Supplementary Files
Keywords
AI Literacy
Moral Competence
Digital Equity
Urban–Rural Divide
NEP 2020
Quasi-Experimental Design
Longitudinal Study
Mixed-Methods
Higher Education
Global South
How to Cite
Open Science Badges
Open Materials: https://alumnijournals.org/raie/article/view/1059/888
Abstract
Background: Generative artificial intelligence is reshaping higher education, yet ethical pedagogical integration lags in the Global South where infrastructural inequities risk widening disparities. India’s NEP 2020 calls for inclusive, interdisciplinary AI adoption, creating demand for rigorous evidence on effective models that cultivate technical competence and moral agency.
Objective: This study evaluated the efficacy of an ethical AI pedagogy integrating technical literacy, socio-technical ethics training, and offline delivery on AI literacy, ethical awareness, creativity, and collaboration, while examining moderation by gender, discipline, and urban–rural context in Indian HEIs.
Methods: A three-year (2022–2025) quasi-experimental mixed-methods study was conducted across 12 Indian HEIs with matched intervention (n=600) and control (n=568) student cohorts (total N=1,168 plus faculty and administrators). Pre-post assessments, reflective journals, focus groups, and learning analytics were analyzed using effect sizes and thematic synthesis. Procedures complied with ICMR (2017), Helsinki (2013), and DPDP Act (2023) standards.
Results: The intervention was associated with large gains in AI literacy (78.9%, d=0.80) and ethical awareness (76%, d=0.90), as well as improvements in creativity and collaboration. These effects were equitable across gender and discipline. Urban–rural contextual moderation was pronounced despite adaptations, yielding 16-point literacy (85.2% urban vs. 69.2% rural) and 23-point ethical awareness (85% vs. 62%) gaps. Qualitative insights affirmed emergent moral agency through fairness-by-design practices. Urban HEIs adopted curricula and policies more readily.
Conclusion: Findings indicate that ethical-technical AI pedagogy integration yields robust, largely equitable outcomes when institutional supports meet critical thresholds. The study offers replicable tools and a governance roadmap for Global South higher education, contingent on addressing infrastructural divides to achieve NEP 2020’s inclusivity and interdisciplinarity goals. It contributes to socio-technical conceptualizations of moral agency in human-AI educational hybrids.
References
Barczak, A. L., Mathrani, A., Han, B., & Reyes, N. H. (2023). Automated assessment system for programming courses: A case study for teaching data structures and algorithms. Educational Technology Research and Development, 71(6), 2365–2388. https://doi.org/10.1007/s11423-023-10277-2
Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4–17. https://doi.org/10.1016/j.intcom.2010.07.003
Challa, J. S. (2025). Sakshm AI: Advancing AI-assisted coding education for engineering students in India through Socratic tutoring and comprehensive feedback [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2503.12479
Córdova, P., Grájeda, A., Córdova, J. P., Vargas-Sánchez, A., Burgos, J., & Sanjinés, A. (2024). Leveraging AI tools in finance education: Exploring student perceptions, emotional reactions and educator experiences. Cogent Education, 11(1), Article 2431885. https://doi.org/10.1080/2331186X.2024.2431885
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6
Ellikkal, A., & Rajamohan, S. (2024). AI-enabled personalized learning: Empowering management students for improving engagement and academic performance. Vilakshan-XIMB Journal of Management, 22(1), 28–44. https://doi.org/10.1108/XJM-02-2024-0023
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), Article 57. https://doi.org/10.1186/s41239-022-00362-6
Facer, K., & Selwyn, N. (2021). Digital technology and the futures of education: Towards ‘Non-Stupid’ optimism. UNESCO Futures of Education initiative. https://unesdoc.unesco.org/ark:/48223/pf0000377071
Floridi, L. (2013). The ethics of information. Oxford University Press.
Goyal, H., Garg, G., Mordia, P., Ramachandran, V., Kumar, D., & Challa, J. S. (2025). The impact of large language models on K-12 education in rural India: A thematic analysis of student volunteer’s perspectives [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.03163
Gupta, K. P., & Bhaskar, P. (2020). Inhibiting and motivating factors influencing teachers’ adoption of AI-based teaching and learning solutions: Prioritization using analytic hierarchy process. Journal of Information Technology Education: Research, 19, 693–723. https://doi.org/10.28945/4640
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M. C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT), version 2018. Canadian Intellectual Property Office, Industry Canada. https://doi.org/10.1136/bmjopen-2020-039246
Indian Council of Medical Research. (2017). National ethical guidelines for biomedical and health research involving human participants. ICMR. https://ethics.ncdirindia.org/asset/pdf/ICMR_National_Ethical_Guidelines.pdf
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Joseph, G. V., Athira, P., Thomas, M. A., Jose, D., Roy, T. V., & Prasad, M. (2024). Impact of digital literacy, use of AI tools and peer collaboration on AI assisted learning: Perceptions of the university students. Digital Education Review, 45, 43–49. https://eric.ed.gov/?id=EJ1434328
Krishnan, S. (2025a). Harnessing generative AI in higher education: Opportunities, challenges, and strategies for responsible integration. International Journal of Emerging Technologies and Innovative Research, 12(8), 12–18. https://doi.org/10.56975/jetir.v12i8.568737
Krishnan, S. (2025b). Scoping the landscape: Opportunities, challenges, and strategies for generative AI in higher education. Indian Journal of Computer Science, 10(4), 8–19. https://doi.org/10.17010/ijcs/2025/v10/i4/175747
Krishnan, S. (2025c). Ethics-integrated AI pedagogy and metacognitive growth: A longitudinal quasi-experimental study in Indian higher education (SSRN Scholarly Paper No. 5526736). SSRN. https://doi.org/10.2139/ssrn.5526736
Ministry of Education, Government of India. (2020). National Education Policy 2020. https://www.education.gov.in/sites/upload_files/mhrd/files/NEP_Final_English_0.pdf
NITI Aayog. (2021). Towards responsible AI for all: Approach document for India part 1 – Principles for responsible AI. https://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
Pardos, Z. A., Tang, M., Anastasopoulos, I., Sheel, S. K., & Zhang, E. (2023). OATutor: An open-source adaptive tutoring system and curated content library for learning sciences research. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Article 416, pp. 1–17). https://doi.org/10.1145/3544548.3581574
Ruiz-Rojas, L. I., Salvador-Ullauri, L., & Acosta-Vargas, P. (2024). Collaborative working and critical thinking: Adoption of generative artificial intelligence tools in higher education. Sustainability, 16(13), Article 5367. https://doi.org/10.3390/su16135367
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Smith, M., & Neupane, S. (2022). Artificial intelligence and human development: Toward a research agenda. International Development Research Centre. http://hdl.handle.net/10625/60907
Tang, Y., & Hare, R. (2023). Combining gamification and intelligent tutoring systems in a serious game for engineering education [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2305.16568
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Usher, M., & Barak, M. (2024). Unpacking the role of AI ethics online education for science and engineering students. International Journal of STEM Education, 11(1), Article 35. https://doi.org/10.1186/s40594-024-00493-4
Wang, C. (2024). Exploring students’ generative AI-assisted writing processes: Perceptions and experiences from native and nonnative English speakers. Technology, Knowledge and Learning, 29(3), 1–22. https://doi.org/10.1007/s10758-024-09744-3
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da
Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, Article 160018. https://doi.org/10.1038/sdata.2016.18
World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053
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