What do Learners See in ChatGPT? Challenges, Benefits, and Writing in the Era of Generative AI Literacy
Bibliographic record
Abstract
This thesis examines the rapid, uncritical adoption of ChatGPT, a widely used generative AI model, by focusing on students' perceptions and use of the tool for writing tasks. The study explores three main questions: how learners use ChatGPT, the advantages and disadvantages they identify, and their awareness of its ethical implications. Using a qualitative approach, semi-structured interviews were conducted with 31 university students and recent graduates, followed by thematic analysis to uncover recurring patterns. Findings show that participants mainly used ChatGPT for drafting, research, editing, and brainstorming, valuing its efficiency and usability. However, they expressed concerns over generic, repetitive outputs, limited research capabilities, and the risk of deskilling, fearing dependence on the tool might erode their own skills. While participants recognized some ethical issues, particularly in education, disinformation, and privacy, awareness of bias, transparency, and sustainability was limited. Despite these drawbacks, participants generally maintained a positive attitude towards ChatGPT, with a strong interest in maximizing its benefits while attempting to manage its limitations. This study underscores the need for promoting critical engagement with AI through Human-Centred AI (HCAI), which emphasizes ethical considerations in technology use. The findings lay a foundation for future research aimed at addressing misconceptions about generative AI and creating educational strategies for ethical AI integration.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".