THE ROLE OF TECHNO-ETHICAL CLASSROOM BEHAVIOR AND METACOGNITIVE EXPERIENCES OVER TIME IN STUDENTS’ ARTIFICIAL INTELLIGENCE ADDICTIONS
Abstract
The study aims to identify the factors that may influence university students’ artificial intelligence addiction in educational activities. The study sample consists of 415 students currently studying at Bayburt University. The snowball sampling method was used in the research. In the study, an electronic survey was conducted with university students. The research surveys were distributed to students attending face-to-face education at various departments of Bayburt University by academic staff (or educators). Students participated in the study voluntarily. In the research application, statistical analysis programs such as SPSS, SPSS Process Macro, and AMOS were utilized. The study was designed using Structural Equation Modeling (SEM). The analyses revealed relational outcomes of the research variables, along with the demographic information of the students. In the analyses, results for both direct effect hypotheses and moderator effect hypotheses were also presented. The study explores the relationships between negative classroom behavior from a technoethical perspective, positive and negative metacognitive experiences related to the passage of time, and artificial intelligence addiction. The results of the study indicate that negative classroom behavior from a techno-ethical perspective increases students’ artificial intelligence addiction. The students’ negative techno-ethical classroom behavior reduced their positive metacognitive experiences related to the passage of time. At the same time, increased their negative metacognitive experiences related to the passage of time. Students’ positive and negative metacognitive experiences associated with time also increased their artificial intelligence addiction. It was observed that many students dedicated more than two hours, or between two and one hour, to artificial intelligence and its applications in their daily activities. In the relationship between negative techno-ethical classroom behavior and artificial intelligence addiction, the daily usage time of artificial intelligence played a moderating role. The daily usage time of artificial intelligence strengthened the relationship between negative techno-ethical classroom behavior and artificial intelligence addiction. There was also a moderating effect of positive metacognitive experiences related to the passage of time in the relationship between negative techno-ethical classroom behavior and artificial intelligence addiction. The student’s educational status and gender were found to have moderating effects on the relationship between negative techno-ethical classroom behavior and artificial intelligence addiction.
Keywords
Artificial intelligence addiction, techno-ethical classroom behavior, metacognitive level related to the passage of time, university students
Supporting Institution
There is no supporting institution.
Project Number
not derived from the project.
Ethical Statement
The ethics committee approval of the research was discussed and accepted by the Bayburt University ethics committee dated 03.06.2024-207096 and numbered.
References
- Alqahtani, T., Badreldin, H.A., Alrashed, M., Alshaya, A.I. et al. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19, 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
- Amrute, S. (2019). Of techno-ethics and techno-affects. Feminist Review, 123, 56-73. https://doi.org/10.1177/0141778919879744
- Backer, L.D., Keer, H.V. & Valcke, M. (2022). The functions of shared metacognitive regulation and their differential relation with collaborative learners’ understanding of the learning content. Learning and Instruction, 77, 1-11. https://doi.org/10.1016/j.learninstruc.2021.101527
- Baron, J.V. & Dajay, B.S. (2024). Double-Edged Sword: Examining the Link between Students’ Dependence on Artificial Intelligence (AI) and their Psychosocial Maturity. TWIST, 19(3), 339-344. https:// doi.org/10.5281/zenodo.10049652#236
- Boer, H., Anouk, S.D., Kostons, D.D.N.M. & Werf, G.P.C. (2018). Long-term effects of metacognitive strategy instruction on student academic performance: A meta-analysis. Educational Research Review, 24, 98-115. https://doi.org/10.1016/j.edurev.2018.03.002