Chatbot AI Distractions and Academic Triumphs: A Mediation Approach with Self-Control and Coping Skills

Hadiyan Prayoga, Zukhruf Nur Wakhid

Abstract


Research aims: This study investigates self-control and coping skill in academic performance moderated by Chabot AI (CAI) Addiction.

Design/Methodology/Approach: This study performed the online survey and archival method, and included 153 accounting students' respondents as the final sample. The Structural Equation Modelling using Smart-PLS was employed to estimate the relationship between variables.

Research findings:  The findings underscore the significant impact of self-control in mitigating addictive tendencies, highlighting the susceptibility of individuals with lower self-control to develop addictive behaviors toward chatbot AI. While coping skills were not found to have a substantial effect on reducing chatbot AI addiction.

Theoretical contribution/ Originality: This research demonstrates that self-control and coping skills play a crucial role in controlling the dependence on AI-based chatbots, ultimately contributing to a better understanding of the relationship between these psychological abilities and managing AI addiction in university accounting students.(Chassignol et al., 2018; Sollosy & McInerney, 2022)

Practitioner/Policy implication: The findings have implications for chatbot designers and developers. Understanding the potential for addiction behavior allows for the implementation of behavior detection and prevention mechanisms within chatbot designs.

Research limitation/Implication: This study overlooked diverse forms of self-control and coping skills, along with other factors that contribute to Chatbot AI Addiction. Recommending the exploration of various self-control strategies and coping skills could be a valuable opportunity in future research.


Keywords


Addiction; ChatbotAI; Coping; Self-Control

References


Abouammoh, N., Irfan, F., & AlFaris, E. (2020). Stress coping strategies among medical students and trainees in Saudi Arabia: A qualitative study. BMC Medical Education, 20(1), 124. https://doi.org/10.1186/s12909-020-02039-y

Abrahim, S., Mir, B. A., Suhara, H., Mohamed, F. A., & Sato, M. (2019). Structural equation modeling and confirmatory factor analysis of social media use and education. International Journal of Educational Technology in Higher Education, 16(1), 32. https://doi.org/10.1186/s41239-019-0157-y

Aiken, L. R. (1980). Content Validity and Reliability of Single Items or Questionnaires. Educational and Psychological Measurement, 40(4), 955–959. https://doi.org/10.1177/001316448004000419

Alhadabi, A., Al-Harthy, I., Aldhafri, S., & Alkharusi, H. (2023). Want-to, have-to, amotivation, grit, self-control, and tolerance ambiguity among university students: Latent profile analysis. BMC Psychology, 11(1), 260. https://doi.org/10.1186/s40359-023-01298-w

Alshater, M. (2022). Exploring the Role of Artificial Intelligence in Enhancing Academic Performance: A Case Study of ChatGPT. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312358

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7

Babin, B. J., & Svensson, G. (2012). Structural equation modeling in social science research: Issues of validity and reliability in the research process. European Business Review, 24(4), 320–330. https://doi.org/10.1108/09555341211242132

Barczak, N., & Eklund, R. C. (2020). The moderating effect of self-compassion on relationships between performance and subsequent coping and motivation. International Journal of Sport and Exercise Psychology, 18(2), 256–268. https://doi.org/10.1080/1612197X.2018.1511620

Breideband, T., Bush, J., Chandler, C., Chang, M., Dickler, R., Foltz, P., Ganesh, A., Lieber, R., Penuel, W. R., Reitman, J. G., Weatherley, J., & D’Mello, S. (2023). The Community Builder (CoBi): Helping Students to Develop Better Small Group Collaborative Learning Skills. Computer Supported Cooperative Work and Social Computing, 376–380. https://doi.org/10.1145/3584931.3607498

Brey, P., & Søraker, J. H. (2009). Philosophy of Computing and Information Technology. In Philosophy of Technology and Engineering Sciences (pp. 1341–1407). Elsevier. https://doi.org/10.1016/B978-0-444-51667-1.50051-3

Carver, C. S. (2014). Self-control and optimism are distinct and complementary strengths. Personality and Individual Differences, 66, 24–26. https://doi.org/10.1016/j.paid.2014.02.041

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, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Chen, Y., & Yuan, Y. (2007). The innovation strategy of firms: Empirical evidence from the Chinese high‐tech industry. Journal of Technology Management in China, 2(2), 145–153. https://doi.org/10.1108/17468770710756095

Chignell, M., Wang, L., Zare, A., & Li, J. (2023). The Evolution of HCI and Human Factors: Integrating Human and Artificial Intelligence. ACM Transactions on Computer-Human Interaction, 30(2), 1–30. https://doi.org/10.1145/3557891

