What should we be assessing exactly? Higher education staff narratives on gen AI integration of assessment in a postplagiarism era
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) has challenged assumptions about assessments in higher education. Although GenAI chatbots create opportunities for inclusive learning, their misuse raises new concerns about academic integrity. This study provides empirical insight to inform assessment practices designed to include chatbots ethically. Guided by a postplagiarism framing, our research question was: How do educators make meaning of the ethical boundaries involved in evaluating students’ skills when chatbots are integrated in assessments? Adopting a qualitative design informed by an interpretive paradigm and narrative inquiry, we conducted a thematic narrative analysis of interviews with higher education staff (N = 28). Educators positioned themselves as stewards of learning with integrity within their disciplines, delineating boundaries that distinguished acceptable from unacceptable chatbot use. Across narratives, participants viewed prompting and critical thinking skills as compatible with chatbot integration. In contrast, perspectives on assessments focusing on writing skills were nuanced, ranging from specific inclusion to complete exclusion, depending on intended learning outcomes, disciplinary conventions and individual conceptions about learning with integrity. Educators also identified broader challenges, such as the potential for language standardisation, overreliance, blurred authorship and untraceable forms of cheating. These findings extend ongoing debates about integrity and assessments in technologically mediated learning.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".