Moderators of academic performance on the use of generative artificial intelligence (original) (raw)

References

• Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Tirado, S., K (2024). Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https://doi.org/10.1186/s40359-024-01764-z
  Article Google Scholar
• Algerafi, M. A. M., Zhou, Y., Alfadda, H., & Wijaya, T. T. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access : Practical Innovations, Open Solutions, 11, 99752–99764. https://doi.org/10.1109/ACCESS.2023.3314499
  Article Google Scholar
• Balabdaoui, F., Dittmann-Domenichini, N., Grosse, H., Schlienger, C., & Kortemeyer, G. (2024). A survey on students’ use of AI at a technical university. Discover Education. https://doi.org/10.1007/s44217-024-00136-4
  Article Google Scholar
• Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 72(4), 2401–2416. https://doi.org/10.1007/s11423-024-10366-w
  Article Google Scholar
• Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
  Article Google Scholar
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
  Article Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  Article Google Scholar
• Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023). Analyzing the students’ views, concerns, and perceived ethics about ChatGPT usage. Computers And Education: Artificial Intelligence, 5, Article 100180. https://doi.org/10.1016/j.caeai.2023.100180
  Article Google Scholar
• Festinger, L. (1957). A Theory of Cognitive Dissonance. https://doi.org/10.1515/9781503620766
  Article Google Scholar
• Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and FsQCA. International Journal of Human-Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495
  Article Google Scholar
• Gustilo, L., Ong, E., & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: Exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity. https://doi.org/10.1007/s40979-024-00153-8
  Article Google Scholar
• Hair, J., Black, W. C., Babin, B., Anderson, R., & Tatham, R. (2014). Multivariate data analysis. Pearson Education Limited.
  Google Scholar
• Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J. (2024). Adopting AI: How familiarity breeds both trust and contempt. AI and Society, 39(4), 1721–1735. https://doi.org/10.1007/s00146-023-01666-5
  Article Google Scholar
• Jang, M. (2024). AI literacy and intention to use text-based GenAI for learning: The case of business students in Korea. Informatics. https://doi.org/10.3390/informatics11030054
  Article Google Scholar
• Kampa, R. K., Padhan, D. K., Karna, N., & Gouda, J. (2024). Identifying the factors influencing plagiarism in higher education: An evidence-based review of the literature. Accountability in Research. https://doi.org/10.1080/08989621.2024.2311212
  Article Google Scholar
• Khalaf, M. A. (2024). Does attitude towards plagiarism predict aigiarism using chatgpt?? AI and Ethics. https://doi.org/10.1007/s43681-024-00426-5
  Article Google Scholar
• Kim, J., & Kweon, S. O. (2023). Effects of English proficiency on motivational regulation in a videoconference-based EFL speaking class. Education and Information Technologies, 28(7), 8401–8422. https://doi.org/10.1007/s10639-022-11374-0
  Article Google Scholar
• Kučinskas, G. (2024). Negative effects of revealing AI involvement in products: Mediation by authenticity and risk, moderation by trust in AI and familiarity with AI. Social Science Research Network. https://doi.org/10.2139/ssrn.4805186
  Article Google Scholar
• Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.
  Google Scholar
• Luo, T., Muljana, P. S., Ren, X., & Young, D. (2024). Exploring instructional designers’ utilization and perspectives on generative AI tools: A mixed methods study. Educational Technology Research and Development. https://doi.org/10.1007/s11423-024-10437-y
  Article Google Scholar
• Ni, A., & Cheung, A. (2023). Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies, 28(3), 3191–3216. https://doi.org/10.1007/s10639-022-11305-z
  Article Google Scholar
• Nikoulina, A., & Caroni, A. (2024). Familiarity, use, and perception of AI-powered tools in higher education. International Journal of Management Knowledge and Learning, 13, 169–181. https://doi.org/10.53615/2232-5697.13.169-181
  Article Google Scholar
• Okyar, H. (2023). University-level EFL students’ views on learning English online: A qualitative study. Education and Information Technologies, 28(1), 81–107. https://doi.org/10.1007/s10639-022-11155-9
  Article Google Scholar
• Roganović, J. (2024). Familiarity with ChatGPT features modifies expectations and learning outcomes of dental students. International Dental Journal, 74(6), 1456–1462. https://doi.org/10.1016/j.identj.2024.04.012
  Article Google Scholar
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7
  Article Google Scholar
• 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
  Article Google Scholar
• Venkatesh, V., Thong, J., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
  Article Google Scholar
• Williamson, O. E. (1985). The economic intstitutions of capitalism. Simon and Schuster. https://doi.org/10.1007/978-3-8349-9320-5_6
• Xia, Q., Chiu, T. K. F., & Chai, C. S. (2023). The moderating effects of gender and need satisfaction on self-regulated learning through artificial intelligence (AI). Education and Information Technologies, 28(7), 8691–8713. https://doi.org/10.1007/s10639-022-11547-x
  Article Google Scholar
• Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(5), 459–482. https://doi.org/10.1002/cne.920180503
  Article Google Scholar
• Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-024-00453-6
  Article Google Scholar
• Zhang, X., Jiang, H., Qiao, Z., & Li, P. (2024). Students’ Response to ChatGPT: An Adaptive Technology-To-Performance Model. Journal of Computer Information Systems, 1–18. https://doi.org/10.1080/08874417.2024.2386546
• Zhou, C. (2023). Integration of modern technologies in higher education on the example of artificial intelligence use. Education and Information Technologies, 28(4), 3893–3910. https://doi.org/10.1007/s10639-022-11309-9
  Article Google Scholar
• Zhu, L., Lai, Y., Mou, W., Zhang, H., Lin, A., Qi, C., Yang, T., Xu, L., Zhang, J., & Luo, P. (2024a). ChatGPT’s ability to generate realistic experimental images poses a new challenge to academic integrity. Journal of Hematology & Oncology. https://doi.org/10.1186/s13045-024-01543-8
  Article Google Scholar
• Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2024b). Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university students’ use of generative AI products?? International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2323277
  Article Google Scholar

Download references