Student Use of Artificial Intelligence to Write Technical Engineering Papers – Cheating or a Tool to Augment Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Considerable concern has emerged over the potential use of AI tools by students for completing assignments in their classes. Reactions in academia have been mixed, with some describing such use of AI tools as "cheating" while others compare it to the use of calculators and see it as the impetus for enabling deeper learning by students. To analyze some of these issues, the recently released AI tool ChatGPT was used to respond to actual Discussion Board questions in our online cybersecurity classes. ChatGPT was also asked to write a Python program to develop a backpropagation Neural Network for XOR. The results were excellent, both for answering the Discussion Board Questions and for writing code. Four findings emerged from this effort: 1) ChatGPT does an exceptional job of answering questions and generating code, 2) it is not clear how student submissions generated with AI should be graded, 3) along with the AI tools themselves, tools have been developed that can detect whether AI was used to generate a student submission but with a high rate of false positives, and 4) despite these three findings, students could and should be encouraged to collaborate with AI tools, similar to the way they would collaborate with other students. These results led to four conclusions: 1) ethically, the use of tools such as ChatGPT without acknowledging that they have been used is cheating, 2) it will be impossible to stop students from using tools like ChatGPT, but unacknowledged use can be detected, albeit with a very high percentage of false positives, 3) use of AI tools should be encouraged rather than discouraged, and 4) higher education should focus on new methods and mechanisms for assessing student learning that take advantage of the AI tools.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it