ChatGPT and the Pedagogical Challenge: Unveiling the Impact on Early-Career Academics in Higher Education
Bibliographic record
Abstract
Emerging Artificial Intelligence (AI) tools like ChatGPT offer considerable promise for enhancing professional development in higher education. This study focuses on the experiences of early-career lecturers who utilise ChatGPT for professional development. Central questions explore ChatGPT's influence on their overall professional experience and the challenges and benefits of its use. While research on AI in education has been growing, few studies have delved into the specific experiences of young academics using ChatGPT. This study employed qualitative methods to address this gap, pre-cisely 45- to 60-minute in-depth interviews with two purposively selected lecturers, followed by a thematic analysis using a grounded theory approach. Our findings reveal a multifaceted landscape: on the one hand, ChatGPT enhances the ability to construct intricate academic arguments, increases effi-ciency in research and teaching tasks, and heightens critical thinking capabilities. Conversely, chal-lenges such as initial technical hurdles, occasional incorrect outputs, and concerns about over-dependency were also highlighted. Through this investigation, the study contributes to the broader discourse on AI in education by illustrating both the promising opportunities and potential pitfalls of using ChatGPT in academia. The study underscores the need for a balanced approach to AI adoption. It offers insights into the role of AI tools like ChatGPT in shaping the future of professional development in higher education.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".