Bibliographic record
Abstract
Recently, the rapid advancement of AI hardware and tools has led to the widespread adoption of natural language transformers like OpenAI’s ChatGPT, Google Bard, Bing AI, and others in various business sectors. Nevertheless, for the academic community, these AI tools present both opportunities and threats. Like their counterparts in the business and industrial sectors, academics can leverage these AI tools for coding, idea/concept generation, planning, and other applications, benefiting from their global usage. However, the academic community also harbors concerns regarding the potential impact on academic integrity, as students may be tempted to rely on these tools to complete their essays, assignments, and exams without putting in their own efforts. In this article, we will present the authors’ approach and findings in dealing with these AI tools while evaluating students’ performance with two university student groups: engineering (Canadian) and communication technology (Taiwanese). We have identified key guidelines to deter students from directly copying answers provided by AI tools like ChatGPT. However, it is important to recognize that this approach will be an ongoing process, as AI tools continuously learn and adapt to new cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".