AI Trust and Attitude Towards AI of University Students
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Abstract
The quantitative study investigated the relationship between AI trust and attitudes toward AI among university college students. An adapted questionnaire was utilized. Data were gathered through Google Forms, where the respondents were selected using a stratified random sampling technique. Validity and reliability tests were employed using Cronbach's Alpha and Average Variance Extracted. Descriptive statistics were used to describe the variables and subcomponents in the study, while bootstrapping analysis was conducted through SmartPLS 4.0 to assess the hypothesized model. The results demonstrated that college students showed a moderate level of AI trust and attitude toward AI and confirmed the significant relationship between the predictor (AI trust) and outcome (attitude toward AI) constructs.
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References
Aderibigbe, A., Ohenhen, P., Nwaobia, N., Gidiagba, J., & Ani, E. (2023). Artificial Intelligence in Developing Countries: Bridging the Gap Between Potential and Implementation. Computer Science & IT Research Journal. https://doi.org/10.51594/csitrj.v4i3.629.
Ahmad, Z., Rahim, S., Zubair, M. & Abdul-Ghafar, J. (2021). Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol, 16(24), 1-16. https://doi. org/10.1186/s13000-021-01085-4
Antwi, S. K., & Hamza, K. (2015). Qualitative and Quantitative Research Paradigms in Business Research: A philosophical reflection. European Journal of Business and Management, 7(3), 217–225. https://www.iiste.org/Journals/index.php/EJBM/article/view/19543
Bantoto, F. M. O., Rillo, R., Abequibel, B., Mangila, B. B., & Alieto, E. O. (2024). Is AI an effective “learning tool” in academic writing? Investigating the perceptions of third-year university students on the use of artificial intelligence in classroom instruction. In S. Motahhir & B. Bossoufi (Eds.), Digital technologies and applications: ICDTA 2024 (Lecture Notes in Networks and Systems, Vol. 1098). Springer. https://doi.org/10.1007/978-3-031-68650-4_8
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
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
Clercq, G. D. (2023, January 28). Top French university bans use of ChatGPT to prevent plagiarism. Reuters. https://www.reuters.com/technology/top-french-university-bans-use-chatgpt-prevent-plagiarism-2023-01-27/
Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2016). A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47–64. https://doi.org/10.1515/jaiscr-2017-0004
Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Jagdeep, S. J. S. S. (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3), e25896. https://doi.org/10.1016/j.heliyon.2024.e25896
Diamantopoulos, A. and Winklhofer, H. M. (2001). Index Construction with Formative Indicators: An Alternative to Scale Development. Journal of Marketing Research, 38(2), 269-277. https://www.jstor.org/ stable/1558630
Dong, Y., Hou, J., Zhang, N. & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 1-10. https://doi. org/10.1155/2020/1680845
Dorton, S., & Harper, S. (2022). A Naturalistic Investigation of Trust, AI, and Intelligence Work. Journal of Cognitive Engineering and Decision Making, 16, 222 - 236. https://doi.org/10.1177/15553434221103718.
Duan, Y., Edwards, J. & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63- 71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers. https://psycnet.apa.org/record/1992-98849-000
Engaging with AI in your education and assessment. (n.d.). University College of London. https://www.ucl.ac.uk/students/exams-and-assessments/assessment-success-guide/engaging-ai-your-education-and-assessment
Ernst, A., & Albers, C. (2017). Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions. PeerJ, 5. https://doi.org/10.7287/peerj.3323v0.2/reviews/1.
Ertel, W. (2018). Introduction to artificial Intelligence. Springer.
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
Gerlich, M. (2023). Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Social Sciences, 12(9), 502. https://doi.org/10.3390/socsci12090502
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLSSEM. European Business Review, 31(1), 2–24. https:// doi.org//10.1108/EBR-11-2018-0203
Hair JF, Hult GTM, Ringle CM, et al. (2017a) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Thousand Oaks, CA: Sage.
Henderson, A. (2005). The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data.. Clinica chimica acta; international journal of clinical chemistry, 359 1-2, 1-26 . https://doi.org/10.1016/J.CCCN.2005.04.002.
Hu, Y., & Plonsky, L. (2019). Statistical assumptions in L2 research: A systematic review. Second Language Research, 37, 171 - 184. https://doi.org/10.1177/0267658319877433.
Huang, J., Salmiza, S., & Liu, Y. (2021). A review on Artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 1. https://doi.org/10.36941/ajis-2021-0077
Jackson, P. C. (2019). Introduction to artificial Intelligence: Third Edition. Courier Dover Publications.
Kaledio, P., Robert, A., & Frank, L. (2024). The impact of artificial intelligence on students’ learning experience. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4716747
Khanduri, Vandana, and Dr. Ankur Teotia. 2023. Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions. International Journal of Research Publication and Reviews 4 (6): 663–72. https://doi.org/10.55248/gengpi.4.623.44407
Liehner, M., Erne, R., & Kestel, C. (2023). Trust in AI systems: A psychological perspective. Journal of Trust Research, 12(1), 12-30. https://doi.org/10.1080/21515581.2023.2103541
Liu, Y., Chen, L., & Yao, Z. (2022). The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.929175.
Lynard, Bobby, L., Asirit., Jocelyn, H., Hua. (2023). Converging perspectives: Assessing AI readiness and utilization in Philippine higher education. doi: 10.58429/pgjsrt.v2n3a152
Martin, A. & Freeland, S. (2020). The Advent of Artificial Intelligence in Space Activities: New Legal Challenges. Space Policy, 55, 101408. https://doi.org/10.1016/j. Spacepol.2020.101408
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.
