Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Adoption of AI chatbots among medical students: integration of AI literacy and information literacy within the theory of planned behavior
0
Zitationen
4
Autoren
2026
Jahr
Abstract
Purpose Artificial intelligence (AI) chatbots have gained significant attention in the field of education, particularly in medical training and learning. This study aims to measure the AI chatbots adoption among medical students by extending the theory of planned behavior (TPB). The research examines the key determinants such as perceived behavioral control (PBC), past behavior (PB), subjective norms (SN), information literacy (IL), AI literacy and behavioral intention (BI). Design/methodology/approach A structured questionnaire was distributed among medical students for data collection. Participants received 400 copies of the questionnaire and the consent form to take part in the study. A total of 326 individuals returned the completed surveys, yielding an 81.5% response rate. 310 (77.5%) valid responses of the 326 questionnaires used for data analysis. To analyze the data, SPSS v26 (IBM) was used along with moment structure analysis (AMOS). Findings Findings indicated that PBC, PB and BI significantly influence AI chatbots adoption among medical students. However, the relationships of IL and AI literacy are positive and insignificant, while the relationship of SN is negative and insignificant. Research limitations/implications The study’s insights are beneficial for educators, policymakers and AI software developers seeking to enhance the use of AI chatbots in educational settings. Originality/value This study adds to the literature on the use of AI in education by analyzing the impact of TPB on predictive factors of using chatbots.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.