ARTIFICIAL INTELLIGENCE AND ITS INFLUENCE ON STUDENT BEHAVIOR
DOI:
https://doi.org/10.37135/kai.03.14.04Keywords:
aptitude, behavior, self-discipline, learning, attitudeAbstract
The purpose of the present study is to analyze the influence of intrinsic and extrinsic motivation, skills, and subjective norms on the adoption of artificial intelligence (AI) by Ecuadorian students. A quantitative methodology was used to measure the intention and behavior of using AI, based on a Structural Equation Model with Partial Least Squares (SEM-PLS). The sample consisted of 223 surveyed students. The results reveal that extrinsic and intrinsic motivation, intention to use, and behavior have a significant impact on students, while skills and subjective norms do not directly influence them.
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References
Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
Amani, S., White, L., Balart, T., Arora, L., Shryock, K. J., Brumbelow, K., & Watson, K. L. (n.d.). Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academia. arXiv preprint arXiv:2304.14415.
Belanger, F., & Crossler, R. E. (2019). Dealing with digital traces: Understanding protective behaviors on mobile devices. Journal of Strategic Information Systems, 28(1), 34–49. https://doi.org/10.1016/j.jsis.2018.11.002
Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context – A survey study. International Journal of Information Management, 60. https://doi.org/10.1016/j.ijinfomgt.2021.102375
Carranza, R., Díaz, E., Sánchez-Camacho, C., & Martín-Consuegra, D. (2021). e-Banking Adoption: An Opportunity for Customer Value Co-creation. Frontiers in Psychology, 11, 621248. https://doi.org/10.3389/fpsyg.2020.621248
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 Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3
Chaurasia, S. S., Verma, S., & Singh, V. (2019). Exploring the intention to use M-payment in India: Role of extrinsic motivation, intrinsic motivation and perceived demonetization regulation. Transforming Government: People, Process and Policy, 13(3–4), 276–305. https://doi.org/10.1108/TG-09-2018-0060
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, (4), 100118. https://doi.org/10.1016/j.caeai.2022.100118
Christino, J. M. M., Cardozo, É. A. A., Petrin, R., & de Aguiar Pinto, L. H. (2021). Fatores que influenciam a intenção e o comportamento de uso de aplicativos de delivery para restaurantes. Revista Brasileira de Gestao de Negocios, 23(1), 21–42. https://doi.org/10.7819/rbgn.v23i1.4095
Coughlan, J. (2007). Structural Equation Modeling: Guidelines for Determining Model Fit. https://www.researchgate.net/publication/254742561
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Gao, J. (2024). R-Squared (R 2 ) – How much variation is explained? . Research Methods in Medicine & Health Sciences, 5(4), 104–109. https://doi.org/10.1177/26320843231186398
Goktas, P., & Dirsehan, T. (2024). Using PLS-SEM and XAI for casual-predictive services marketing research. Journal of Services Marketing, 39(1). https://doi.org/10.1108/JSM-10-2023-0377
Guanaquiza-Leiva, M., Espinoza-Saraguro, D., & Bonisoli, L. (2022). Análisis de los factores ambientales en la decisión de compra de los productos orgánicos en el consumidor ecuatoriano. 593 Digital Publisher CEIT, 7(2), 247–259. https://doi.org/10.33386/593dp.2022.2.1026
Ho, J. C., Wu, C. G., Lee, C. S., & Pham, T. T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63. https://doi.org/10.1016/j.techsoc.2020.101360
Hoces, Z. (2023). Motivación y estrategias de aprendizaje en educación universitaria. Instituto Universitario de Innovación Ciencia y Tecnología Inudi Perú. https://doi.org/10.35622/inudi.b.117
Jang, M., Aavakare, M., Nikou, S., & Kim, S. (2021). The impact of literacy on intention to use digital technology for learning: A comparative study of Korea and Finland. Telecommunications Policy, 45(7). https://doi.org/10.1016/j.telpol.2021.102154
Kusumawati, M. D., Fauziddin, M., & Ananda, R. (2023). The Impact of Reward and Punishment on the Extrinsic Motivation of Elementary School Students. AL-ISHLAH: Jurnal Pendidikan, 15(1), 183–192. https://doi.org/10.35445/alishlah.v15i1.2856
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
Leong, L. Y., Hew, T. S., Ooi, K. B., Metri, B., & Dwivedi, Y. K. (2023). Extending the Theory of Planned Behavior in the Social Commerce Context: A Meta-Analytic SEM (MASEM) Approach. Information Systems Frontiers, 25(5), 1847–1879. https://doi.org/10.1007/s10796-022-10337-7
Liu, R. De, Wang, J., Gu, D., Ding, Y., Oei, T. P., Hong, W., Zhen, R., & Li, Y. M. (2019). The effect of parental phubbing on teenager’s mobile phone dependency behaviors: The mediation role of subjective norm and dependency intention. Psychology Research and Behavior Management, 12, 1059–1069. https://doi.org/10.2147/PRBM.S224133
