Students’ Perspective of the Advantages and Disadvantages of ChatGPT Compared to Reference Librarians
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,035 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».