Differences in Male and Female Responses to Artificial Intelligence Integration for Education Faculty: Study of Thailand International Students at Islamic Universities in Indonesia
DOI:
https://doi.org/10.20414/elhikmah.v18i1.10037Keywords:
male, female, artificial intelligence, teacher training study program, international student, Thailand, Islamic college, IndonesiaAbstract
This study aims to investigate the different views and responses of male and female Thai students regarding the advancement of artificial intelligence (AI) technology in the context of teacher training study programs in Islamic universities in Indonesia. Through a qualitative approach with a case study design, the study collected data through interviews, document studies, observations, and surveys. Data validity is guaranteed through triangulation and analysis using Miles and Huberman models. Involving Thai students at UIN Walisongo Semarang, this study found significant differences in responses to AI between genders. Women tend to support the use of existing AI for learning, while men are more interested in developing new technologies to overcome challenges. This difference is reflected in their views on the role of AI in improving access and quality of education as well as in concern for data privacy and security. In addition, the study highlights differences in engagement rates between genders, with women more open to the effective use of AI in learning, while men are more active in the development of AI technologies for education. These findings illustrate the influence of cultural, social, and psychological factors in the adoption and development of AI. This research contributes to finding the characteristics of men and women in responding to AI in Islamic higher education, and this plays a role in determining policies so that AI development can be utilized optimally by international students.
Abstrak: Penelitian ini bertujuan untuk mengetahui perbedaan pandangan dan tanggapan mahasiswa Thailand laki-laki dan perempuan terhadap kemajuan teknologi kecerdasan buatan (AI) dalam konteks program studi keguruan di universitas-universitas Islam di Indonesia. Melalui pendekatan kualitatif dengan desain studi kasus, penelitian ini mengumpulkan data melalui wawancara, studi dokumen, observasi, dan survei. Keabsahan data dijamin melalui triangulasi, dan analisis menggunakan model Miles dan Huberman. Dengan melibatkan mahasiswa asal Thailand di UIN Walisongo Semarang, penelitian ini menemukan perbedaan signifikan respon terhadap AI antar gender. Perempuan cenderung mendukung penggunaan AI yang ada untuk pembelajaran, sementara laki-laki lebih tertarik mengembangkan teknologi baru untuk mengatasi tantangan. Perbedaan ini tercermin dalam pandangan mereka mengenai peran AI dalam meningkatkan akses dan kualitas pendidikan serta kepedulian terhadap privasi dan keamanan data. Selain itu, penelitian ini menyoroti perbedaan tingkat keterlibatan antar gender, dimana perempuan lebih terbuka terhadap penggunaan AI secara efektif dalam pembelajaran, sementara laki-laki lebih aktif dalam pengembangan teknologi AI untuk pendidikan. Temuan ini menggambarkan pengaruh faktor budaya, sosial, dan psikologis dalam adopsi dan pengembangan AI. Penelitian ini berkontribusi untuk menemukan karakteristik laki-laki dan perempuan dalam menyikapi AI di perguruan tinggi Islam, hal ini berperan dalam menentukan kebijakan agar pengembangan AI dapat dimanfaatkan secara maksimal oleh mahasiswa internasional.
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Bennett, C. L., Gleason, C., Scheuerman, M. K., Bigham, J. P., Guo, A., & To, A. (2021, May). "It's complicated": Negotiating accessibility and (mis) representation in image descriptions of race, gender, and disability. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–19).
Borau, S., Otterbring, T., Laporte, S., & Fosso Wamba, S. (2021). The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI. Psychology & Marketing, 38(7), 1052–1068.
Capella, M. E. T., & Villalba, R. G. (2024). Treatment of the information on gender-based violence: With contributions from artificial intelligence. Spanish Journal of Legal Medicine.
Chowdhury, R. (2023). Technology facilitated gender-based violence in an era of generative AI. UNESCO Publishing.
Costa, P., & Ribas, L. (2019). AI becomes her: Discussing gender and artificial intelligence. Technoetic Arts: A Journal of Speculative Research, 17(1–2), 171–193.
Costa, P. (2018). Conversing with personal digital assistants: On gender and artificial intelligence. Journal of Science and Technology of the Arts, 10(3), 59–72.
