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Cited by 53Artificial Intelligence in EducationTechnology Adoption and User BehaviourGender and Technology in Education

Training and technology acceptance of ChatGPT in university students of social sciences: a netcoincidental analysis

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García-Alonso, Elena María, León-Mejía, Ana Cristina, Sánchez-Cabrero, Roberto, Guzmán-Ordaz, Raquel·2024·Behavioral Sciences
DOI 10.3390/bs14070612PMC PMC11274043
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Abstract

This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this artificial intelligence tool. The analysis considers variables such as gender, academic year, prior experience with ChatGPT, and the training provided by university faculty. The networks created with the statistical tool “CARING” highlight the role of perceived utility, credibility, and prior experience in shaping attitudes and behaviors toward this emerging technology. Previous experience, familiarity with video games, and programming knowledge were related to more favorable attitudes towards ChatGPT. Students who received specific training showed lower confidence in the tool. These findings underscore the importance of implementing training strategies that raise awareness among students about both the potential strengths and weaknesses of artificial intelligence in educational contexts.

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  • Technology acceptance, learning outcomes, and user attitudes have often been examined separately, leaving the process links among search mode, task trust, source verification, and learning transfer insufficiently explained.

    “Second, technology acceptance, learning outcomes, and user attitudes have often been examined separately, leaving the process links among search mode, task trust, source verification, and learning transfer insufficiently explained (García-Alonso et al., 2024; Chen et al., 2025).”

    From Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions (2026) · cites this paper as García-Alonso et al., 2024

    The Literature Review (§2.3) discusses ChatGPT acceptance, attitudes, and perceived learning value, while the Results (§4) examine credibility and attitudes. Neither passage says these topics have often been studied separately or identifies an unexplained link among search mode, task trust, source verification, and learning transfer. Searches for those process links and the claimed research gap found no supporting passage in our copy. (LLM, conf=0.75)

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Cited by

Sorted by citations

Authors
García-Alonso, E. M., León-Mejía, A. C., Sánchez-Cabrero, R., & Guzmán-Ordaz, R.
Affiliations
  • Universidad de Salamanca (ES)
  • Department of Sociology and Communication, Faculty of Social Sciences, University of Salamanca, 37007 Salamanca, Spain
  • Universidad Autónoma de Madrid (ES)
Corresponding author
García-Alonso, Elena María
ORCID
Venue
Behavioral Sciences
Publisher
MDPI AG
Published
2024-07-18
Volume
14
Issue
7
Pages
612
ISSN
2076-328X (ISSN-L)
DOI
10.3390/bs14070612
PMC
PMC11274043
OpenAlex
W4400764339
Cited by
53
References
39
Citations by year
2026: 23 · 2025: 27 · 2024: 3
FWCI
13.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavioral Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Hispana (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Gredos (University of Salamanca) (submittedVersion, cc-by-nc-nd)
  • Hispana (submittedVersion, cc-by)
  • Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, public-domain)
Funders
  • Opinion Dynamics, Collective Action and Abrupt Social Change. The Role of Preference Falsification and Social Networks (PID2019-107589GB-I00, PDC2022-133355-I00)
  • Analytical networks for dissemination and applied research (PID2019-107589GB-I00, PDC2022-133355-I00)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Gender Studies › Gender and Technology in Education
Keywords
ChatGPT, technology acceptance model, university students, social sciences students, AI training, student attitudes toward AI, perceived usefulness, perceived credibility, programming knowledge, gender differences, academic year differences

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