AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia
Abstract
1. Introduction
2. Related Work
2.1. AI Literacy and Technology Adoption in a Global Context
2.2. Gender Bias in STEM and AI Across Cultures
2.3. Comparative Context: UK and Indonesia
3. Materials and Methods
- RQ1: How does AI literacy differ between the UK and Indonesia?
- RQ2: How do perceptions of gender bias in the technology field differ between respondents in the UK and Indonesia?
3.1. Research Design
3.2. Participants
3.3. Instruments
- Familiarity with AI concepts: Participants’ awareness of AI systems and their ability to identify them.
- Confidence in AI tools: The extent to which respondents feel comfortable using AI applications.
- Discussion of AI ethics: Engagement in conversations about the ethical and societal impact of AI.
- No AI Literacy: Respondents scoring in the bottom quintile (≤20th percentile) on our composite AI literacy index, indicating minimal awareness of AI concepts and tools.
- Basic AI Literacy: Those scoring between the 21st and 60th percentiles, reflecting familiarity with AI terminology and limited hands-on experience (e.g., can identify common AI applications but lack deeper understanding).
- Advanced AI Literacy: Respondents in the top 40% (≥61st percentile), demonstrating both conceptual understanding (e.g., algorithmic principles) and practical skills (e.g., regular use of AI tools for problem-solving), as well as engagement with ethical and societal implications.
- Witnessed gender bias: Whether participants have observed gender discrimination in AI-related environments.
- Availability of mentorship programs for women: Awareness of structured initiatives supporting women’s participation in AI and STEM fields.
- Opportunities for women in leadership: Perceived frequency of women’s promotion into leadership positions.
- Challenges for women in tech careers: Agreement with statements about barriers that women face in entering and advancing in AI and STEM fields.
- Prevalence of gender stereotypes: How often respondents encounter gender-based stereotypes in technology-related roles.
3.4. Analysis Methods
4. Results
4.1. Analysis of AI Literacy
- For AI concept familiarity, the F-test showed F(60, 130) = 1.386, p > 0.05. As we failed to reject the null hypothesis of equal variances, equal variances were assumed for the subsequent t-test.
- For AI tool confidence, the F-test showed F(60, 130) = 1.228, p > 0.05. As we failed to reject the null hypothesis of equal variances, equal variances were assumed for the subsequent t-test.
- For discussion of AI ethics, the F-test showed F(60, 130) = 1.194, p > 0.05. As we failed to reject the null hypothesis of equal variances, equal variances were assumed for the subsequent t-test.
- Writing scientific papers;
- Completing university assignments;
- Reviewing study materials;
- Enhancing work quality and efficiency.
4.2. Analysis of Gender Bias
5. Discussion
5.1. Implications
5.2. Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bahagijo, S., Prasetyo, Y. E., Kawuryan, D., Tua, B., & Eridani, A. D. (2022). Closing the digital gender gap in Indonesia through the roles and initiatives of civil society organizations. Jurnal Ilmu Sosial, 21(1), 14–38. [Google Scholar] [CrossRef]
- Bennaceur, A., Cano, A., Georgieva, L., Kiran, M., Salama, M., & Yadav, P. (2018, May 27–June 3). Issues in gender diversity and equality in the UK. 2018 IEEE/ACM 1st International Workshop on Gender Equality in Software Engineering (GE) (pp. 5–9), Gothenburg, Sweden. [Google Scholar] [CrossRef]
- Broo, D. G., Kaynak, O., & Sait, S. M. (2022). Rethinking engineering education at the age of industry 5.0. Journal of Industrial Information Integration, 25, 100311. [Google Scholar] [CrossRef]
- Cachero, C., Tomás, D., & Pujol, F. A. (2025). Gender bias in self-perception of artificial intelligence knowledge, impact, and support among higher education students: An observatioanal study. ACM Transactions on Computing Education, 25(2), 15. [Google Scholar] [CrossRef]
