsive methods—highlighting their promise alongside resource constraints, scalability challenges, and persistent institutional inertia. We scrutinize the roles of institutional leaders, academic staff, disability specialists, neurodivergent students, and community advocates in moving from superficial accommodations to genuinely inclusive design and propose strategic reforms in professional development, policy, and quality assurance. This roadmap challenges stakeholders to dismantle entrenched barriers and embed equity-driven assessment practices that celebrate cognitive diversity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".