Mikalef, P., van de Wetering, R., & Krogstie, J. (2021). Building dynamic capabilities through big data analytics: The role of IT ambidexterity. Information & Management, 58(3), 103439. https://doi.org/10.1016/j.im.2020.103439
Montag, C., Becker, B., & Gan, C. (2023). The psychology of trust in AI. AI & Society, 38, 615-631. https://doi.org/10.1007/s00146-021-01257-4
Mota, A. L., Ferraciolli, S. F., Ayres, A. S., Polsin, L. L. M., da Costa Leite, C., & Kitamura, F. (2023). AI and Big Data for Intelligent Health: Promise and Potential. In Trends of Artificial Intelligence and Big Data for E-Health (pp. 1-14). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-11199-0_1
Nielsen, E., Nørskov, A., Lange, T., Thabane, L., Wetterslev, J., Beyersmann, J., Uña-Álvarez, J., Torri, V., Billot, L., Putter, H., Winkel, P., Gluud, C., & Jakobsen, J. (2019). Assessing assumptions for statistical analyses in randomised clinical trials. BMJ Evidence-Based Medicine, 24, 185 - 189. https://doi.org/10.1136/bmjebm-2019-111174.
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. (2023). 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., Caballo, J. H. S., Caangay, R. B. R., Makigod, T. E. C., Almocera, S. M., Bayno, J. L. M., Camposano, J. J. R., Cena, S. J. G., Garcia, J. A. K., Labajo, B. F. M., & Tua, A. G. (2024). Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model. American Journal of Applied Statistics and Economics, 3(1), 99–108. https://doi.org/10.54536/ajase.v3i1.2510
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
Obenza, B. N., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2023). 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/10.31149/ijhcs.v5i12.5033
Pascual, J. (2023, November 16). PH schools adopting AI; UP among first in Asia: group. ABS CBN News. https://news.abs-cbn.com/business/11/16/23/ph-schools-adopting-ai-up-among-first-in-asia-group
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. Br. J. Educ. Technol., 51, 1693-1710. https://doi.org/10.1111/bjet.12994.
Rahman, M. S. (2016). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review. Journal of Education and Learning, 6(1). https://doi.org/10.5539/jel.v6n1p102
Rai, A., Constantinides, P., & Sarker, S. (2019). Editor’s comments: Next-generation digital platforms: Toward human–AI hybrids. MIS Quarterly, 43(1), iii–ix. https://doi.org/10.25300/MISQ/2019/13765
Samonte, M., Escarillo, J., Go, K., Landrito, N., & Randhawa, J. (2023). Determining the Trust Level of Senior High School Associated with the Use of AI-Powered Digital Assistants. Proceedings of the 2023 6th International Conference on Information Management and Management Science. https://doi.org/10.1145/3625469.3625490.
Schepman, A. & Rodway, P. (2023). The general attitudes 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/10.1080/104473 18.2022.2085400
Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence. Technology in Society, 77, 102537. https://doi.org/10.1016/j.techsoc.2024.102537
Schmidt, A., & Finan, C. (2017). Linear regression and the normality assumption.. Journal of clinical epidemiology, 98, 146-151 . https://doi.org/10.1016/j.jclinepi.2017.12.006.
Scotti, V. (2020). Artificial intelligence. IEEE Instrumentation & Measurement Magazine, 23(3), 27–31. https://doi.org/10.1109/mim.2020.9082795
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: definition and background. In Mission AI (pp. 15–41). https://doi.org/10.1007/978-3-031- 21448-6_2
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ 48, 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Teodorescu, D., Aivaz, K., Vancea, D., Condrea, E., Drăgan, C., & Olteanu, A. (2023). Consumer Trust in AI Algorithms Used in E-Commerce: A Case Study of College Students at a Romanian Public University. Sustainability. https://doi.org/10.3390/su151511925.
Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & De Visser, E. J. (2024). Student Perceptions of ChatGPT use in a college essay Assignment: Implications for learning, grading, and trust in Artificial intelligence. IEEE Transactions on Learning Technologies, 17, 1069–1081. https://doi.org/10.1109/tlt.2024.3355015
Wasserman, S., & Böckenholt, U. (1989). Bootstrapping: applications to psychophysiology.. Psychophysiology, 26 2, 208-21 . https://doi.org/10.1111/J.1469-8986.1989.TB03159.X.
Wu, W., Li, H., Wang, H., & Zhu, K. (2017). Semantic Bootstrapping: A Theoretical Perspective. IEEE Transactions on Knowledge and Data Engineering, 29, 446-457. https://doi.org/10.1109/TKDE.2016.2619347.
Zanna, M. P., & Rempel, J. K. (1988). Attitudes: A new look at an old concept. In D. Bar-Tal & A. W. Kruglanski (Eds.), The social psychology of knowledge (pp. 315–334). Cambridge University Press; Editions de la Maison des Sciences de l’Homme
Zoubir, A., & Boashash, B. (1998). The bootstrap and its application in signal processing. IEEE Signal Process. Mag., 15, 56-76. https://doi.org/10.1109/79.647043.
Drolet A. L. & Morrison D. G. (2001) Do We Really Need Multiple-Item Measures in ServiceResearch? Journal of Service Research, 3(3), 196-204. https://doi. org/10.1177/109467050133001
Yüzbaşıoğlu, E. (2020). Attitudes and perceptions of dental students towards artificial intelligence. Journal of Dental Education, 85(1), 60–68. https://doi.org/10.1002/jdd.12385