AI Chatbots in Higher Education
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
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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