The Role of ChatGPT in the Educational Process: The Perspective of Sociology Students
Abstract
This paper examines the role of ChatGPT within higher education by integrating two theoretical frameworks: the Technology Acceptance Model (TAM) and Actor-Network Theory (ANT). Through a quantitative survey conducted among 155 sociology students across five Croatian universities, the study explores perceptions of ChatGPT’s usefulness, ease of use,
trustworthiness, and adoption intentions. TAM provides a quantitative foundation for understanding students’ attitudes toward ChatGPT, while ANT enables the interpretation of ChatGPT as an active participant within educational networks. The results indicate that although students recognize ChatGPT’s potential to enhance learning efficiency (79% agreed that
ChatGPT speeds up academic task completion – 49.4% strongly; 29.6% somewhat), concerns about trust (mean trust in the reliability of supporting technologies = 2.91 on a 1–5 scale), cognitive load, and limited social influence moderate its adoption.Findings highlight the importance of AI literacy, the need for institutional guidelines on ethical AI use, and the role of
educational institutions in supporting responsible integration of AI technologies into academic practice. The study concludes by offering recommendations for strengthening AI adoption strategies in higher education and outlines directions for future research.
Keywords
Full Text:
PDFReferences
Abdallah, Naya, Rateb Katmah, Kinda Khalaf, Herbert F. Jelinek (2025), ˝Systematic review of ChatGPT in higher education: Navigating impact on learning, wellbeing, and collaboration˝, Social Sciences & Humanities Open, 12, 101866.
Ajzen, Icek, Martin Fishbein (1977), ˝Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research˝, Philosophy and Rhetoric, 10(2), 130-132.
An, Yunjo, Ji Hyun Yu, Shadarra James (2025), ˝Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration˝, International Journal of Educational Technology in Higher Education, 22, 10.
Ardito, Cesare Giulio (2025), ˝Generative AI detection in higher education assessments˝, New Directions for Teaching and Learning, 182, 11–28.
Bagozzi, Richard P. (2007), ˝The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift˝ Journal of the Association for Information Systems, 8(4), 244–254.
Baig, Maria Ijaz, Elaheh Yadegaridehkordi (2024), ˝ChatGPT in the higher education: A systematic literature review and research challenges˝, International Journal of Educational Research, 127, 102411.
Bittle, Kyle, Omar El-Gayar (2025), ˝Generative AI and academic integrity in higher education: A systematic review and research agenda˝, Information, 16(4), 296.
Callon, Michel (1984), ˝Some Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of St Brieuc Bay˝, The Sociological Review, 32(1_suppl), 196-233.
Castañeda, Linda, Neil Selwyn (2018), ˝More than Tools? Making Sense of the Ongoing Digitizations of Higher Education˝, International Journal of Educational Technology in Higher Education, 15(1), 1–10.
Davis, Fred D. (1989), ˝Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology˝ MIS Quarterly, 13(3), 319–340.
Davis, Fred D., Richard P. Bagozzi, Paul R. Warshaw (1989),˝User Acceptance of Computer Technology: A Comparison of Two Theoretical Models˝ Management Science, 35(8), 982–1003.
EDUCAUSE Working Group (2024), AI literacy in teaching and learning — Executive summary, EDUCAUSE; https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/executive-summary
Fenwick, Tara, Richard Edwards (2010), Actor-Network Theory in Education (1st ed.), Routledge
Fenwick, Tara, Richard Edwards, Petar Sawchuk (2011), Emerging Approaches to Educational Research: Tracing the Sociomaterial, Routledge, London
HEPI (2025), Student generative AI survey 2025 (HEPI & Kortext report), HEPI; https://www.hepi.ac.uk/wp-content/uploads/2025/02/HEPI-Kortext-Student-Generative-AI-Survey-2025.pdf
Klačmer, Marina (2020). Modeli prihvaćanja tehnologije i digitalna pismenost: empirijska primjena u obrazovanju, Sveučilište u Zagrebu, Zagreb
Knox, Jeramy (2020), ˝Artificial Intelligence and Education in China ˝, Learning, Media and Technology, 45(3), 298–311.
