Will ChatGPT replace libraries? A thematic literature review and recommendation framework
Bibliographic record
Abstract
Purpose This study is concerned with the disruptive impact of artificial intelligence (AI) technologies – specifically focusing on OpenAI’s ChatGPT – on human engagement and use of it. It seeks to determine whether these tools pose a danger to the utility and function of traditional libraries and resource centers.Design/methodology/approach Analysis is conducted through the lenses of the technological capabilities of AI systems, the socio-institutional roles of libraries, and a content analysis of recent literature. It looks at the ways in which traditional information institutions and AI-driven platforms are changing within a larger digital knowledge ecosystem.Originality/value This article takes a balanced approach to the possible implications of these relations between traditional information services and nascent AI technology. The argument is advanced by reiterating the services that libraries alone uniquely render, such as cultural stewardship, egalitarian access, and ethical custodianship.Findings ChatGPT and similar AI applications are valuable for providing information and cognitive support, but they do not encompass the full range of functions, social presence, and ethical responsibilities of libraries and resource centers. AI represents an extension rather than a replacement of libraries in the digital age.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.048 | 0.058 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".