A Bibliometric analysis for AI-Powered Chatbots
Bibliographic record
Abstract
This study reports on the bibliometric analysis of AI chatbots from 2004 to 2024 (20 years) from the Elsevier Scopus database. Through bibliographical analysis of 915 Scopus-indexed documents, the review found that this is very recent literature, with over 98.46% of the relevant documents published since 2016. The contributions of institutional publications by affiliation showed that University of Toronto had the highest number of publications. In this bibliometric analysis, we examine the application of AI-powered chatbots across various domains, focusing on their potential for service enhancement and the challenges associated with their implementation in universities and higher education environment. By reviewing selected research articles, we identify trends, patterns, and key contributors in this expanding field. Notably, AI chatbots offer numerous advantages, such as efficiently handling user inquiries, which are relevant across multiple sectors. We ensure the scientific validity of the study and provide a concise analysis of the existing literature. This bibliometric analysis aims to contribute to the knowledge base and facilitate discussions and planning for the effective deployment of AI chatbots in different sectors and also in university environment in future. In conclusion, this research offers practical recommendations to policymakers, industry leaders, and technology developers on the utilization of AI chatbots to maximize their positive impact and foster supportive environments across different industries in future.
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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 | 0.014 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.256 | 0.299 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".