Humanizing Learning in Higher Education: Pengalaman Mahasiswa dalam Pembelajaran Berbasis Humanistik pada Era Artificial Intelligence Humanizing Learning in Higher Education: Student Experiences in Humanistic-Based Learning in the Artificial Intelligence Era

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Abstract

Artificial Intelligence (AI)-based learning has become a focus of various studies, but research that specifically explores students’ experiences in humanistic learning in the AI era remains limited. This study aims to explore students’ experiences in humanistic-based learning and to understand how humanistic values are maintained amid the use of AI in higher education. This study used a qualitative approach with a phenomenological design, involving 12 undergraduate students selected through purposive sampling. Data were collected through in-depth interviews, observation, and documentation, and were then analyzed using Colaizzi’s phenomenological analysis technique. The results show four main themes, namely AI as a learning facilitator, the importance of humanistic interaction in learning, dilemmas in the use of AI in academic activities, and students’ expectations regarding the integration of AI and humanistic learning. The findings show that AI provides easier access to information, increases learning efficiency, and supports understanding of course material. However, students still view interaction with lecturers, empathy, dialogue, and reflection as essential elements that cannot be replaced by technology. The use of AI also raises challenges in the form of potential dependence, a decline in critical thinking skills, and issues of academic ethics. The conclusion of this study emphasizes that AI needs to be positioned as a supporting instrument for learning, while the humanistic approach remains the foundation for creating meaningful learning experiences. This study contributes to the development of the concept of humanizing learning in the digital transformation of higher education and provides implications for universities in designing ethical, adaptive, and student-centered AI-based learning policies and strategies.

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Article Details

How to Cite
Syaputri, R., & Rahmi, A. (2026). Humanizing Learning in Higher Education: Pengalaman Mahasiswa dalam Pembelajaran Berbasis Humanistik pada Era Artificial Intelligence. YASIN, 6(4), 4741-4755. https://doi.org/10.58578/yasin.v6i4.11090

References

Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11, Article 912. https://doi.org/10.1057/s41599-024-03432-4

Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: Opportunities and concerns. Cognitive Research: Principles and Implications, 9, Article 20. https://doi.org/10.1186/s41235-024-00547-9

Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8

Colaizzi, P. F. (1978). Psychological research as the phenomenologist views it. In R. S. Valle & M. King (Eds.), Existential-phenomenological alternatives for psychology (pp. 48–71). Oxford University Press.

Crawford, J., Allen, K.-A., Pani, B., & Cowling, M. (2024). When artificial intelligence substitutes humans in higher education: The cost of loneliness, student success, and retention. Studies in Higher Education, 49(5), 883–897. https://doi.org/10.1080/03075079.2024.2326956

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.

Egert, F., Cordes, A.-K., & Hartig, F. (2022). Can e-books foster child language? Meta-analysis on the effectiveness of e-book interventions in early childhood education and care. Educational Research Review, 37, Article 100472. https://doi.org/10.1016/j.edurev.2022.100472

Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PLOS ONE, 15(5), Article e0232076. https://doi.org/10.1371/journal.pone.0232076

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.

Maslow, A. H. (1970). Motivation and personality (2nd ed.). Harper & Row.

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693_eng

Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook (4th ed.). SAGE Publications.

Moustakas, C. (1994). Phenomenological research methods. SAGE Publications.

Mustafa, M. Y., Tlili, A., Huang, R., Zhao, J., Salha, S., & Burgos, D. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): A roadmap to a future research agenda. Smart Learning Environments, 11, Article 59. https://doi.org/10.1186/s40561-024-00350-5

Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). SAGE Publications.

Rogers, C. R. (1969). Freedom to learn: A view of what education might become. Charles E. Merrill Publishing Company.

Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE Publications.

Tracy, S. J. (2020). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact (2nd ed.). Wiley-Blackwell.

Triberti, S., Di Fuccio, R., Scuotto, C., Marsico, E., & Limone, P. (2024). “Better than my professor?” How to develop artificial intelligence tools for higher education. Frontiers in Artificial Intelligence, 7, Article 1329605. https://doi.org/10.3389/frai.2024.1329605

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0

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