AI Chatbots in Higher Education
Bibliographic record
Abstract
This chapter evaluates the qualitative studies on how AI chatbots impact HE, specifically their benefits and challenges. A systematic search was conducted across academic databases resulting in the inclusion of 27 research papers published between 2018 and 2023. The research in this involved utilizing the Critical Appraisal Skills Programme (CASP) checklist for Systematic Review to evaluate the quality and relevance of each study followed by a thematic analysis of the data using Braun and Clarke's approach to identify key themes. The first theme, ” Improved Learning Experience,” explores the benefits of including personalized support, increased engagement, user-friendliness, skills development, and efficiency. The second theme, “: Practical and Ethical Issues,” delves into the practical and ethical issues, such as ethical concerns, pedagogical limitations, information accuracy, and technical challenges. A balanced approach to integrate AI chatbots in HE, addressing ethical and technical concerns while maximizing its benefits were given emphasis on the studies reviewed and evaluated.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".