Examining Recommendations for Artificial Intelligence Use with Integrity from a Scholarship of Teaching and Learning Lens
Bibliographic record
Abstract
New developments in the Artificial Intelligence (AI) field allowed the development of Generative Artificial Intelligence (GenAI), capable of creating text resembling what humans can produce. As a result, educators’ concerns in the higher education sector quickly emerged. Many organizations and experts have addressed these concerns through recommendations. In this conceptual paper, we draw from the Integrated Model for Academic Integrity through a Scholarship of Teaching and Learning Lens to examine and stimulate discussion from eleven documents that focus on using GenAI with integrity. We identified recommendations suitable for the individual (micro), the departmental/program (meso), the institutional (macro), and the interinstitutional/ national/ international (mega) levels concerning two core elements of the model: “high-impact professional learning for individuals and groups” and “local-level leadership and microcultures.” Suggestions around the core element “scholarship, research and inquiry” were lacking at the micro and meso levels; likewise, recommendations for the core element “learning spaces, pedagogies, and technologies” were also absent at the meso, macro, and mega levels. We acknowledge that these recommendations focus on learning, involve various stakeholders, and go beyond student conduct, which aligns with current approaches to academic integrity. However, some gaps need further exploration. We highlight the need to develop more specific and practical guidance and resources for educational stakeholders around GenAI issues related to academic integrity, explore how to better support networks and leaders in higher education in creating the conditions for ethical GenAI use, and emphasizing the need for an Equity, Diversity, and Inclusion lens on 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 | 0.121 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.022 | 0.076 |
| Scholarly communication | 0.049 | 0.045 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".