Exploring student perspectives on generative artificial intelligence in higher education 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 This study examined the perspectives of Ghanaian higher education students on the use of ChatGPT. The Students’ ChatGPT Experiences Scale (SCES) was developed and validated to evaluate students’ perspectives of ChatGPT as a learning tool. A total of 277 students from universities and colleges participated in the study. Through exploratory factor analysis, a three-factor structure of students' perspectives (ChatGPT academic benefits, ChatGPT academic concerns, and accessibility and attitude towards ChatGPT) was identified. A confirmatory factor analysis was carried out to confirm the identified factors. The majority of students are aware of and recognize the potential of Gen AI tools like ChatGPT in supporting their learning. However, a significant number of students reported using ChatGPT mainly for non-academic purposes, citing concerns such as academic policy violations, excessive reliance on technology, lack of originality in assignments, and potential security risks. Students mainly use ChatGPT for assignments rather than for class or group projects. Students noted that they have not received any training on how to use ChatGPT safely and effectively. The implications for policy and practice are discussed in terms of how well-informed policy guidelines and strategies on the use of Gen AI tools like ChatGPT can support teaching and improve student learning.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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