Students’ Perspective of the Advantages and Disadvantages of ChatGPT Compared to Reference Librarians
Bibliographic record
Abstract
A Review of: Adetayo, A. J. (2023). ChatGPT and librarians for reference consultations. Internet Reference Services Quarterly, 27(3), 131–147. https://doi.org/10.1080/10875301.2023.2203681 Objective – To investigate students’ use of ChatGPT and its potential advantages and disadvantages compared to reference librarians at a university library. Design – Survey research. Setting – A university library in Nigeria. Subjects – Students familiar with ChatGPT (n=54) who were enrolled in a library users’ education course. Methods – A survey was conducted in a sample of undergraduate students enrolled in a library users’ education course, who had previously used ChatGPT. Participants were asked questions based on six categories that reflected frequency of use, types of inquiries, frequency of reference consultations, desire to consult reference librarians despite the availability of ChatGPT, and potential advantages and disadvantages of ChatGPT compared to reference librarians. A 4-point Likert scale was used to measure the responses from often to never, strongly agree to strongly disagree, and rarely to frequently. Main Results – The sample of students who participated (n=54) were a diverse group whose age varied from below 20 (35.2%) to above 30 years (31.5%) and represented a variety of fields of study, such as engineering, business and social sciences, arts, law, sciences, basic and medical sciences. Regarding frequency of use, the author reported that 40.7% of participants occasionally used ChatGPT, and 26.1% and 16.7% used it frequently or very frequently, respectively. Of the five options that represented types of inquiries (religious, political, academic, entertainment, and work), academic and work-related inquiries were topics most often searched in ChatGPT. Participants indicated that they consulted reference librarians occasionally (40.8%), frequently (37%), or rarely (22.2%). Most students (87%) would continue to consult reference librarians despite the availability of ChatGPT. For questions that compared ChatGPT to reference librarians, four options were provided to describe potential advantages and four options were provided to describe potential disadvantages. Most students agreed or strongly agreed that ChatGPT is more user friendly (83.4%), that it includes a broad knowledge base (90.7%), is easily accessible (83.3%), and saves time by responding to questions quickly (98%) compared to reference librarians. Fewer than half of the students agreed or strongly agreed that ChatGPT’s knowledge base is not up to date (47.2%). Most agreed or strongly agreed that it cannot comprehend some questions (72.3%), that it cannot read emotions as a librarian would (74.1%), and that responses to questions may be incorrect (66.6%). The potential advantage with the strongest response score was that ChatGPT saves time by responding to questions quickly (mean 3.52). The potential disadvantage with the strongest response score was ChatGPT could not read emotions as a librarian would (mean 2.91). Conclusion – Students from an academic institution acknowledged the potential advantages and disadvantages of ChatGPT over reference librarians, yet the majority of students would continue to utilize reference librarian services. The author suggests that ChatGPT is a versatile and useful tool as a supplement rather than a replacement for knowledgeable and personable reference librarians. Based on the results of the study, the author emphasizes the importance of interpersonal skills and enhanced accessibility of reference librarians outside of typical work hours.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.035 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".