A Study on the Knowledge and Perception of Artificial Intelligence
Notice bibliographique
Résumé
A Review of: Subaveerapandiyan, A., Sunanthini, C., & Amees, M. (2023). A study on the knowledge and perception of artificial intelligence. IFLA Journal, 49(3), 503–513. https://doi.org/10.1177/03400352231180230 Objective – To assess the knowledge, perception, and skills of library and information science (LIS) professionals related to artificial intelligence (AI). Design – 45 statements were distributed to 469 LIS professionals via Google Forms to collect primary data. 245 participants responded to the structured questionnaire. Setting – University and college libraries in Zambia. Subjects – Zambian library and information science professionals.Methods – A descriptive approach was employed for the study. Data was gathered via a questionnaire. “The objective was to assess the statistical relationship between the knowledge, perception, and skills of LIS professionals (the independent variables) and AI (the dependent variable)” (Subaveerapandiyan et al., p. 506). The survey used a 5-point Likert scale with (1) strongly disagree being the lowest score and (5) strongly agree the highest. Means and standard deviations are included in data display tables. Thematic analysis was employed to analyze the data. SPSS was used for data analysis.Main Results – Survey results are presented in three tables. Table 1, “Awareness of AI among LIS professionals,” contains 21 statements related to AI use in various library environments and services, including reference (finding articles and citations, content summarization, detecting misinformation), circulation of library materials, security and surveillance, character recognition and document preservation, research data management, language translation, and others. The authors note that 44.1 percent of the respondents agreed that “AI is essential for the effectiveness and efficiency of library service delivery, enabling libraries to enhance and offer dynamic services for their users” (Subaveerapandiyan et al., 2023, p. 506). Table 2, “Perception of AI among LIS professionals,” contains 10 statements. Over 85 percent of respondents either strongly agreed or agreed that AI “makes library staff lazy” while 58.1 percent either strongly agreed or agreed that AI is a “threat to librarians’ employment” (Subaveerapandiyan et al., 2023, p. 506). The authors note that the “respondents also indicated barriers to the adoption of AI in libraries, such as the lack of LIS professionals’ skills and budgetary constraints” (Subaveerapandiyan et al., 2023, p. 506). Table 3 lists 13 competencies required by library professionals in the AI era. The majority of the respondents (an average of 65 percent) were in strong agreement that “electronic communication, hardware and software, Internet applications, computing and networking, cyber security and network management, data quality control, data curation, database management … are necessary competencies required by LIS professionals for them to be proficient in AI” (Subaveerapandiyan et al., 2023, p. 506).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,137 |
| 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 ».