Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model
DOI:
https://doi.org/10.54536/ajase.v3i1.2510Keywords:
Attitude Toward Artificial Intelligence, Behavioral Toward AI, UTAUT Model, Partial Least Square Structural Equation Modeling (PLS-SEM), PhilippinesAbstract
This quantitative study using Partial Least Square Structural Equation Modeling (PLS-SEM) examined a structural model of the attitudes and behaviors of university students toward AI in higher education. The results obtained using SmartPLS 4.0 indicate that the constructs exhibit validity and reliability (λ ≥ 0.708, α=0.767-0.948, AVE=0.584-0.777, HTMT=< 3.3). Further, the analysis of the hypothesized extended Unified Theory of Acceptance and Use (UTAUT) model reveals that AI Awareness significantly impacts Attitude toward AI (β = 0.156, p = 0.003) and Behavioral Intention to Use AI (BIU) (β = 0.337, p < 0.001). AI Trust also significantly influences Attitude toward AI (β = 0.366, p < 0.001) and BIU-AI (β = 0.173, p = 0.007). Additionally, Attitude toward AI is a strong predictor of BIU-AI (β = 0.457, p < 0.001). Social Influence significantly affects Attitude toward AI (β = 0.21, p < 0.001), while Effort Expectancy and Performance Expectancy do not show significant effects in this context. The link between Facilitating Conditions and BIU-AI is also insignificant. The model explained a substantial portion of the variance in attitude (R2 =0.612) and behavior (R2 =0.710). Fit indices indicate good model fit, and predictive relevance metrics were satisfactory.
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Alam, A. (2021, December). Should robots replace teachers? Mobilisation of AI and learning analytics in education. In 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3) (pp. 1-12). IEEE. https://doi.org/10.1109/ICAC353642.2021.9697300.
Alzahrani, L. (2023). Analyzing students’ attitudes and behavior toward artificial intelligence technologies in higher education. International Journal of Recent Technology and Engineering (IJRTE), 11(6), 65-73.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
Barton, D., Woetzel, J., Seong, J., & Tian, Q. (2017). Artificial intelligence: Implications for China (Discussion Paper). Retrieved from McKinsey&Company website: http://dln.jaipuria.ac.in:8080/jspui/bitstream/123456789/1888/1/MGI-Artificial-intelligence-implications-for-China.pdf
Becker, J., Ringle, C. M., Sarstedt, M., & Völckner, F. (2014). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643–659. https://doi.org/10.1007/s11002-014-9299-9
Beig, S., & Qasim, S. H. (2023). Attitude towards artificial intelligence: Change in the educational era. International Journal of Creative Research Thoughts, 11(8), 718–b721. http://ijcrt.org/viewfull.php?&p_id=IJCRT2308192
Berdiyorova, I., Akhtamova, P., & Ganiev, I.M. (2021). Artificial intelligence in various industries. Proceedings from International scientific-practical conference, 2021 March 25-26 (pp. 186-193).
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modeling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
Chen, M. Siu-Yung, M., Chai, C.S., Zheng, C., & Park, M.Y. (2021). A pilot study of students’ behavioral intention to use AI for language learning in higher education. Proceedings of International Symposium on Educational Technology (ISET) Tokai Nagoya Japan (pp. 182-184). https://doi.org/10.1109/ISET52350.2021.00045.
Choung, H., David, P., & Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543
Cockburn, I.M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (Working Paper 24449). https://www.nber.org/system/files/working_papers/w24449/w24449.pdf
Cook, J. (2023, November 21). How can we make AI adoption more human? https://www.linkedin.com/pulse/how-can-we-make-ai-adoption-more-human-jo-cook-zl0sf/
Creswell, J. W., & Creswell, J. D. (2023). Research design: “Qualitative, Quantitative, and Mixed Methods Approaches” (6th ed.). SAGE Publications.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20 (22). https://doi.org/10.1186/s41239-023-00392-8
Emon, M. M. H., Hassan, F., Nahid, M. H., & Rattanawiboonsom, V. (2023). Predicting Adoption Intention of Artificial Intelligence CHATGPT. The AIUB Journal of Science and Engineering, 22(2), 189–199. https://doi.org/10.53799/ajse.v22i2.797
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 chat GPT usage. Computers and Education: Artificial Intelligence, 5, 1-8. https://doi.org/10.1016/j.caeai.2023.100180.