Liu, Z., Wang, S., & Gu, Q. (2023). Study on the Mechanism of Influencing Adolescents’ Willingness to Participate in Ice Sports. Children, 10(6), 1080. https://doi.org/10.3390/children10061080
Malek, S. L., Sarin, S., & Haon, C. (2020). Extrinsic Rewards, Intrinsic Motivation, and New Product Development Performance. Journal of Product Innovation Management, 37(6), 528–551. https://doi.org/10.1111/jpim.12554
Martín-Núñez, J. L., Ar, A. Y., Fernández, R. P., Abbas, A., & Radovanović, D. (2023). Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic? Computers and Education: Artificial Intelligence, 4, 100128. https://doi.org/10.1016/j.caeai.2023.100128
Montalván-Vélez, C. L., Mogrovejo-Zambrano, J. N., Rodríguez-Andrade, A. E., & Andrade-Vaca, A. L. (2024). Adopción y Efectividad de Tecnologías Emergentes en la Educación desde una Perspectiva Administrativa y Gerencial. Journal of Economic and Social Science Research, 4(1), 160–172. https://doi.org/10.55813/gaea/jessr/v4/n1/92
Nguyen, G. N., & Ho, T. T. H. (2022). Interplay between subjective norm, emotions, and purchase intention towards foreign brands: evidence from Vietnam. Innovative Marketing, 18(1), 79–93. https://doi.org/10.21511/im.18(1).2022.07
Perri, C., Giglio, C., & Corvello, V. (2020). Smart users for smart technologies: Investigating the intention to adopt smart energy consumption behaviors. Technological Forecasting and Social Change, 155. https://doi.org/10.1016/j.techfore.2020.119991
Razak, N. I. A., Zamzuri, Z. H., & Suradi, N. R. M. (2018). Bootstrapping technique in structural equation modeling: A Monte Carlo study. Journal of Physics: Conference Series, 1132(1). https://doi.org/10.1088/1742-6596/1132/1/012072
Rochina Chileno, S. C., Duarte Mora, M. J., Macanchí Pico, M. L., & Tipantuña Soria, E. G. (2024). Transformación educativa en el siglo XXI: Integración de Tecnologías Emergentes para el Aprendizaje Efectivo. Reincisol., 3(6), 6092–6109. https://doi.org/10.59282/reincisol.V3(6)6092-6109
Sarango, E., Vásquez, F., & Bonisoli, L. (2024). Responsabilidad social corporativa y lealtad a la marca: un análisis empírico. Journal Business Science, 5(1), 53–68. https://doi.org/10.56124/jbs.v5i1.004
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Shneor, R., & Munim, Z. H. (2019). Reward crowdfunding contribution as planned behaviour: An extended framework. Journal of Business Research, 103, 56–70. https://doi.org/10.1016/j.jbusres.2019.06.013
Singh, A., Sharma, S., & Paliwal, M. (2020). Adoption intention and effectiveness of digital collaboration platforms for online learning: the Indian students’ perspective. Interactive Technology and Smart Education, 18(4), 493–514. https://doi.org/10.1108/ITSE-05-2020-0070
Strzelecki, A. (2023). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education. https://doi.org/10.1007/s10755-023-09686-1
Tilibașa, M. A., Boncilică, A. N., Popa, I., Ștefan, S. C., & Tărăban, I. (2023). Implications of digital risks on teachers’ motivation and intention to use digital tools: a PLS-POS perspective in Romanian preuniversity education system. Kybernetes, 52(13), 45–60. https://doi.org/10.1108/K-06-2023-1116
Urhahne, D., & Wijnia, L. (2023). Theories of Motivation in Education: an Integrative Framework. Educational Psychology Review, 35(2), 45. https://doi.org/10.1007/s10648-023-09767-9
Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: a perspective of AI anxiety. Interactive Learning Environments, 32(6), 2584–2600. https://doi.org/10.1080/10494820.2022.2153147
Wu, C. H., Liu, C. H., & Huang, Y. M. (2022). The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. International Journal of STEM Education, 9(1). https://doi.org/10.1186/s40594-022-00346-y
Xu, L., Zhang, J., Ding, Y., Zheng, J., Sun, G., Zhang, W., & Philbin, S. P. (2023). Understanding the role of peer pressure on engineering students’ learning behavior: A TPB perspective. Frontiers in Public Health, 10, 1069384. https://doi.org/10.3389/fpubh.2022.1069384
Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance. Journal of Educational Computing Research, 59(1), 154–177. https://doi.org/10.1177/0735633120952067
Zaccone, M. C., & Pedrini, M. (2019). The effects of intrinsic and extrinsic motivation on students learning effectiveness. Exploring the moderating role of gender. International Journal of Educational Management, 33(6), 1381–1394. https://doi.org/10.1108/IJEM-03-2019-0099
Zarouali, B., Van Den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting Consumer Responses to a Chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491–497. https://doi.org/10.1089/cyber.2017.0518
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