Cyr, D., Gefen, D., & Walczuch, R. (2017). Exploring the relative impact of biological sex and masculinity–femininity values on information technology use. Behaviour & Information Technology, 36(2), 178–193. https://doi.org/10.1080/0144929X.2016.1212091
Ely, R. J., & Meyerson, D. E. (2000). Theories of gender in organizations: A new approach to organizational analysis and change. Research in Organizational Behavior, 22, 103–151.
Evetts, J. (1996). Gender and career in science and engineering (Vol. 2). CRC Press.
Gebru, T. (2020). Race and gender. In The Oxford Handbook of Ethics of AI (pp. 251–269).
Georgievskaya, A. (2022). Artificial intelligence confirming treatment success: The role of gender- and age-specific scales in performance evaluation. Plastic and Reconstructive Surgery, 150, 34S–40S.
Gioia, D. (2021). A systematic methodology for doing qualitative research. The Journal of Applied Behavioral Science, 57(1), 20–29.
Gupta, M., Parra, C. M., & Dennehy, D. (2022). Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter? Information Systems Frontiers, 24(5), 1465–1481.
Hearn, J., & Parkin, P. W. (1983). Gender and organizations: A selective review and a critique of a neglected area. Organization Studies, 4(3), 219–242.
Hong, J. W., Wang, Y., & Lanz, P. (2020). Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction, 36(18), 1768–1774.
Katyal, S. K., & Jung, J. Y. (2021). The gender panopticon: AI, gender, and design justice. UCLA Law Review, 68, 692.
Katyal, S. K., & Jung, J. Y. (2022). The gender panopticon: AI, gender, and design justice. Dukeminier Awards: Best Sexual Orientation & Gender Identity Law Review, 21, 135.
Kim, A., Cho, M., Ahn, J., & Sung, Y. (2019). Effects of gender and relationship type on the response to artificial intelligence. Cyberpsychology, Behavior, and Social Networking, 22(4), 249–253.
Kruttschnitt, C. (2013). Gender and crime. Annual Review of Sociology, 39, 291–308.
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences, 117(23), 12592–12594.
Latorre Ruiz, E., & Pérez Sedeño, E. (2023). Gender bias in artificial intelligence. In Gender in AI and Robotics: The Gender Challenges from an Interdisciplinary Perspective (pp. 61–75). Cham: Springer International Publishing.
Lütz, F. (2023). Gender equality and algorithms: HRC resolution on new and emerging technologies. Blog Interdisziplinäre Geschlechterforschung, 5, 2023.
Malterud, K., Siersma, V., & Guassora, A. D. (2021). Information power: Sample content and size in qualitative studies. Qualitative Research.
Manasi, A., Panchanadeswaran, S., Sours, E., & Lee, S. J. (2022). Mirroring the bias: Gender and artificial intelligence. Gender, Technology and Development, 26(3), 295–305.
Milella, F., Natali, C., Scantamburlo, T., Campagner, A., & Cabitza, F. (2023, August). The impact of gender and personality in human-AI teaming: The case of collaborative question answering. In IFIP Conference on Human-Computer Interaction (pp. 329–349). Cham: Springer Nature Switzerland.
Nurdin, N. (2021). Employing online and offline qualitative interpretive case studies in understanding e-procurement effectiveness. International Journal of Quantitative and Qualitative Research Methods, 9(1), 23–41.
Prates, M. O., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: A case study with Google Translate. Neural Computing and Applications, 32, 6363–6381.
Raghunathan, K., McKenna, L., & Peddle, M. (2023). Factors in integrating academic electronic medical records in nursing curricula: A qualitative multiple case studies approach. Nurse Education Today, 120, 105626.
Stewart, A. J., & McDermott, C. (2004). Gender in psychology. Annual Review of Psychology, 55, 519–544.
Vorsino, Z. (2021). Chatbots, gender, and race on web 2.0 platforms: Tay.AI as monstrous femininity and abject whiteness. Signs: Journal of Women in Culture and Society, 47(1), 105–127.
Weeks, W. A., Moore, C. W., McKinney, J. A., & Longenecker, J. G. (1999). The effects of gender and career stage on ethical judgment. Journal of Business Ethics, 20, 301–313.
Witz, A., & Savage, M. (1991). The gender of organizations. The Sociological Review, 39(1_suppl), 3–62.
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