An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI

Abdelwahab, H. R., Rauf, A., & Chen, D. (2023). Business students’ perceptions of Dutch higher educational institutions in preparing them for artificial intelligence work environments. Industry and Higher Education, 37(1), 22–34. https://doi.org/10.1177/09504222221087614

Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036

Agrawal, A., Gans, J., & Goldfarb, A. (2022). Chatgpt and how AI disrupts industries. Harvard Business Review. Retrieved February 28, 2023, from https://hbr.org/2022/12/chatgpt-and-how-ai-disrupts-industries

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior1. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x

Backfisch, I., Lachner, A., Stürmer, K., & Scheiter, K. (2021a). Variability of teachers’ technology integration in the classroom : A matter of utility ! Computers & Education, 166, 104159. https://doi.org/10.1016/j.compedu.2021.104159

Backfisch, I., Scherer, R., Siddiq, F., Lachner, A., & Scheiter, K. (2021b). Teachers’ technology use for teaching: Comparing two explanatory mechanisms. Teaching and Teacher Education, 104, 103390. https://doi.org/10.1016/j.tate.2021.103390

Bai, L., Liu, X., & Su, J. (2023). ChatGPT: The cognitive effects on learning and memory. Brain-X, 1(3), e30. https://doi.org/10.1002/brx2.30

Ball, C., Huang, K.-T., Rikard, R. V., & Cotten, S. R. (2019). The emotional costs of computers : An expectancy-value theory analysis of predominantly low-socioeconomic status minority students’ STEM attitudes. Information, Communication & Society, 22(1), 105–128. https://doi.org/10.1080/1369118X.2017.1355403

Bonsu, E., & Baffour-Koduah, D. (2023). From the consumers’ side: Determining students’ perception and intention to use chatgptin Ghanaian higher education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4387107

Brown, T. A. (2006). Confirmatory factor analysis for applied research. The Guilford Press.

Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101.

Chan, C. K. Y. (2022). Assessment for experiential learning (1st ed.). Routledge. https://doi.org/10.4324/9781003018391

Chan, C. K. Y. (2023b). Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of 'AI-giarism'. (Under review).

Chan, C. K. Y. (2023a). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 1–25. 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. https://doi.org/10.1186/s41239-023-00411-8

Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environment, 10, 60. https://doi.org/10.1186/s40561-023-00269-3

Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233

Chen, J.-L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57(2), 1501–1511. https://doi.org/10.1016/j.compedu.2011.02.009

Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022). Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Frontiers in Medicine, 9, 990604. https://doi.org/10.3389/fmed.2022.990604

Cheng, S.-L., Lu, L., Xie, K., & Vongkulluksn, V. W. (2020). Understanding teacher technology integration from expectancy-value perspectives. Teaching and Teacher Education, 91, 103062. https://doi.org/10.1016/j.tate.2020.103062

Chui, M., Roberts, R., & Yee, L. (2022). Generative AI is here: How tools like ChatGPT could change your business. McKinsey & Company. Retrieved February 28, 2023, from https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business

Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of chatgpt. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8

Dahlkemper, M. N., Lahme, S. Z., & Klein, P. (2023). How do physics students evaluate ChatGPT responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality. https://arxiv.org/abs/2304.05906

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

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), 272–299. https://doi.org/10.1037/1082-989X.4.3.272

Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. https://doi.org/10.1016/j.cedpsych.2015.03.002

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Fu, S., Gu, H., & Yang, B. (2020). The affordances of AI-enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China. British Journal of Educational Technology, 51(5), 1674–1692. https://doi.org/10.1111/bjet.12995

Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 2(1), 37–56. https://doi.org/10.1177/14757257211037149

Gaskin, J., James, M., Lim, J, & Steed, J. (2023). Master Validity Tool. AMOS Plugin. Gaskination's StatWiki.

Haensch, A. C., Ball, S., Herklotz, M., & Kreuter, F. (2023). Seeing ChatGPT through students’ eyes: An analysis of tiktok data. https://arxiv.org/abs/2303.05349

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis. Prentice Hall.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hu, Y.-H. (2022). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037. https://doi.org/10.1007/s10639-021-10664-3

Ifinedo, P. (2018). Roles of perceived fit and perceived individual learning support in students’ weblogs continuance usage intention. International Journal of Educational Technology in Higher Education, 15(1), 1–18. https://doi.org/10.1186/s41239-018-0092-3

Iyer, L. S. (2021). AI enabled applications towards Intelligent Transportation. Transportation Engineering, 5, 100083. https://doi.org/10.1016/j.treng.2021.100083

Jeffrey, T. (2020). Understanding college student perceptions of artificial intelligence. Systemics, Cybernetics and Informatics, 18(2), 8–13.

Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human-Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227

Königstorfer, F., & Thalmann, S. (2020). Applications of artificial intelligence in commercial banks—A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 100352. https://doi.org/10.1016/j.jbef.2020.100352

Kumar, V. V. R., & Raman, R. (2022). Student perceptions on artificial intelligence (AI) in higher education. In International Symposium on... [Details of the conference proceedings]. https://doi.org/10.1109/ISEC54952.2022.10025165

Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Educational Information Technology, 26(6), 6629–6649. https://doi.org/10.1007/s10639-021-10465-8

Malechwanzi, J. M., Shen, H., & Mbeke, C. (2016). Policies of access and quality of higher education in China and Kenya: A comparative study. Cogent Education, 3(1), 1201990.

Mok, L. (2023). Hong Kong Education University approves use of chatgpt in coursework despite bans by two other schools. Hong Kong Free Press HKFP. https://hongkongfp.com/2023/03/24/hong-kong-education-university-approves-use-of-chatgpt-in-coursework-despite-bans-by-two-other-schools/

Mucharraz, Y., Cano, Y., Venuti, F., & Herrera Martinez, R. (2023). ChatGPT and AI text generators: Should academia adapt or resist? Harvard Business School. Retrieved February 28, 2023, from https://www.hbsp.harvard.edu/inspiring-minds/chatgpt-and-ai-text-generators-should-academia-adapt-or-resist

Nah, F.F.-H., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814

Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487

Pimentel, J. L. (2010). A note on the usage of Likert Scaling for research data analysis. USM R&d Journal, 18(2), 109–112.

Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468

Raman, R., Mandal, S., & Das, P., et al. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study [Preprint version 1]. Research Square. https://doi.org/10.21203/rs.3.rs-2734142/v1

Ranellucci, J., Rosenberg, J. M., & Poitras, E. G. (2020). Exploring pre-service teachers’ use of technology: The technology acceptance model and expectancy-value theory. Journal of Computer Assisted Learning, 36(6), 810–824. https://doi.org/10.1111/jcal.12459

Regmi, K., & Jones, L. (2020). A systematic review of the factors—enablers and barriers—affecting e-learning in health sciences education. BMC Medical Education, 20(1). https://doi.org/10.1186/s12909-020-02007-6

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.

Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23–74.

Schulman, J., Zoph, B., Kim, C., Hilton, J., Menick, J., Weng, J., Uribe, J. F. C., Fedus, L., Metz, L.,Pokorny, M., Lopes, R. G., Zhao, S., Vijayvergiya, A., Sigler, E., Perelman, A., Voss, C., Heaton, M., Parish, J., Cummings, R. N., & Ryder, N. (2022). ChatGPT: Optimizing language models for dialogue. https://openai.com/blog/chatgpt/

Sin, H. X., Tan, L., & McPherson, G. E. (2022). A PRISMA review of expectancy-value theory in music contexts. Psychology of Music, 50(3), 976–992. https://doi.org/10.1177/03057356211024344

Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893–898. https://doi.org/10.1016/j.paid.2006.09.017

Stüber, J. (2018). Barriers of Digital Technologies in Higher Education: A Teachers’ Perspective from a Swedish University [Mater Thesis, Linnaeus University] diva-portal. https://www.diva-portal.org/smash/get/diva2:1201871/FULLTEXT01.pdf.

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006

Topol, E. J. (2019). High-performance medicine: The convergence of human and Artificial Intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

UNESCO. (2023). Guidance for generative AI in education and research. Unesdoc.unesco.org. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wang, F., King, R. B., Chai, C. S., & Zhou, Y. (2023). University students’ intentions to learn artificial intelligence: The roles of supportive environments and expectancy–value beliefs. International Journal of Educational Technology in Higher Education, 20(1), 51. https://doi.org/10.1186/s41239-023-00417-2

Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49–78. https://doi.org/10.1007/bf02209024

Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12(3), 265–310. https://doi.org/10.1016/0273-2297(92)90011-P

Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (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), 39. https://doi.org/10.1186/s41239-019-0171-0

Zhai, X., Chu X., Sing Chai, C., Yung Jong, M. S., Istenic, A., Spector, M., Liu J.-B., Yuan J., Li, Y. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021(8812542), 2021. https://doi.org/10.1155/2021/8812542

Zou, B., Liviero, S., Hao, M., & Wei, C. (2020). Artificial Intelligence Technology for EAP Speaking Skills: Student Perceptions of Opportunities and Challenges. In: Freiermuth, M.R., Zarrinabadi, N. (eds) Technology and the Psychology of Second Language Learners and Users. New Language Learning and Teaching Environments. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-34212-8_17