Fornell CG and Larcker DF. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research 18(1), 39-50.
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, 21(1), 37-56. https://doi.org/10.1177/14757257211037149
Geetha, R. & BhanuSree Reddy, D. (2018). Recruitment through artificial intelligence: A conceptual study. International Journal of Mechanical Engineering and Technology, 9(7), 63–70. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=7
Ghotbi, N., Ho, T., & Mantello, P. (2022). The attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society, 37(2), 1-8. https://doi.org/10.1007/s00146-021-01168-2
Gold, A. H., & Malhotra, A. H. (2001). Title of the article. Journal of Management Information Systems, 18, 185-214.
Hair JF, Hult GTM, Ringle CM, et al. (2017a) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Thousand Oaks, CA: Sage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org//10.1108/EBR-11-2018-0203
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE Publications, Incorporated.
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE Publications, Incorporated.
Hall, J. & Pesenti, J. (2017). Growing the artificial intelligence industry in the UK. GOV.UK. https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk
Hamid, M. R. A., Sami, W., & Sidek, M. H. M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Hassani, H., Silva, E.S., Unger, S., Ta jMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI 2020, pp. 1, 143–155. https://doi.org/10.3390/ai1020008
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Journal of the Academy of Marketing Science, 20, 227–319.
Hilale, N. (2021). The evolution of artificial intelligence (AI) and its impact on women: how it nurtures discrimination towards women and strengthens gender inequality. International Journal of Human Rights, 1(2), 141–150. http://www.humanrights.periodikos.com.br/article/61489565a9539526b5418543
Hulland, J. (1999). Title of the Article. Strategic Management Journal, pp. 20, 195–204.
Humble, N., Mozelius, P. The threat, hype, and promise of artificial intelligence in education. Discover Artificial Intelligence, 2(22). https://doi.org/10.1007/s44163-022-00039-z
Intiser, R., Nahid, M. H., Anwar, M. A., & Nahar, R. (2023). Adoption of AI-powered web-based English writing assistance software: An Exploratory Study. AIUB Journal of Business and Economics, 20(1), 90–101. https://ajbe.aiub.edu/index.php/ajbe/article/view/194
Isaac, O., Abdullah, Z., Ramayah, T. and Mutahar, A.M. (2017). “Internet usage, user satisfaction, task-technology fit, and performance impact among public sector employees in Yemen,” International Journal of Information and Learning Technology, 34(3), 210–241. https://doi.org/10.1108/IJILT-11-2016-0051
Jarrett, A, Choo, K.-K. (2021). The impact of automation and artificial intelligence on digital forensics. WIREs Forensic Science, pp. 1–17. https://doi.org/10.1002/wfs2.1418
Jindal, H., Kumar, D., Ishika, Kumar, S., & Kumar, R. (2021). Role of artificial intelligence in the distinct sector: a study. Asian Journal of Computer Science and Technology, 10(1), 18–28. https://doi.org/10.51983/ajcst-2021.10.1.2696
Kairu, C. (2020). Students’ attitude towards the use of artificial intelligence and machine learning to measure classroom engagement activities. In Association for the Advancement of Computing in Education (AACE) (Ed.). Proceedings of EdMedia + Innovate Learning, 23 June 2020 (pp. 793–802). https://www.learntechlib.org/primary/p/217382/.
Kandoth, S. ., & Kushe Shekhar, S. . (2022). Social influence and intention to use AI: the role of personal innovativeness and perceived trust using the parallel mediation model. Forum Scientiae Oeconomia, 10(3), 131–150. https://doi.org/10.23762/FSO_VOL10_NO3_7
Kavitha, V., & Lohani, R. (2019). A critical study on the use of artificial intelligence, e-learning technology, and tools to enhance the learner’s experience. Cluster Computing, 22, 6985–6989. https://doi.org/10.1007/s10586-018-2017-2
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence. International Journal of Human–Computer Interaction. https:// doi.org/10.1080/10447318.2022.2151730
Khanagar, S., Al-ehaideb A., Maganur, P., Vishwanathaiah, S., Patil, S., Baeshen, H., S., S., & Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences, 16(1), 508–522. https://doi.org/10.1016/j.jds.2020.06.019.
