Relationship between Students’ Attitudes towards Artificial Intelligence (AI) and their usage of AI Chatbots
Published 2026-03-16
Keywords
- Artificial Intelligence,
- Chatbots,
- ChatGPT,
- Student attitudes,
- Higher education
How to Cite
Copyright (c) 2026 Suchitra Veera, Samantha Bietsch, Anthony Bennett, Susan Jones, James Rice

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Integration of AI in the classroom has raised questions about academic integrity, ethics, and the educational value of chatbots. This quantitative study examined graduate students’ attitudes toward AI chatbots and their self-reported usage, with particular attention to perceptions of academic integrity, ethics, and educational value. Data were collected from 54 doctoral students enrolled at a private, online university in the United States using a structured survey instrument. Statistical analyses indicated no significant gender differences in attitudes toward AI chatbots, but significant differences across fields of study. Favorable attitudes toward chatbot use, perceptions that chatbot-generated results were superior, and disagreement with prohibiting chatbot use were positively correlated with reported ChatGPT usage. Findings highlight the need for discipline-sensitive guidance and clear institutional policies addressing ethical AI use in higher education.
References
- Acosta-Enriquez, B. G., Arbulu Ballesteros, M., Vilcapoma Pérez, C. R., Huamaní Jordan, O., Martin Vergara, J. A., Martel Acosta, R., Arbulu Perez Vargas, C. G., & Arbulú Castillo, J. C. (2025). AI in academia: How do social influence, self-efficacy, and integrity influence researchers' use of AI models? Social Sciences & Humanities Open, 11, 101274. https://doi.org/https://doi.org/10.1016/j.ssaho.2025.101274
- Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11(1), 1-12.
- Anderson, J. E., Schwager, P. H., & Kerns, R. L. (2006). The Drivers for Acceptance of Tablet PCs by Faculty in a College of Business [Article]. Journal of Information Systems Education, 17(4), 429-440. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=23720689&site=bsi-live
- Asiksoy, G. (2024). An Investigation of University Students' Attitudes towards Artificial Intelligence Ethics. International Journal of Engineering Pedagogy, 14(8), 153-169. https://doi.org/10.3991/ijep.v14i8.50769
- Barrot, J. S. (2024). Leveraging ChatGPT in the Writing Classrooms: Theoretical and Practical Insights. Language Teaching Research Quarterly, 43, 43-53. https://research.ebsco.com/linkprocessor/plink?id=403611d6-5b35-3469-a1ad-711d06d523e1
- Biloš, A., & Budimir, B. (2024). Understanding the Adoption Dynamics of ChatGPT among Generation Z: Insights from a Modified UTAUT2 Model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863. https://doi.org/https://doi.org/10.3390/jtaer19020045
- Bobula, M. (2024). Generative Artificial Intelligence (AI) in Higher Education: A Comprehensive Review of Challenges, Opportunities, and Implications. Journal of Learning Development in Higher Education(30). https://research.ebsco.com/linkprocessor/plink?id=8a881d67-c7e2-3e17-b127-e55ba750b976
- Chan, C. K. Y. (2023). A Comprehensive AI Policy Education Framework for University Teaching and Learning. International Journal of Educational Technology in Higher Education, 20. https://doi.org/10.1186/s41239-023-00408-3
- Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1-18. https://doi.org/10.1186/s41239-023-00411-8
- Crootof, R., Kaminski, M. E., Price, W., & Nicholson, I. (2023). Humans in the Loop. Vand. L. Rev., 76, 429.
- Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8).
- Dhanya, C., & Ramya, K. (2025). Unlocking Banking Chatbot Adoption: A Unified Approach through Extended TAM and UTAUT Model. SDMIMD Journal of Management, 16(1), 93-104. https://doi.org/https://doi.org/10.1831l/sdmimd/2025/48908
- Dhiman, N., & Jamwal, M. (2023). Tourists’ post-adoption continuance intentions of chatbots: integrating task–technology fit model and expectation–confirmation theory. Foresight : the Journal of Futures Studies, Strategic Thinking and Policy, 25(2), 209-224. https://doi.org/https://doi.org/10.1108/FS-10-2021-0207
- Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1), 2287917.
- Gruenhagen, J. H., Sinclair, P. M., Carroll, J.-A., Baker, P. R. A., Wilson, A., & Demant, D. (2024). The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence, 7, 100273. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100273
- Hatwar, N., Patil, A., & Gondane, D. (2016). AI based chatbot. International Journal of Emerging Trends in Engineering and Basic Sciences, 3(2), 85-87.
- Ivanov, S. (2023). The dark side of artificial intelligence in higher education. Service Industries Journal, 43(15/16), 1055-1082. https://doi.org/10.1080/02642069.2023.2258799
- Jaboob, M., Hazaimeh, M., & Al-Ansi, A. M. (2025). Integration of Generative AI Techniques and Applications in Student Behavior and Cognitive Achievement in Arab Higher Education. International Journal of Human-Computer Interaction, 41(1), 353-366. https://doi.org/10.1080/10447318.2023.2300016
- Kalenda, P. J., Rath, L., Abugasea Heidt, M., & Wright, A. (2025). Pre-service teacher perceptions of ChatGPT for lesson plan generation. Journal of Educational Technology Systems, 53(3), 219-241.