- Callea, V., Dagklis, E., Nantsou, T., Otegui, X., Tovar, E., & Villa, G. (2024, May 8–11). Factors influencing women’s underrepresentation in engineering: A literature review at EDUCON. 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–10), Kos, Greece. [Google Scholar] [CrossRef]
- Casad, B. J., Franks, J. E., Garasky, C. E., Kittleman, M. M., Roesler, A. C., Hall, D. Y., & Petzel, Z. W. (2021). Gender inequality in academia: Problems and solutions for women faculty in STEM. Journal of Neuroscience Research, 99(1), 13–23. [Google Scholar] [CrossRef]
- Christie, M., O’Neill, M., Rutter, K., Young, G., & Medland, A. (2017). Understanding why women are under-represented in science, technology, engineering and mathematics (STEM) within higher education: A regional case study. Production, 27, e20162205. [Google Scholar] [CrossRef]
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [CrossRef]
- Druga, S., Otero, N., & Ko, A. J. (2022, July 11–13). The landscape of teaching resources for ai education. 27th ACM Conference on Innovation and Technology in Computer Science Education (Vol. 1, pp. 96–102), Dublin, Ireland. [Google Scholar]
- Elysee_Palace. (2025, December 2). Statement on inclusive and sustainable artificial intelligence for people and the planet. Elysee Palace. Available online: https://www.elysee.fr/en/emmanuel-macron/2025/02/11/statement-on-inclusive-and-sustainable-artificial-intelligence-for-people-and-the-planet (accessed on 14 February 2025).
- Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. [Google Scholar] [CrossRef]
- González-Pérez, S., Martínez-Martínez, M., Rey-Paredes, V., & Cifre, E. (2022). I am done with this! Women dropping out of engineering majors. Frontiers in Psychology, 13, 918439. [Google Scholar] [CrossRef] [PubMed]
- Hobeika, E., Hallit, R., Malaeb, D., Sakr, F., Dabbous, M., Merdad, N., Rashid, T., Amin, R., Jebreen, K., Zarrouq, B., Alhuwailah, A., Shuwiekh, H. A. M., Hallit, S., Obeid, S., & Fekih-Romdhane, F. (2024). Multinational validation of the Arabic version of the artificial intelligence literacy scale (AILS) in university students. Cogent Psychology, 11(1), 2395637. [Google Scholar] [CrossRef]
- Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage Publications. [Google Scholar]
- Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.). McGraw Hill LLC. [Google Scholar]
- Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165. [Google Scholar] [CrossRef]
- Hornberger, M., Bewersdorff, A., Schiff, D. S., & Nerdel, C. (2025). A multinational assessment of AI literacy among university students in Germany, the UK, and the US. Computers in Human Behavior: Artificial Humans, 4, 100132. [Google Scholar] [CrossRef]
- Hossain, Z., Biswas, M. S., & Khan, G. (2025). AI literacy of library and information science students: A study of Bangladesh, India and Pakistan. Journal of Librarianship and Information Science, 09610006241309323. [Google Scholar] [CrossRef]
- Jain, H., Padmanabhan, B., Pavlou, P. A., & Raghu, T. S. (2021). Editorial for the special section on humans, algorithms, and augmented intelligence: The future of work, organizations, and society. Information Systems Research, 32(3), 675–687. [Google Scholar] [CrossRef]
- Kasinidou, M. (2023, July 7–12). AI literacy for all: A participatory approach. 2023 Conference on Innovation and Technology in Computer Science Education (Vol. 2, pp. 607–608), Turku, Finland. [Google Scholar] [CrossRef]
- Kazanidis, I., & Pellas, N. (2024). Harnessing generative artificial intelligence for digital literacy innovation: A comparative study between early childhood education and computer science undergraduates. AI, 5(3), 1427–1445. [Google Scholar] [CrossRef]
- Kong, S.-C., Korte, S.-M., Burton, S., Keskitalo, P., Turunen, T., Smith, D., Wang, L., Lee, J. C.-K., & Beaton, M. C. (2024). Artificial intelligence (AI) literacy—An argument for AI literacy in education. Innovations in Education and Teaching International, 62(2), 477–483. [Google Scholar] [CrossRef]
- Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: A cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24(1), 401. [Google Scholar] [CrossRef]
