Can AI be Helpful for Teaching Engineering Subjects?
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
Recently we have been hearing a lot about artificial intelligence and how it can influence the ways we are doing things, for the better or the worse. In particular, the effects it has on education and learning has received a lot of attention. This paper focuses on if indeed AI can be used as a useful tool in tackling engineering problems. Here, based on my limited experience with AI but a long history of teaching engineering courses I have come to some conclusions about if AI can become helpful for learning engineering subjects, in which the physics of matters play an important role. Although this work is limited more to engineering problems, it can well apply to other disciplines, such as biology, economics, and some other subjects.The main question is if AI can provide us with ways for better teaching or facilitates the ways students learn a subject. In this regard, first it must be reliable and trustworthy. It is shown that the present performance of AI is not acceptable/satisfactory to provide correct and reliable help for education of engineering subjects.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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