Kim, J. M. (2017). Study on intention and attitude of using artificial intelligence technology in healthcare. Journal of Convergence for Information Technology, 7(4), 53-60. https://doi.org/10.22156/CS4SMB.2017.7.4.053
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: The Guilford Press.
Kock N and Hadaya P. (2018). Minimum Sample Size Estimation in PLS-SEM: The Inverse Square Root and Gamma-Exponential Methods. Information Systems Journal 28(1), 227- 261.
Kushmar, L.V., Vornachev, A.O Korobova.I.O., & Kaida,N.O. (2022). Artificial Intelligence in Language Learning: What Are We Afraid of? Arab World English Journal (AWEJ) Special Issue on CALL, (8), 262-273. https://dx.doi.org/10.24093/awej/call8.18
Liehner, G.L., Biermann, H., & Hick, A., Brauner, P. & Ziefle, M. (2023). Perceptions, attitudes, and trust towards artificial intelligence — an assessment of the public opinion. Artificial Intelligence and Social Computing, 72, 32–41. https://doi.org/10.54941/ahfe1003271
Lin, H.C., Ho, C.F., & Yang, H. (2021). Understanding the adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model. Home International Journal of Mobile Learning and Organisation, 16(1), 79-94. https://www.inderscienceonline.com/doi/epdf/10.1504/IJMLO.2022.119966
Loble, L., Creenaune, T., & Hayes, J. (2017). Future frontiers education for an AI world. Melbourne University Press.
Lu, H., Li, Y., Chen, M., Kim, Hyoungseop, K., & Serikawa, S. (2017). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23, 368–375. https://doi.org/10.1007/s11036-017-0932-8
Marrone, R., Taddeo, V., & Hill, G. (2022). Creativity and artificial intelligence—a student perspective. Journal of Intelligence, 10(3), 1-11. https://doi.org/10.3390/jintelligence10030065
Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280. (PDF) How Collinearity Affects Mixture Regression Results.
Mintz, Y. & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81. https://doi.org/10.1080/13645706.2019.1575882
Mohamed, S.S.A. & Alian, E.M.I. (2023). Students’ attitudes toward using a chatbot in EFL Learning. Arab World English Journal (AWEJ), 14(3), 15–27. https://dx.doi.org/10.24093/awej/vol14no3.2
Obenza, B. N., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2023b). University Students’ Perception and Use of ChatGPT Generative Artificial Intelligence (AI) in Higher Education. International Journal of Human Computing Studies, 5(12), 5–18. https://doi.org/10.5281/zenodo.10360697
Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., Granado, L. B., Loreco, K. C. A., Matugas, L. P., Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & Caangay, R. B. R. (2023a). The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students. International Journal of Metaverse, 2(1), 1–10. https://doi.org/10.54536/ijm.v2i1.2286
Obenza, B. N., Go, L. E., Francisco, J. A. M., Buit, E. E. T., Mariano, F. V. B., Cuizon Jr, H. L., Cagabhion, A. J. D., & Agbulos, K. A. J. L. (2024). The Nexus between Cognitive Absorption and AI Literacy of College Students as Moderated by Sex. American Journal of Smart Technology and Solutions, 3(1), 32–39. https://doi.org/10.54536/ajsts.v3i1.2603
Olhede, S. C., & Wolfe, P. J. (2018). The AI Spring of 2018. Significance, 15(3), 6–7. https://doi.org/10.1111/j.1740-9713.2018.01140.x
Pande, K., Sonawane, S., Jadhava, V., & Malia, M. (2023). Artificial intelligence: exploring the attitude of secondary students. Journal of e-learning and knowledge society, 19(3), 43-48. https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/1135865
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R.K. (2021). Artificial intelligence in drug discovery and development. Drug Discov, 26(1), 80-93. doi: 10.1016/j.drudis.2020.10.010.