- Laupichler, M. C., Aster, A., Perschewski, J.-O., & Schleiss, J. (2023). Evaluating AI courses: A valid and reliable instrument for assessing artificial-intelligence learning through comparative self-assessment. Education Sciences, 13(10), 978. [Google Scholar] [CrossRef]
- Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. [Google Scholar] [CrossRef]
- Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025, April 26–May 1). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. [Google Scholar] [CrossRef]
- Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021, March 13–20). Developing middle school students’ AI literacy. 52nd ACM Technical Symposium on Computer Science Education (pp. 191–197), Virtual Event. [Google Scholar] [CrossRef]
- Lemus-Delgado, D., & Cerda, C. (2025). ASEAN, gender equality and women’s empowerment in STEM. Asian Education and Development Studies, 14(2), 299–313. [Google Scholar] [CrossRef]
- Li, L. (2022). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers, 26, 1697–1712. [Google Scholar] [CrossRef]
- Long, D., & Magerko, B. (2020, April 25–30). What is AI literacy? Competencies and design considerations. 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16), Honolulu, HI, USA. [Google Scholar] [CrossRef]
- Morais Maceira, H. (2017). Economic benefits of gender equality in the EU. Intereconomics, 52(3), 178–183. [Google Scholar] [CrossRef]
- Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. [Google Scholar] [CrossRef]
- Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. [Google Scholar] [CrossRef]
- Nweje, U., Amaka, N. S., & Makai, C. C. (2025). Women in STEM: Breaking barriers and building the future. International Journal of Science and Research Archive, 14(1), 202–217. [Google Scholar] [CrossRef]
- Pal, S., Lazzaroni, R. M., & Mendoza, P. (2024). AI’s missing link: The gender gap in the talent pool. Available online: https://www.stiftung-nv.de/publications/ai-gender-gap (accessed on 1 March 2025).
- Piloto, C. (2023). The gender gap in STEM: Still gaping in 2023. MIT Professional Education. [Google Scholar]
- Ramseook-Munhurrun, P., Naidoo, P., & Armoogum, S. (2025). Navigating the challenges of female leadership in the information and communication technology and engineering sectors. Journal of Business and Socio-Economic Development, 5(1), 55–70. [Google Scholar] [CrossRef]
- Roopaei, M., Horst, J., Klaas, E., Foster, G., Salmon-Stephens, T. J., & Grunow, J. (2021, May 10–13). Women in AI: Barriers and solutions. 2021 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA. [Google Scholar] [CrossRef]
- Rubin, C., & Utomo, E. (2022). Strengthening ASEAN women’s participation in STEM. Available online: https://asean.org/wp-content/uploads/2023/10/Policy-Brief-Strengthening-ASEAN-Womens-Participation-in-STEM-Endorsed.FINAL_.pdf (accessed on 1 March 2025).
- Sari, D. K., Supahar, S., Rosana, D., Dinata, P. A., & Istiqlal, M. (2025). Measuring artificial intelligence literacy: The perspective of Indonesian higher education students. Journal of Pedagogical Research, 9(2), 143–157. [Google Scholar] [CrossRef]
- Schwab, K., Samans, R., Zahidi, S., Leopold, T. A., Ratcheva, V., Hausmann, R., & Tyson, L. D. (2021). The global gender gap report 2021. World Economic Forum. Available online: https://www3.weforum.org/docs/WEF_GGGR_2021.pdf (accessed on 31 January 2025).
- Secretary of State for Digital, Culture, Media and Sport. (2021). National AI strategy. Command Paper 525. HM Government. Available online: https://www.gov.uk/government/publications/national-ai-strategy (accessed on 31 January 2025).
- Shah, S. S. (2024). Gender bias in artificial intelligence: Empowering women through digital literacy. Journal of Artificial Intelligence, 1, 1000088. [Google Scholar] [CrossRef]
- Sulmont, E., Patitsas, E., & Cooperstock, J. R. (2019, February 27–March 2). Can you teach me to machine learn? 50th ACM Technical Symposium on Computer Science Education (pp. 948–954), Minneapolis, MN, USA. [Google Scholar] [CrossRef]
- TheCultureFactor. (n.d.). Country comparison tool. Available online: https://www.hofstede-insights.com/country-comparison/ (accessed on 6 February 2025).
