Generative artificial intelligence attitude analysis of undergraduate students and their precise improvement strategies: A differential analysis of multifactorial influences (original) (raw)

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  Article Google Scholar
• Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27–58. https://doi.org/10.1146/annurev.psych.52.1.27
  Article Google Scholar
• Ali, O., Murray, P. A., Momin, M., & Al-Anzi, F. S. (2023). The knowledge and innovation challenges of ChatGPT: A scoping review. Technology in Society, 75, 102402. https://doi.org/10.1016/j.techsoc.2023.102402
  Article Google Scholar
• Alier, M., García-Peñalvo, F., & Camba, J. D. (2024). Generative artificial intelligence in education: from deceptive to disruptive. https://doi.org/10.9781/ijimai.2024.02.011
• Altares-López, S., Bengochea-Guevara, J. M., Ranz, C., Montes, H., & Ribeiro, A. (2024). Generative AI: The power of the new education. arXiv preprint arXiv:2405.13487. https://doi.org/10.48550/arXiv.2405.13487
• Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954. https://doi.org/10.1016/j.chb.2019.03.018
  Article Google Scholar
• Ashworth, J., & Evans, J. L. (2001). Modeling student subject choice at secondary and tertiary level: A cross-section study. The Journal of Economic Education, 32(4), 311–320. https://doi.org/10.1080/00220480109596111
  Article Google Scholar
• Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983. https://doi.org/10.3390/su151712983
• Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.2139/ssrn.4337484
  Article Google Scholar
• Bandura, A. (2012). Cultivate self‐efficacy for personal and organizational effectiveness. Handbook of principles of organizational behavior: indispensable knowledge for evidence‐based management, 179–200. https://doi.org/10.1002/9781119206422.ch10
• Bartlett, M. S. (1954). A note on the multiplying factors for various χ 2 approximations. Journal of the Royal Statistical Society. Series B (Methodological), 296–298.
• Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2–3), 153–192. https://doi.org/10.1080/08993408.2014.963363
  Article Google Scholar
• Burbach, L., Nakayama, J., Plettenberg, N., Ziefle, M., & Valdez, A. C. (2018). User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms. In Proceedings of the 12th ACM Conference on Recommender systems (pp. 306–310).
• Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. https://doi.org/10.1016/j.techfore.2024.123247
  Article Google Scholar
• Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Sage publications.
• Carter, C. (2023). Technoracism: The Inherent Racism Within AI and How It Affects People of Color (Doctoral dissertation, Elizabeth City State University).
• Casillas, A., Robbins, S., Allen, J., Kuo, Y.-L., Hanson, M. A., & Schmeiser, C. (2012). Predicting early academic failure in high school from prior academic achievement, psychosocial characteristics, and behavior. Journal of Educational Psychology, 104(2), 407. https://doi.org/10.1037/a0027180
  Article Google Scholar
• Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
  Article Google Scholar
• 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), 43. https://doi.org/10.1186/s41239-023-00411-8
  Article Google Scholar
• Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4
  Article Google Scholar
• Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Procedia CIRP, 119, 84–90. https://doi.org/10.1016/j.procir.2023.05.002
  Article Google Scholar
• DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. Sage publications.
• Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
• Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
  Article Google Scholar
• Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), 57. https://doi.org/10.1186/s41239-022-00362-6
  Article Google Scholar
• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272. https://doi.org/10.1037/1082-989X.4.3.272
  Article Google Scholar
• Fakhri, M. M., Ahmar, A. S., Isma, A., Rosidah, R., & Fadhilatunisa, D. (2024). Exploring Generative AI Tools Frequency: Impacts on Attitude, Satisfaction, and Competency in Achieving Higher Education Learning Goals. EduLine: Journal of Education and Learning Innovation, 4(1), 196–208. https://doi.org/10.35877/454RI.eduline2592
• Grolnick, W. S. (2002). The psychology of parental control: How well-meant parenting backfires. Psychology Press.
• Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers & Education, 203, 104862. https://doi.org/10.1016/j.compedu.2023.104862
  Article Google Scholar
• Gupta, S., Abbas, A. F., & Srivastava, R. (2022). Technology Acceptance Model (TAM): A bibliometric analysis from inception. Journal of Telecommunications and the Digital Economy, 10(3), 77–106. https://search.informit.org/doi/10.3316/informit.664054795107308. Accessed 20 July 2024.
