AI Dependency and Critical Thinking in Higher Education: A Life-Course Perspective on Ethical Awareness and  Algorithmic Bias

Jabal Nur Popalia; Muh. Al-Habsy; Muh. Akbar; Akhmad Affandi

doi:10.66053/aillce.v1i1.3

Authors

Jabal Nur Popalia Universitas Negeri Makassar
Muh. Al-Habsy Universitas Negeri Makassar
Muh. Akbar Universitas Negeri Makassar
Akhmad Affandi Dresden University

DOI:

https://doi.org/10.66053/aillce.v1i1.3

Keywords:

AI Dependency, Critical Thinking, Artificial intelligence in education, Life-course education, Algorithmic bias awareness

Abstract

Purpose – The rapid adoption of artificial intelligence (AI) in higher education has transformed how students engage with learning tasks, raising concerns about dependency, ethical awareness, and algorithmic bias. From a life-course education perspective, early adulthood represents a critical developmental stage in which patterns of AI use may shape long-term critical thinking and lifelong learning dispositions. However, empirical studies integrating AI dependency, ethical awareness, and algorithmic bias awareness in relation to students’ critical thinking remain limited. This study examines the effects of AI dependency, ethical awareness, and algorithmic bias awareness on university students’ critical thinking skills in the context of Indonesian higher education.
Design/methods/approach – A quantitative cross-sectional design was employed. Data were collected from 110 undergraduate students across four universities in South Sulawesi, Indonesia, using purposive sampling. A validated questionnaire measured AI dependency, ethical awareness, algorithmic bias awareness, and critical thinking skills. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS.
Findings – The results indicate that all three variables significantly and positively influence students’ critical thinking skills. Algorithmic bias awareness exhibits the strongest effect, followed by AI dependency and ethical awareness. These findings suggest that critical awareness of AI limitations contributes more substantially to critical thinking development than the intensity of AI use alone.
Research implications/limitations – The cross-sectional design limits causal interpretation, and the dominance of early-semester STEM students constrains generalizability. Potential moderating factors were not examined.
Originality/value – This study contributes to the literature on artificial intelligence in education by integrating ethical awareness and algorithmic bias awareness within a life-course framework, highlighting the central role of critical AI literacy in supporting sustainable critical thinking development in higher education.

References

Abbas, A. (2023). Analisis Survey Penggunaan Artificial Intelligence (AI) dalam Penulisan Tugas Akhir Karya Tulis Ilmiah (TA-KTI) di Kampus Akademi Ilmu Komputer (AIKOM) Ternate, Maluku Utara, Indonesia.

Barr, N., Pennycook, G., Stolz, J. A., & Fugelsang, J. A. (2015). The Brain in Your Pocket: Evidence that Smartphones are Used to Supplant Thinking. Computers in Human Behavior, 48, 473–480.

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 1–12. https://doi.org/10.1080/14703297.2023.2190148

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications.

Dewantara & Dewi. (2025). Generative AI dalam Pembelajaran Mahasiswa: Antara Inovasi Pendidikan dan Integritas Akademik. http://Jiip.stkipyapisdompu.ac.id

Firdaus, J. A. & others. (2025). Ketergantungan Penggunaan Kecerdasan Buatan (AI) pada Tugas Akademik Mahasiswa Terhadap Kemampuan Berpikir Kritis dan Kreatif.

Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Hair, S., J. F. ,. Hult, G. T. M. ,. Ringle, C. M. ,. Sarstedt, M. ,. Danks, N. P. ,. &. Ray. (2021). Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. Springer International Publishing. Https://doi.org/10.1007/978-3-030-80519-7. https://doi.org/10.1007/978-3-030-80519-7

Harahap, L. K. (2020). Analisis SEM (Structural Equation Modelling) dengan SMARTPLS.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial Intelligence in Education. In Data Ethics: Building Trust (pp. 621–653). Globethics Publications. https://doi.org/10.58863/20.500.12424/4276068

Ifani, A. Z., Abdullah, M. A., Vega, N., & Ilahi, W. S. (2024). Analisis Ketergantungan Penggunaan Chat GPT di Kalangan Mahasiswa Menyebabkan Penurunan Kualitas Belajar. SMARTLOCK. https://e-jurnal.nobel.ac.id/index.php/smartlock

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, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the CHI Conference on Human Factors in Computing Systems.

Miao, F., Giannini, S., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. UNESCO.

Robiul, D., Arya, I., & Zakariyya, A. (2023). Manfaat Kecerdasan Buatan untuk Pendidikan.

Rohman, H. I. & others. (2025). Disrupsi Teknologi dalam Pendidikan Tinggi di Indonesia: Studi Literatur tentang Bagaimana Artificial Intelligence Mengubah Lanskap Pembelajaran.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In Handbook of Market Research. Springer. https://doi.org/10.1007/978-3-319-05542-8_15-2

Sukma & others. (2025). Pemahaman Pelajar Tentang Kecerdasan Buatan dan Implikasinya Terhadap Literasi.

Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in Higher Education: Considerations for Academic Integrity and Student Learning. Journal of Applied Learning and Teaching, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17

Syahroni, M. I. (2022). Prosedur Penelitian Kuantitatif. Jurnal Al-Musthafa STIT Al-Aziziyah Lombok Barat, 43(3).

Tian, S., Zhang, B., & He, H. (2024). Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 899–920. https://doi.org/10.3390/jtaer19020047