Choi, I., Lim, S., Catapano, R., & Choi, J. (2018). Comparing two roads to success: Self-control predicts achievement and positive affect predicts relationships. Journal of Research in Personality, 76, 50–63. https://doi.org/10.1016/j.jrp.2018.07.001

Cohen, A., Soffer, T., & Henderson, M. (2022). Students’ use of technology and their perceptions of its usefulness in higher education: International comparison. Journal of Computer Assisted Learning, 38(5), 1321–1331. https://doi.org/10.1111/jcal.12678

De Boer, B. J., Van Hooft, E. A. J., & Bakker, A. B. (2015). Self-control at work: Its relationship with contextual performance. Journal of Managerial Psychology, 30(4), 406–421. https://doi.org/10.1108/JMP-08-2012-0237

Dekker, I., De Jong, E. M., Schippers, M. C., De Bruijn-Smolders, M., Alexiou, A., & Giesbers, B. (2020). Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting. Frontiers in Psychology, 11, 1063. https://doi.org/10.3389/fpsyg.2020.01063

Duckworth, A. L., Taxer, J. L., Eskreis-Winkler, L., Galla, B. M., & Gross, J. J. (2019). Self-Control and Academic Achievement. Annual Review of Psychology, 70(1), 373–399. https://doi.org/10.1146/annurev-psych-010418-103230

F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Freire, C., Ferradás, M. D. M., Regueiro, B., Rodríguez, S., Valle, A., & Núñez, J. C. (2020). Coping Strategies and Self-Efficacy in University Students: A Person-Centered Approach. Frontiers in Psychology, 11, 841. https://doi.org/10.3389/fpsyg.2020.00841

Góngora-Coronado, E. A., & Vásquez-Velázquez, I. I. (2018). From Coping with Stress to Positive Coping with Life: Theoretical Review and Application. Psychology, 09(15), 2909–2932. https://doi.org/10.4236/psych.2018.915169

Grové, C. (2021). Co-developing a Mental Health and Wellbeing Chatbot With and for Young People. Frontiers in Psychiatry, 11, 606041. https://doi.org/10.3389/fpsyt.2020.606041

Haque, M. D. R., & Rubya, S. (2023). An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews. JMIR mHealth and uHealth, 11, e44838. https://doi.org/10.2196/44838

Hewett, R., Liefooghe, A., Visockaite, G., & Roongrerngsuke, S. (2018). Bullying at work: Cognitive appraisal of negative acts, coping, wellbeing, and performance. Journal of Occupational Health Psychology, 23(1), 71–84. https://doi.org/10.1037/ocp0000064

Honken, N., Ralston, P. A., & Tretter, T. R. (2016). Self-Control And Academic Performance In Engineering. American Journal of Engineering Education (AJEE), 7(2), 47–58. https://doi.org/10.19030/ajee.v7i2.9831

Hu, S., Yan, D., An, J., Guo, S., & Qian, M. (2019). Investigation and analysis of Chinese residential building occupancy with large-scale questionnaire surveys. Energy and Buildings, 193, 289–304. https://doi.org/10.1016/j.enbuild.2019.04.007

Ibrahim, H., Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., Adnan, W., Alhanai, T., AlShebli, B., Baghdadi, R., Bélanger, J. J., Beretta, E., Celik, K., Chaqfeh, M., Daqaq, M. F., Bernoussi, Z. E., Fougnie, D., Garcia De Soto, B., Gandolfi, A., … Zaki, Y. (2023). Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Scientific Reports, 13(1), 12187. https://doi.org/10.1038/s41598-023-38964-3

Iorga, M., Ciuhodaru, T., & Romedea, S.-N. (2013). Ethic and Unethic. Students and the Unethical Behavior During Academic Years. Procedia - Social and Behavioral Sciences, 93, 54–58. https://doi.org/10.1016/j.sbspro.2013.09.151

Labrague, L. J. (2023). Problematic internet use and psychological distress among student nurses: The mediating role of coping skills. Archives of Psychiatric Nursing, 46, 76–82. https://doi.org/10.1016/j.apnu.2023.08.009

Lee, M. H. L., Kaur, M., Shaker, V., Yee, A., Sham, R., & Siau, C. S. (2023). Cyberbullying, Social Media Addiction and Associations with Depression, Anxiety, and Stress among Medical Students in Malaysia. International Journal of Environmental Research and Public Health, 20(4), 3136. https://doi.org/10.3390/ijerph20043136

Lee, S. (2022). AI as an explanation agent and user-centered explanation interfaces for trust in AI-based systems. In Human-Centered Artificial Intelligence (pp. 91–102). Elsevier. https://doi.org/10.1016/B978-0-323-85648-5.00014-1

Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022). Using AI chatbots to provide self-help depression interventions for university students: A randomized trial of effectiveness. Internet Interventions, 27, 100495. https://doi.org/10.1016/j.invent.2022.100495

Liu, H., & Zhang, Z. (2023). The impact of managerial myopia on environmental, social and governance (ESG) engagement: Evidence from Chinese firms. Energy Economics, 122, 106705. https://doi.org/10.1016/j.eneco.2023.106705

Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838. https://doi.org/10.1016/j.jbusres.2023.113838

Mei, S., Yau, Y. H. C., Chai, J., Guo, J., & Potenza, M. N. (2016). Problematic Internet use, well-being, self-esteem and self-control: Data from a high-school survey in China. Addictive Behaviors, 61, 74–79. https://doi.org/10.1016/j.addbeh.2016.05.009

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434

Mohamed, E. K. A., & Lashine, S. H. (2003). Accounting knowledge and skills and the challenges of a global business environment. Managerial Finance, 29(7), 3–16. https://doi.org/10.1108/03074350310768319

Nicolescu, L., & Tudorache, M. T. (2022). Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. Electronics, 11(10), 1579. https://doi.org/10.3390/electronics11101579

Nielsen, K. S., & Bauer, J. M. (2019). The Merits of Goal Support as a Self-Control Strategy. Social Psychological and Personality Science, 10(5), 671–680. https://doi.org/10.1177/1948550618780729

Rabenu, E., Yaniv, E., & Elizur, D. (2017). The Relationship between Psychological Capital, Coping with Stress, Well-Being, and Performance. Current Psychology, 36(4), 875–887. https://doi.org/10.1007/s12144-016-9477-4

Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Martín-Antón, L. J., González Díez, I., & Almeida, L. (2023). Perceived satisfaction of university students with the use of chatbots as a tool for self-regulated learning. Heliyon, 9(1), e12843. https://doi.org/10.1016/j.heliyon.2023.e12843

Saka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M., & Bello, S. A. (2023). Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities. Advanced Engineering Informatics, 55, 101869. https://doi.org/10.1016/j.aei.2022.101869

Schmeichel, B. J., & Zell, A. (2007). Trait Self‐Control Predicts Performance on Behavioral Tests of Self‐Control. Journal of Personality, 75(4), 743–756. https://doi.org/10.1111/j.1467-6494.2007.00455.x

Sollosy, M., & McInerney, M. (2022). Artificial intelligence and business education: What should be taught. The International Journal of Management Education, 20(3), 100720. https://doi.org/10.1016/j.ijme.2022.100720

Sumiyana, S., Pratiwi, I. D. E., Hadi, C., Utami, E. R., & Saputra, M. A. (2022). Students’ game addiction caused by multi-constructs and multi-dimensions: Self-controls, cognitive biases and opportunistic behaviours. Frontiers in Education, 7, 966079. https://doi.org/10.3389/feduc.2022.966079

Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High Self-Control Predicts Good Adjustment, Less Pathology, Better Grades, and Interpersonal Success. Journal of Personality, 72(2), 271–324. https://doi.org/10.1111/j.0022-3506.2004.00263.x

Taylor, I. M., Boat, R., & Murphy, S. L. (2020). Integrating theories of self-control and motivation to advance endurance performance. International Review of Sport and Exercise Psychology, 13(1), 1–20. https://doi.org/10.1080/1750984X.2018.1480050

Tibbetts, S. G., & Myers, D. L. (1999). Low self-control, rational choice, and student test cheating. American Journal of Criminal Justice, 23(2), 179–200. https://doi.org/10.1007/BF02887271

Tummers, L. (2017). The Relationship Between Coping and Job Performance. Journal of Public Administration Research and Theory, 27(1), 150–162. https://doi.org/10.1093/jopart/muw058

Turel, Serenko, & Giles. (2011). Integrating Technology Addiction and Use: An Empirical Investigation of Online Auction Users. MIS Quarterly, 35(4), 1043. https://doi.org/10.2307/41409972

Vancouver, J. B. (2008). Integrating self-regulation theories of work motivation into a dynamic process theory. Human Resource Management Review, 18(1), 1–18. https://doi.org/10.1016/j.hrmr.2008.02.001

Vizoso, C., Arias-Gundín, O., & Rodríguez, C. (2019). Exploring coping and optimism as predictors of academic burnout and performance among university students. Educational Psychology, 39(6), 768–783. https://doi.org/10.1080/01443410.2018.1545996

White, R. (1974). Strategies of adaptation an attempt at systematic description. Coping and Adaptation, 47–68.




DOI: https://doi.org/10.18196/jai.v25i2.20755

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