Towards inclusive and equitable assessment practices in the age of GenAI: Revisiting academic literacies for multilingual students in academic writing
Bibliographic record
Abstract
Since higher education institutions aim to promote social justice through equity, diversity, and inclusion (EDI), educators have raised concerns about the equitable and inclusive implementation of AI-based assessment practices in academic writing for multilingual students, who have been historically disenfranchised. Using academic literacies and critical digital literacies, this opinion piece examines the potential and perils of GenAI in designing more equitable and inclusive assessment practices, particularly for multilingual students in English-medium academic contexts. To ensure that AI-mediated assessments are designed in accordance with the principles of EDI, educators need to shift from current skills-based assessments to an assets-based approach that focuses on developing students’ critical AI literacies. We propose redesigning writing assessments to focus on the students’ writing process rather than the product, engaging them in conversations about power imbalances and cultural biases in GenAI.
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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 | 0.048 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".