Academic Level as a Moderator in University Students’ Acceptance of Educational AI Chatbots: An Extended TAM3 Model
Source abstract
Abstract
AI chatbots have the potential to facilitate students’ academic progress and enhance knowledge accessibility in higher education, yet learners’ attitudes toward these technologies vary amid AI-driven disruptions, with factors influencing acceptance remaining debated. The current study constructs an integrated model based on Technology Acceptance Model 3 (TAM3), an extension of the original TAM, incorporating factors including Self-Efficacy, Perceived Enjoyment, Anxiety, Perceived Ease of Use, Perceived Usefulness, Output Quality, Social Influence, and Behavioral Intention, to explore determinants and mechanisms influencing learners’ acceptance of AI chatbots. This addresses key challenges in AI-augmented learning, such as personalization benefits versus risks like information inaccuracy and ethical concerns. Results from the questionnaire survey analysis with 265 valid responses reveal significant relationships: (1) self-efficacy significantly predicts perceived ease of use; (2) both perceived enjoyment and perceived ease of use positively influence perceived usefulness; and (3) self-efficacy, perceived usefulness, and social influence collectively exert significant effects on behavioral intention. Measurement invariance tests further indicate significant differences in acceptance between undergraduate and graduate students, suggesting academic level moderates behavioral intentions. Findings offer principled guidance for designing inclusive AI tools that mitigate accessibility barriers and promote equitable adoption in educational environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in Service Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Design, Guangdong University of Technology, Guangzhou 510090, China
Funding
the Philosophy and Social Sciences Fund of Guangdong Province · the Postgraduate Education Innovation Program of Guangdong University of Technology · the Humanities and Social Sciences Research Foundation of the Ministry of Education, China
References (75)
Show source-provided references
1. The application of artificial intelligence in language teaching over the past three decades: Retrospect and prospect
2. Role of AI chatbots in education: Systematic literature review10.1186/s41239-023-00426-1 ↗
3. Moraes, C.L. (2021). Chatbot as a Learning Assistant: Factors Influencing Adoption and Recommendation. [Masters’ Thesis, Universidade NOVA de Lisboa].
4. Customer service chatbots: Anthropomorphism and adoption10.1016/j.jbusres.2020.04.030 ↗
5. Ho, C.C., Lee, H.L., Lo, W.K., and Lui, K.F.A. (August, January 31). Developing a chatbot for college student programme advisement. Proceedings of the 2018 International Symposium on Educational Technology (ISET), Osaka, Japan.
6. AI-based academic advising framework: A knowledge management perspective
7. Bilingual AI-driven chatbot for academic advising
8. Lim, M.S., Ho, S.-B., and Chai, I. (2020, January 29–30). Design and functionality of a university academic advisor chatbot as an early intervention to improve students’ academic performance. Proceedings of the Computational Science and Technology: 7th ICCST 2020, Pattaya, Thailand.
9. Perceived usefulness, perceived ease of use, and user acceptance of information technology10.2307/249008 ↗
10. Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students
11. Gupta, P., and Yadav, S. (2021, January 4–5). A TAM-based Study on the ICT Usage by the Academicians in Higher Educational Institutions of Delhi NCR. Proceedings of the Congress on Intelligent Systems: Proceedings of CIS 2021, Bengaluru, India.
12. Technology acceptance model 3 and a research agenda on interventions10.1111/j.1540-5915.2008.00192.x ↗
13. Study on the Influencing Factors of the Willingness to Use the Handheld Technology Digital Experiment MOOC Using PLS-SEM Measurement Based on TAM3 Model
14. My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education10.1080/10447318.2020.1801227 ↗
15. Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants10.1007/s10639-022-10947-3 ↗
16. Investigating the acceptance of applying chat-bot (Artificial intelligence) technology among higher education students in Egypt
17. Investigating student acceptance of an academic advising chatbot in higher education institutions10.1007/s10639-023-12076-x ↗
18. Qaid, E.H., Ateeq, A.A., Abro, Z., Milhem, M., Alzoraiki, M., Alkadash, T.M., and Al-Fahim, N.H. (2024). Conceptual Framework in Attitude Factors Affecting Yemeni University Lecturers’ Adoption of E-Government. Information and Communication Technology in Technical and Vocational Education and Training for Sustainable and Equal Opportunity: Business Governance and Digitalization of Business Education, Springer.10.1007/978-981-99-7798-7_29 ↗
19. Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success10.1007/s10796-022-10291-4 ↗
20. ChatGPT in higher education: Considerations for academic integrity and student learning
21. The promises and challenges of artificial intelligence for teachers: A systematic review of research10.1007/s11528-022-00715-y ↗
22. Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI)
23. Success through simplicity: What other artificial intelligence applications in medicine should learn from history and ChatGPT10.1007/s10439-023-03287-x ↗
24. Artificial Intelligence Education Application:Intelligent Tools, Advantages, Challenges and Future Development