Kumar, Vishesh, Mike Tissenbaum (2022), ˝Supporting Collaborative Classroom Networks Through Technology: An Actor Network Theory Approach to Understanding Social Behaviours and Design˝, British Journal of Educational Technology, 53(6), 1549-1570.
Latour, Bruno (1996), ˝On Interobjectivity˝, Mind, Culture, and Activity, 3(4): 228–245.
Latour, Bruno (2005), Reassembling the Social: An Introduction to Actor-Network-Theory, Oxford University Press, Oxford
Law, John (1992) Notes on the Theory of the Actor-Network: Ordering, Strategy, and Heterogeneity. Systems Practice, 5(4): 379–393. https://doi.org/10.1007/BF01059830
Legris, Paul, John Ingham, Pierre Collerette (2003), ˝Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model˝, Information & Management, 40(3), 191–204.
Luckin, Rose, Wayne Holmes, Mark Griffiths, Laurie B. Forcier (2016), Intelligence Unleashed: An Argument for AI in Education, Pearson Education, London
Orlikowski, Wanda J., C. Suzanne Iacono (2001), ˝Research Commentary: Desperately Seeking the ‘IT’ in IT Research – A Call to Theorizing the IT Artifact˝, Information Systems Research, 12(2), 121–134.
Selwyn, Neil (2010), ˝Looking Beyond Learning: Notes Towards the Critical Study of Educational Technology˝, Journal of Computer Assisted Learning, 26(1): 65–73.
Selwyn, Neil (2022), ˝The Future of AI and Education: Some Cautionary Notes˝, European Journal of Education, 57, 620–631.
Sørebø, Øystein, Hallgeir Halvari, Vebjørn Flaata Gulli, Roar Kristiansen (2009), ˝The Role of Self-Determination Theory in Explaining Teachers’ Motivation to Continue to Use E-Learning Technology˝, Computers & Education, 53(4): 1177–1187.
Sørensen, Estrid (2009), The Materiality of Learning: Technology and Knowledge in Educational Practice, Cambridge University Press, Cambridge
The Guardian 2025), ˝Sector analysis: Rise of AI-related academic misconduct and the need for assessment reform˝, The Guardian; https://www.theguardian.com/education/2025/jun/15/thousands-of-uk-university-students-caughtcheating-
using-ai-artificial-intelligence-survey?utm_source=chatgpt.com
Turnitin (2024–2025), Technical notes on AI detection (thresholds and false positives), Turnitin; https://guides.turnitin.com/hc/en-us/articles/28457596598925-AI-writing-detection-in-the-classic-report-view?utm_source= chatgpt.com
UNESCO (2023), Guidance for generative AI in education and research,UNESCO; https://unesdoc.unesco.org/ark:/48223/pf0000386693
Venkatesh, V., & Davis, F.D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, Viswanath, Michael G. Morris, Gordon B. Davis, Fred D. Davis (2003), ˝User Acceptance of Information Technology: Toward a Unified View˝, MIS Quarterly, 27(3), 425–478.
Watson, Steven, Erik Brezovec, Jonathan Romic (2025), ˝The Role of Generative AI in Academic and Scientific Authorship: An Autopoietic Perspective ˝, AI & Society, 40, 3225-3235.
Yu, Chengcheng, Jinzhe Yan, Na Cai (2024), ˝ChatGPT in higher education: Factors influencing ChatGPT user satisfaction and continued use intention˝, Frontiers in Education, 9, 1354929.
Zawacki-Richter, Olaf, Victoria I. Marín, Melissa Bond, Franziska Gouverneur (2019), ˝Systematic Review of Research on Artificial Intelligence Applications in Higher Education – Where Are the Educators?˝, International Journal of Educational Technology in Higher Education, 16(1), 1–27.
DOI: https://doi.org/10.51558/2490-3647.2025.10.2.937
Refbacks
- There are currently no refbacks.
ISSN: 2490-3604 (print) ● ISSN: 2490-3647 (online)
Društvene i humanističke studije - DHS is under the Creative Commons licence.