A Toolkit for Sustainable Educational Environment in the Modern AI Era: Guidelines for Mitigating the Misuse of GenAI in Assessments
Bibliographic record
Abstract
Large Language Models (LLMs), utilised through a number of software tools, are now widely used by students to support their learning and complete their assignments. Evaluating the intellectual abilities of learners has always been an important component of assessment to gauge learning performance and learning outcomes, an aspect that has become challenging with the availability of LLMs. Nevertheless, the requirements of digital evaluation remain an important mechanism to support equality and integrity in academia, especially for authentic assessments. Educators require a methodology to evaluate the reliability of evaluating student performance; hence, Artificial Intelligence (AI) can also be used to guide assessment design methods. This study introduces a toolkit for a sustainable educational environment that leverages AI to mitigate the misuse of AI by students in completing their assignments and assessing their learning, while empowering professional degrees, such as engineering, to maintain their accreditation status. This article features views of prominent academics, researchers, distinguished scientists and professional services staff from various disciplines within Queen Mary University of London (QMUL), along with external national and international experts from both academia and industry, who discuss effective guidelines and learning practices to support fair assessment and reduce the misuse of AI by students in completing their assessments.
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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 | 0.117 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".