- Kerimbayev, N., Adamova, K., Shadiev, R., & Altinay, Z. (2025). Intelligent educational technologies in individual learning: a systematic literature review. Smart Learning Environments, 12(1), 1.
- Kizilcec, R. F., & Lee, H. (2022). Algorithmic fairness in education. In The ethics of artificial intelligence in education (pp. 174-202). Routledge.
- Laak, K.-J., & Aru, J. (2025). AI and personalized learning : Bridging the gap with modern educational goals. Educational Technology & Society, 28(4), 133-150. https://research.ebsco.com/linkprocessor/plink?id=f07c6594-34a4-3a1b-a788-ad5e01f26a33
- Landers, M. (2025). Adapting to the Unsanctioned Use of AI-Supported Technologies in Student Assessments. Higher Education for the Future, 12(1), 76-96. https://doi.org/10.1177/23476311241300608
- Lee, C. T., Ling-Yen, P., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329-1356. https://doi.org/https://doi.org/10.1108/INTR-01-2021-0030
- Lei, S. I., Liu, G., Shen, H., Ye, S., & Sitou, C. F. (2023). An Integrated Model of Customers’ Intention to Reuse Information Service: What’s New for Conversational Agents? Tourism Analysis, 28(4), 527-543. https://doi.org/https://doi.org/10.3727/108354223X16829171933930
- Ltifi, M. (2023). Trust in the chatbot: a semi-human relationship. Future Business Journal, 9(1), 109. https://doi.org/https://doi.org/10.1186/s43093-023-00288-z
- Mahadi Hasan, M., Abba, Y. u., Adeyinka-Ojo, S., Sarkar, J. B., Hasan, M. T., Hoque, K., & Hwang Ha, J. (2024). Intention to use determinants of AI chatbots to improve customer relationship management efficiency. Cogent Business & Management, 11(1). https://doi.org/https://doi.org/10.1080/23311975.2024.2411445
- Memarian, B., & Doleck, T. (2023). Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI), and higher education: A systematic review. Computers and Education: Artificial Intelligence, 100152.
- Memarian, B., & Doleck, T. (2024). Human-in-the-loop in artificial intelligence in education: A review and entity-relationship (ER) analysis. Computers in Human Behavior: Artificial Humans, 2(1), 100053.
- Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4), 3005-3054.
- Mpinganjira, M., Dlodlo, N., & Idemudia, E. C. (2024). Perceived experiential value and continued use intention of e-retail chatbots. International Journal of Retail & Distribution Management, 52(13), 121-135. https://doi.org/https://doi.org/10.1108/IJRDM-04-2023-0237
- Nakao, Y., Stumpf, S., Ahmed, S., Naseer, A., & Strappelli, L. (2022). Toward involving end-users in interactive human-in-the-loop AI fairness. ACM Transactions on Interactive Intelligent Systems (TiiS), 12(3), 1-30.
- Nguyen, A., Kremantzis, M., Essien, A., Petrounias, I., & Hosseini, S. (2024). Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges. Journal of University Teaching & Learning Practice, 21(6), 1-13. https://doi.org/10.53761/caraaq92
- Payne, E. H. M., & O'Brien, C. A. (2024). The search for AI value: The role of complexity in human-AI engagement in the financial industry. Computers in Human Behavior: Artificial Humans, 2(1), 100050.
- Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226. https://doi.org/https://doi.org/10.1108/IJCHM-04-2020-0259
- Rathnayake, A. S., Nguyen, T. D. H. N., & Ahn, Y. (2025). Factors Influencing AI Chatbot Adoption in Government Administration: A Case Study of Sri Lanka's Digital Government. Administrative Sciences (2076-3387), 15(5), 157. https://doi.org/10.3390/admsci15050157
- Retzlaff, C. O., Das, S., Wayllace, C., Mousavi, P., Afshari, M., Yang, T., Saranti, A., Angerschmid, A., Taylor, M. E., & Holzinger, A. (2024). Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities. Journal of Artificial Intelligence Research, 79, 359-415.
- Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
- Silva, F. A., Shojaei, A. S., & Barbosa, B. (2023). Chatbot-Based Services: A Study on Customers’ Reuse Intention. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 457. https://doi.org/https://doi.org/10.3390/jtaer18010024
- Silva, S. C., De Cicco, R., Vlačić, B., & Maher, G. E. (2023). Using chatbots in e-retailing – how to mitigate perceived risk and enhance the flow experience. International Journal of Retail & Distribution Management, 51(3), 285-305. https://doi.org/https://doi.org/10.1108/IJRDM-05-2022-0163
- Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259.
- Yang, H. C. (2013). Bon appetit for apps: Young American consumers' acceptance of mobile applications [Article]. Journal of Computer Information Systems, 53(3), 85-95. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=87725942
