An examination of business school students’ experience with Generative Artificial Intelligence in a blog writing assignment
Bibliographic record
Abstract
Purpose: This study explores the utilisation of generative artificial intelligence (GenAI) by students in assessments within higher education. Design/methodology/approach: We reviewed assessments from 53 students undertaking a postgraduate module in a business school. In developing their submissions, students were required to experiment with GenAI and discuss their experiences in accompanying reflective statements. These were subsequently analysed using a thematic coding approach and combined with other relevant data. Findings: Students utilised GenAI for various purposes and to different levels. Higher use of GenAI was associated with lower performance. Participants demonstrated understanding of the strengths and limitations of GenAI, with students with dyslexia reporting specific benefits. Research limitations/implications: To date, most empirical research on GenAI has focused on student perceptions of the technology. This study extends previous work by exploring how students use GenAI in practice. The primary limitation relates to the sample size and singular cohort. Practical implications: The study has implications for students, educators, and higher education institutions and related organisations. In particular, it emphasises the need for clear policies related to GenAI use, as well as initiatives to improve AI literacy. Social implications: As GenAI becomes increasingly embedded in the fabric of society, it is essential that individuals understand how to utilise the technology ethically and effectively. Higher education institutions have a critical role to play in this regard. Originality/value: This study addresses a gap in relation to understanding of how students utilise GenAI, identifying varying stages and levels of application. In addition, it examines the relationship between GenAI use and student performance. Keywords: Generative artificial intelligence, ChatGPT, Chatbot, Learning and teaching, Learning outcomes, 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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".