• Hartnett, M., Brown, C., Forbes, D., Gedera, D., & Datt, A. (2023). Enhanced or diminished attitudes: University students’ agency. Computers & Education, 198, 104773. https://doi.org/10.1016/j.compedu.2023.104773
  Article Google Scholar
• Hashem, R., Ali, N., El Zein, F., Fidalgo, P., & Khurma, O. A. (2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research & Practice in Technology Enhanced Learning, 19. https://doi.org/10.58459/rptel.2024.19023
• Hernández de la Hera, J. M., Morales-Rodríguez, F. M., Rodríguez-Gobiet, J. P., & Martínez-Ramón, J. P. (2023). Attitudes toward mathematics/statistics, anxiety, self-efficacy and academic performance: An artificial neural network. Frontiers in Psychology, 14, 1214892. https://doi.org/10.3389/fpsyg.2023.1214892
  Article Google Scholar
• Ikkatai, Y., Hartwig, T., Takanashi, N., & Yokoyama, H. M. (2022). Octagon measurement: Public attitudes toward AI ethics. International Journal of Human-Computer Interaction, 38(17), 1589–1606. https://doi.org/10.1080/10447318.2021.2009669
  Article Google Scholar
• Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Education and Information Technologies, 27(8), 11635–11667. https://doi.org/10.1007/s10639-022-11086-5
  Article Google Scholar
• Kamalov, F., SantandreuCalonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
  Article Google Scholar
• Kang, S., Choi, Y., & Kim, B. (2024). Impact of motivation factors for using generative AI services on continuous use intention: Mediating trust and acceptance attitude. Social Sciences, 13(9), 475. https://doi.org/10.3390/socsci13090475
  Article Google Scholar
• Kennedy, D. B. (1992). Classification techniques in accounting research: Empirical evidence of comparative performance. Contemporary Accounting Research, 8(2), 419–442. https://doi.org/10.1111/j.1911-3846.1992.tb00853.x
  Article Google Scholar
• Kim, S.-W., & Lee, Y. (2020). Attitudes toward artificial intelligence of high school students’ in Korea. Journal of the Korea Convergence Society, 11(12), 1–13. https://doi.org/10.15207/JKCS.2020.11.12.001
• Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
• Koç, F. Ş. (2024). The development of listening and speaking skills in EFL via an artificially intelligent chatbot application: A quasi-experimental design study.
• Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
  Article Google Scholar
• Labonté, C., & Smith, V. R. (2022). Learning through technology in middle school classrooms: Students’ perceptions of their self-directed and collaborative learning with and without technology. Education and Information Technologies, 27(5), 6317–6332. https://doi.org/10.1007/s10639-021-10885-6
  Article Google Scholar
• Lee, M. K., Grgić-Hlača, N., Tschantz, M. C., Binns, R., Weller, A., Carney, M., & Inkpen, K. (2020, April). Human-centered approaches to fair and responsible AI. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–8).
• Li, H. (2023). Effects of a ChatGPT-based flipped learning guiding approach on learners’ courseware project performances and perceptions. Australasian Journal of Educational Technology, 39(5), 40–58. https://doi.org/10.14742/ajet.8923
• Mahapatra, S. (2024). Impact of ChatGPT on ESL students’ academic writing skills: A mixed methods intervention study. Smart Learning Environments, 11(1), 9. https://doi.org/10.1186/s40561-024-00295-9
  Article Google Scholar
• Mason, S. L., & Rich, P. J. (2020). Development and analysis of the elementary student coding attitudes survey. Computers & Education, 153, 103898. https://doi.org/10.1016/j.compedu.2020.103898
  Article Google Scholar
• Nikolopoulou, K. (2024). Generative Artificial Intelligence in Higher Education: Exploring ways of harnessing pedagogical Practices with the assistance of ChatGPT. International Journal of Changes in Education, 1(2), 103–111. https://doi.org/10.47852/bonviewIJCE42022489
• Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 564294. https://doi.org/10.3389/fpsyg.2020.564294
  Article Google Scholar
• Papastergiou, M. (2008). Are computer science and information technology still masculine fields? High school students’ perceptions and career choices. Computers & Education, 51(2), 594–608. https://doi.org/10.1016/j.compedu.2007.06.009
  Article Google Scholar
• Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology, 156(1), 68–94. https://doi.org/10.1080/00223980.2021.2012109
  Article Google Scholar
• Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 3034. https://doi.org/10.3390/su16073034
  Article Google Scholar
• Qureshi, B. (2023). Exploring the use of chatgpt as a tool for learning and assessment in undergraduate computer science curriculum: Opportunities and challenges. arXiv preprint arXiv:2304.11214. https://doi.org/10.48550/arXiv.2304.11214
• Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
• Sallam, M. (2023). The utility of ChatGPT as an example of large language models in healthcare education, research and practice: Systematic review on the future perspectives and potential limitations. MedRxiv, 2023.2002. 2019.23286155. https://doi.org/10.1101/2023.02.19.23286155
• Salloum, S. A., Aljanada, R. A., Alfaisal, A. M., Al Saidat, M. R., & Alfaisal, R. (2024). Exploring the Acceptance of ChatGPT for Translation: An Extended TAM Model Approach. In Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom (pp. 527–542). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52280-2_33
• Sansone, C., Smith, J. L., Thoman, D. B., & MacNamara, A. (2012). Regulating interest when learning online: Potential motivation and performance trade-offs. The Internet and Higher Education, 15(3), 141–149. https://doi.org/10.1016/j.iheduc.2011.10.004
  Article Google Scholar
• Sapnas, K. G., & Zeller, R. A. (2002). Minimizing sample size when using exploratory factor analysis for measurement. Journal of Nursing Measurement, 10(2), 135–154. https://doi.org/10.1891/jnum.10.2.135.52552
  Article Google Scholar
• Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
  Article Google Scholar
• Schiefele, U. (1999). Interest and learning from text. Scientific Studies of Reading, 3(3), 257–279. https://doi.org/10.1207/s1532799xssr0303_4
  Article Google Scholar
• Shaengchart, Y. (2023). A conceptual review of TAM and ChatGPT usage intentions among higher education students. Advance Knowledge for Executives, 2(3), 1–7. https://ssrn.com/abstract=4581231. Accessed 20 July 2024.
• Shashaani, L., & Khalili, A. (2001). Gender and computers: Similarities and differences in Iranian college students’ attitudes toward computers. Computers & Education, 37(3–4), 363–375. https://doi.org/10.1016/S0360-1315(01)00059-8
  Article Google Scholar
• Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11.https://doi.org/10.12691/AJAMS-9-1-2
• Solomovich, L., & Abraham, V. (2024). Exploring the influence of ChatGPT on tourism behavior using the technology acceptance model. Tourism Review. https://doi.org/10.1108/TR-10-2023-0697
  Article Google Scholar
• Steinmetz, H., Knappstein, M., Ajzen, I., Schmidt, P., & Kabst, R. (2016). How effective are behavior change interventions based on the theory of planned behavior? Zeitschrift Für Psychologie. https://doi.org/10.1027/2151-2604/a000255
  Article Google Scholar
• Sun, L., Hu, L., & Zhou, D. (2022a). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education, 181, 104457. https://doi.org/10.1016/j.compedu.2022.104457
  Article Google Scholar
• Sun, L., Hu, L., & Zhou, D. (2022b). Single or combined? A study on programming to promote junior high school students’ computational thinking skills. Journal of Educational Computing Research, 60(2), 283–321. https://doi.org/10.1177/07356331211035182
  Article Google Scholar
• Tate, T., Doroudi, S., Ritchie, D., Xu, Y., & Warschauer, M. (2023). Educational research and AI-generated writing: Confronting the coming tsunami. EdArXiv. January, 10. https://doi.org/10.35542/osf.io/4mec3
• Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI?. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 9795–9799). https://doi.org/10.1609/aaai.v33i01.33019795
• Vo, A., & Nguyen, H. (2024). Generative artificial intelligence and ChatGPT in language learning: EFL students' perceptions of technology acceptance. Journal of University Teaching and Learning Practice, 21(6), 199–218. https://doi.org/10.53761/fr1rkj58
• Williams, C. (2023). Hype, or the future of learning and teaching? 3 Limits to AI’s ability to write student essays.
• Yan, W., Nakajima, T., & Sawada, R. (2024). Benefits and Challenges of Collaboration between Students and Conversational Generative Artificial Intelligence in Programming Learning: An Empirical Case Study. Education Sciences, 14(4), 433. https://doi.org/10.3390/educsci14040433
  Article Google Scholar
• Yilmaz, R., & Yilmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
  Article Google Scholar
• Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562–590. https://doi.org/10.1177/0735633115608444
  Article Google Scholar
• Zhou, H., & Zhou, D. (2024, April). Transformation of Vocational Education Based on Generative Artificial Intelligence: Impact, Opportunity and Countermeasures. In Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–26, 2023, Zhengzhou, China. https://doi.org/10.4108/eai.24-11-2023.2343636
• Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1259531. https://doi.org/10.3389/fpsyg.2023.1259531
  Article Google Scholar

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