Can GenAI Communicate? University Students' Views on Generative Language and Image Models
Bibliographic record
Abstract
The paper reviews the possibilities of human-GenAI communication based on classical communication paradigms. It then investigates the attitude of university students towards GenAI by interviewing engineering and teacher training students using an online questionnaire. The responses show that 40% of the teacher training students attribute intent to GenAI, one third fear that it will take away their work, while they also responded in the majority, that there are human-specific activities, such as the arts or communicating God's word, that AI cannot convey. Engineering students are much less afraid that Gen AI will take away their work, they can look at GenAI with a more user-centric attitude. They consider music to be algorithmizable, they think therefore can be mediated by AI or a robot. Also a quarter of them acknowledge that in the interaction between humans and AI, the human is the ethical regulator who should monitor the machine's actions.
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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 | 0.000 | 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) | 0.001 | 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".