25. A systematic literature review on personalised learning in the higher education context10.1007/s10758-022-09628-4 ↗
26. ChatGPT-Reshaping medical education and clinical management10.12669/pjms.39.2.7653 ↗
27. ChatGPT for good? On opportunities and challenges of large language models for education10.1016/j.lindif.2023.102274 ↗
28. Assessing the effect of using artificial intelligence on the writing skill of Indonesian learners of English10.21744/lingcure.v5n1.1555 ↗
29. Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context10.1016/j.chb.2023.107903 ↗
30. Chatgpt as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses
31. Ethical use of artificial intelligence in health professions education: AMEE Guide No. 15810.1080/0142159X.2023.2186203 ↗
32. A theoretical extension of the technology acceptance model: Four longitudinal field studies10.1287/mnsc.46.2.186.11926 ↗
33. Computer self-efficacy: Development of a measure and initial test10.2307/249688 ↗
34. Extrinsic and intrinsic motivation to use computers in the workplace 110.1111/j.1559-1816.1992.tb00945.x ↗
35. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning
36. An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach10.1016/j.compedu.2011.03.002 ↗
37. The acceptance and use of a virtual learning environment in China10.1016/j.compedu.2006.09.001 ↗
38. Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation10.1016/j.im.2003.10.007 ↗
39. Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective10.1016/j.jretconser.2014.09.006 ↗
40. Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM)10.1007/s10936-020-09752-1 ↗
41. User acceptance of computer technology: A comparison of two theoretical models10.1287/mnsc.35.8.982 ↗
42. Unpacking teachers’ intentions to integrate technology: A meta-analysis10.1016/j.edurev.2019.03.001 ↗
43. Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model10.1007/s10796-017-9774-y ↗
44. Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics10.1080/03055698.2020.1850426 ↗
45. Hair, J.F. (2009). Multivariate Data Analysis, Pearson.
46. Five point vs. eleven point scales: Does it make a difference to data characteristics
47. Likert items and scales of measurement
48. Li, K. (2023). Determinants of college students’ actual use of AI-based systems: An extension of the technology acceptance model. Sustainability, 15.10.3390/su15065221 ↗
49. Xu, J., Zhang, X., Li, H., Yoo, C., and Pan, Y. (2023). Is everyone an artist? A study on user experience of AI-based painting system. Appl. Sci., 13.10.3390/app13116496 ↗
50. Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis10.1186/s41239-023-00420-7 ↗
51. Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs10.1016/j.compedu.2018.01.003 ↗
52. Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model10.1109/ACCESS.2019.2939467 ↗
53. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)10.1016/j.techsoc.2019.101212 ↗
54. The emotional state of technology acceptance
55. The information quality and source credibility matter in customers’ evaluation toward food O2O commerce10.1016/j.ijhm.2018.10.011 ↗
56. Khan, M.I., Saleh, M.A., and Quazi, A. (2021, January 25–27). Social media adoption by health professionals: A TAM-based study. Proceedings of the Informatics, Madrid, Spain.10.3390/informatics8010006 ↗
57. Incorporating UTAUT predictors for understanding home care patients’ and clinician’s acceptance of healthcare telemedicine equipment10.4067/S0718-27242014000200003 ↗
58. Determinants predicting undergraduates’ intention to adopt e-learning for studying english in chinese higher education context: A structural equation modelling approach10.1007/s10639-021-10462-x ↗
59. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology10.2307/41410412 ↗
60. Why do people continue to play mobile game apps? A perspective of individual motivation, social factor and gaming factor
61. Pan, L., Xia, Y., Xing, L., Song, Z., and Xu, Y. (2022). Exploring use acceptance of electric bicycle-sharing systems: An empirical study based on PLS-SEM analysis. Sensors, 22.10.3390/s22187057 ↗
62. AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool
63. Testing for multigroup invariance using AMOS graphics: A road less traveled10.1207/s15328007sem1102_8 ↗
64. Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance10.1037/0033-2909.105.3.456 ↗
65. Structural equation modeling with AMOS: Basic concepts, applications, and programming (multivariate applications series)
66. Evaluating structural equation models with unobservable variables and measurement error10.1177/002224378101800104 ↗
67. Nunnally, J. (1994). Psychometric Theory, McGraw-Hill Inc.
68. Hayduk, L.A. (1987). Structural Equation Modeling with LISREL: Essentials and Advances, Jhu Press.10.56021/9780801834783 ↗
69. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives10.1080/10705519909540118 ↗
70. On the evaluation of structural equation models10.1007/BF02723327 ↗
71. Brown, T.A. (2015). Confirmatory Factor Analysis for Applied Research, Guilford Publications.
72. Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups10.1037/0033-2909.97.3.562 ↗
73. Assessing reliability and stability in panel models10.2307/270754 ↗
74. Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument10.1207/S15327574IJT0101_4 ↗
75. Unlocking the potential of GPT-3 in education: Opportunities, limitations, and recommendations for effective integration10.1080/10494820.2023.2220401 ↗