- Tortoise. (2024). The global AI index. Available online: https://www.tortoisemedia.com/intelligence/global-ai (accessed on 1 March 2025).
- Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5–27. [Google Scholar] [CrossRef]
- Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. [Google Scholar] [CrossRef]
- West, S. M. (Director). (2020, February 25). Discriminating systems: Gender, race and power in artificial intelligence [Video recording]. AI Now Institute. Available online: http://hdl.handle.net/1853/62480 (accessed on 14 February 2025).
- Young, E., Wajcman, J., & Sprejer, L. (2021). Where are the women? Mapping the gender job gap in AI. The Alan Turing Institute. [Google Scholar]
- Zheng, H., Li, W., & Wang, D. (2022). Expertise diversity of teams predicts originality and long-term impact in science and technology. arXiv, arXiv:2210.04422. [Google Scholar] [CrossRef]
AI Components | Description |
---|---|
Familiarity | Level of awareness about AI systems, ability to identify them, and practical experience using AI tools |
Knowledge and application | Understanding of AI concepts combined with technical expertise in implementing and utilizing AI technologies |
Ethical perceptions | Views on how AI affects academic integrity and its broader societal implications |
Indonesia (n = 131) | UK (n = 61) |
---|---|
Age distribution: | Age distribution: |
18–24 years: 45% | 18–24 years: 33% |
25–34 years: 38% | 25–34 years: 41% |
35–44 years: 12% | 35–44 years: 20% |
45+ years: 5% | 45+ years: 6% |
Median age range: 25–34 years | Median age range: 25–34 years |
Indonesia | UK | t | df | p-Value | CI 95% | ||
---|---|---|---|---|---|---|---|
Mean ± SD | Mean ± SD | Lower | Upper | ||||
Familiarity with AI Concepts | 2.47 ± 0.79 | 2.97 ± 0.93 | 3.8550 | 190 | 0.0002 | −0.7558 | −0.2442 |
Confidence in AI Tools | 4.05 ± 0.74 | 4.08 ± 0.82 | 0.2526 | 190 | 0.8008 | −0.2643 | 0.2043 |
Discussion of AI Ethics | 2.66 ± 0.97 | 2.74 ± 1.06 | 0.5165 | 190 | 0.6061 | −0.3855 | 0.2255 |
Gender | No AI Literacy | Basic Literacy | Advanced Literacy | |
---|---|---|---|---|
Indonesia | Female | 74.24 | 12.12 | 13.64 |
Male | 60 | 21.54 | 18.46 | |
UK | Female | 42.31 | 30.77 | 26.92 |
Male | 34.29 | 20 | 45.71 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tunjungbiru, A.D.; Pranggono, B.; Sari, R.F.; Sanchez-Velazquez, E.; Purnamasari, P.D.; Liliana, D.Y.; Andryani, N.A.C. AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia. Educ. Sci. 2025, 15, 1143. https://doi.org/10.3390/educsci15091143
Tunjungbiru AD, Pranggono B, Sari RF, Sanchez-Velazquez E, Purnamasari PD, Liliana DY, Andryani NAC. AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia. Education Sciences. 2025; 15(9):1143. https://doi.org/10.3390/educsci15091143
Chicago/Turabian StyleTunjungbiru, Amrita Deviayu, Bernardi Pranggono, Riri Fitri Sari, Erika Sanchez-Velazquez, Prima Dewi Purnamasari, Dewi Yanti Liliana, and Nur Afny Catur Andryani. 2025. "AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia" Education Sciences 15, no. 9: 1143. https://doi.org/10.3390/educsci15091143
APA StyleTunjungbiru, A. D., Pranggono, B., Sari, R. F., Sanchez-Velazquez, E., Purnamasari, P. D., Liliana, D. Y., & Andryani, N. A. C. (2025). AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia. Education Sciences, 15(9), 1143. https://doi.org/10.3390/educsci15091143