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development. United Nations Educational, Scientific and Cultural Organization, 7, 1-48. https://unesdoc.unesco.org/ark:/48223/pf0000366994
Ringle CM, Wende S and Becker J-M. (2015) SmartPLS 3. Bönningstedt: SmartPLS.
Romero-Rodriguez, J.M., Ramirez-Montoya, M.S., Buenestado-Fernández, M. & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students’ perceived usefulness. Journal of New Approaches in Education Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458
Roy, R., Babakerkhell, M.D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666-125678. doi: 10.1109/ACCESS.2022.3225555.
Saravanan, K., Sreedevi, E., & Subhamathi, V. (2017). A Review of Artificial Intelligence Systems. International Journal of Advanced Research in Computer Science, 8(9), 418–421. DOI 10.26483/ijarcs.v8i9.5095
Sarstedt M, Ringle CM and Hair JF. (2017a) Partial Least Squares Structural Equation Modeling. In: Homburg C, Klarmann M and Vomberg A (eds) Handbook of Market Research. Heidelberg: Springer.
Sarstedt M, Ringle CM, Cheah J-H, et al. (2019b). Structural Model Robustness Checks in PLS-SEM. Tourism Economics is forthcoming.
Schepman, A. & Rodway, P. (2023). The general attitude towards artificial intelligence scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
Seo, K., Tang, J., Roll, I., Fels, S., & Yong, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18 (54). https://doi.org/10.1186/s41239-021-00292-9
Shao, Z., Yuan, S., & Wang, Y., (2020). Institutional collaboration and competition in artificial intelligence. IEEE Access, 8, 69734-69741. https://doi.org/10.1109/ACCESS.2020.2986383.
Skeat, J. & Ziebell, N. (2023). University students are using AI, but not how you think. The University of Melbourne. https://pursuit.unimelb.edu.au/articles/university-students-are-using-ai-but-not-how-you-think
Slavov, V., Yotovska, K. & Asenova, A. (2023, March 11-13). Research on the attitudes of high school students toward the application of artificial intelligence in education. 19th International Conference on Mobile Learning 2023, Lisbon Portugal.
Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. SAGE Open, 12(2), 215824402211004. https://doi.org/10.1177/21582440221100463
Tahiru, F. (2021). AI in education: A systematic literature review. Journal of Cases on Information Technology (JCIT), 23(1), 1–20. DOI: 10.4018/JCIT.2021010101
Tan, L. & Ran, N. (2022). Applying artificial intelligence technology to analyze the athletes’ training under a sports training monitoring system. International Journal of Humanoid Robotics, 20(06). https://doi.org/ 10.1142/S0219843622500177
Venkatesh, M., Davis, & Davis (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425.
Venkatesh, V. & Davis, F.D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.
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.
Welding, L. (2023). Half of college students say using AI on schoolwork is cheating or plagiarism. BestColleges. https://www.bestcolleges.com/research/college-students-ai-tools-survey/
Wold HOA. (1982) Soft Modeling: The Basic Design and Some Extensions. In: Jöreskog KG and Wold HOA (eds) Systems Under Indirect Observations: Part II. Amsterdam: North- Holland, 1–54.
Xie, X. & Wang, T. (2023). Artificial intelligence: A help or threat to contemporary education. Should students be forced to think and do their tasks independently? Education and Information Technologies. https://doi.org/10.1007/s10639-023-11947-7
Yadrovskaia, M., Porksheyan, M., Petrova, A., Dudukalova, D., & Bulygin, Y. (2023). About the attitude towards artificial intelligence technologies. E3S Web of Conferences, 376, 05025. https://doi.org/10.1051/e3sconf/202337605025
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 (39). https://doi.org/10.1186/s41239-019-0171-0
Zhang, K. & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100025.
Zhou, Z., Chen, X., Li E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951
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Copyright (c) 2024 Brandon Nacua Obenza, John Harry S. Caballo, Ria Bianca R. Caangay, Trisha Eunice C. Makigod, Sharldawn M. Almocera, John Lawrence M. Bayno, Joseph Jr. R. Camposano, Sandy Jean G. Cena, Judy Ann Kyll Garcia, Bea Faye M. Labajo, Athena Grace Tua
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