TRANSFORMING PROFESSIONAL PRACTICE WITH CHATGPT: LEARNING AND INFORMATION PROCESSING
Notice bibliographique
Résumé
Purpose: The main objective of the present study was to identify differences in how employed and non-employed students evaluate ChatGPT’s dual functions – information processing and tutoring. Design/methodology/approach: A Computer-Assisted Self-Interview (CASI) survey was conducted in the second quarter of 2024. After excluding non-users of ChatGPT, 449 valid responses were analyzed. Instrument reliability and factorability were verified. To assess the intensity of selected variables, a five-point Likert-type scale was applied. Because variables departed from normality, non-parametric tests (Mann-Whitney U) compared evaluations between employed and non-employed respondents. Findings: Respondents in both groups evaluated ChatGPT positively as a substitute for a traditional search engine, with no notable differences between employed and non-employed students. In contrast, non-employed students assessed ChatGPT’s tutoring role more favorably, which may reflect their greater reliance on digital tools for academic support. Overall, evaluations tended to be positive, although the variability in responses suggests differing levels of familiarity with or expectations toward the technology. Research limitations/implications: This study reflects one point in time, so future research should examine changes over longer periods. The analysis focused only on two main functions of ChatGPT – information processing and tutoring and on general use rather than specific academic tasks. Because the sample consisted solely of Polish students, the findings may not be fully applicable in other cultural contexts. Future studies should therefore involve more diverse populations and explore additional functions and learning situations. Practical implications: For students and early-career knowledge workers, conversational search with summarized answers can serve as the standard approach. Tutoring and guided support may be especially useful for those with more time for structured learning, such as non employed students. Universities and organizations should combine AI use with basic training in how to check information, create effective prompts, and evaluate results, while also providing clear source information to ensure that human judgment remains central. Social implications: Adjusting AI support to students’ time and workload can help reduce inequalities in learning. Teaching habits of verification – such as citing sources and signaling uncertainty – can lower the risks of overreliance, bias, and weakened critical thinking, while still allowing users to benefit from productivity gains. Originality/value: Introduces a two-function framework (interactive retrieval/processing vs. tutoring) linking HCIR-style information work with AI-supported learning, and provides empirical evidence that employment status does not shape evaluations of the search-substitution function but does differentiate evaluations of the tutoring function in a large sample of active users.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| 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 ».