PENGARUH HUMAN CONTROL TERHADAP KEPERCAYAAN MAHASISWA TEKNIK INFORMATIKA DALAM PENGGUNAAN CLAUDE AI DI UNIVERSITAS MALIKUSSALEH
DOI:
https://doi.org/10.62671/suliwa.v3i3.350Keywords:
Human Control, trust, Claude ai, informatics students, generative aiAbstract
Generative AI assistants such as Claude AI are increasingly used by informatics students, yet trust in them depends not only on output quality but also on how much control users feel they have over the process. This study examines the effect of Human Control on trust among Informatics Engineering students at Universitas Malikussaleh using Claude AI. A cross-sectional survey of 55 students measured Human Control through four indicators (controlling AI decisions, correcting AI output, determining AI use, and retaining final decisions) and trust through an eight-item scale (1–5). Both Human Control (M = 3.95) and trust (M = 3.62) were high. The habit of checking AI output (M = 4.07) was stronger than the ability to fix wrong parts oneself (M = 3.76), and consistency of answer quality was the lowest-rated trust item (M = 3.49). Simple linear regression showed that Human Control had a positive and significant effect on trust (B = 0.733, β = 0.742, p < .001), explaining 55.1% of its variance (R² = .551). Perceived control is therefore a strong, but not the only, determinant of trust in generative AI, supporting learning designs that train students to verify and correct AI output.
References
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 3, pp. 1–13). ACM. https://doi.org/10.1145/3290605.3300233
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643
Ghozali, I. (2018). Aplikasi analisis multivariate dengan program IBM SPSS 25 (Edisi 9). Badan Penerbit Universitas Diponegoro.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32(2), 311–328. https://doi.org/10.1037/0022-3514.32.2.311
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., … Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35.
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Sugiyono. (2019). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019). Understanding the effect of accuracy on trust in machine learning models. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 279, pp. 1–12). ACM. https://doi.org/10.1145/3290605.3300509
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020). Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 295–305). ACM. https://doi.org/10.1145/3351095.3372852
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muksalmina (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
