AI-enhanced education: exploring the impact of AI literacy on generation Z’s academic performance in Northern India
Abstract
Purpose Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context. Design/methodology/approach The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi. Findings The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students. Research limitations/implications The implications of the study include the significance of integrating AI education initiatives into curricula, prioritisin…
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AI literacy is highly correlated with productive academic use of GenAI.
“As GenAI becomes increasingly embedded in education and professional practice, AI literacy is highly correlated with productive academic use of GenAI (Tzirides et al. 2024; Xiao et al. 2024; Singh et al. 2025).”
From Generative AI Expands the Intellectual Reach of Course Based Undergraduate Research Experiences (CUREs) (2026) · cites this paper as Singh et al. 2025
Cites
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · see citing works
Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463 · see citing works
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · see citing works
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228 · see citing works
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089 · see citing works
Role of AI chatbots in education: systematic literature review
Labadze, L., Grigolia, M., & Machaidze, L. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00426-1 · Cited by 1,043 · see citing works
Wang, B., Rau, P.-L. P., & Yuan, T. (2023) · Behaviour & Information Technology · doi:10.1080/0144929x.2022.2072768 · Cited by 1,036 · see citing works
Artificial Intelligence trends in education: a narrative overview
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.08.233 · Cited by 956 · see citing works
Interacting with educational chatbots: A systematic review
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11177-3 · Cited by 841 · see citing works
The impact of artificial intelligence on learner–instructor interaction in online learning
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00292-9 · Cited by 788 · see citing works
Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2023) · 2023 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon54358.2023.10125121 · Cited by 707 · see citing works
Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions
Kooli, C. (2023) · Sustainability · doi:10.3390/su15075614 · Cited by 666 · see citing works
Chan, C. K. Y., & Lee, K. K. W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00269-3 · Cited by 634 · see citing works
Predicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355 · see citing works
Alshater, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4312358 · Cited by 304 · see citing works
Alotaibi, N. S., & Alshehri, A. H. (2023) · Sustainability · doi:10.3390/su151310723 · Cited by 181 · see citing works
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Bećirović, S., Polz, E., & Tinkel, I. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00384-3 · Cited by 133 · see citing works
Artificial intelligence literacy among university students—a comparative transnational survey
Mansoor, H. M. H., Bawazir, A., Alsabri, M. A., Alharbi, A., & Okela, A. H. (2024) · Frontiers in Communication · doi:10.3389/fcomm.2024.1478476 · Cited by 84 · see citing works
Ding, N., Chen, M., & Hu, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105650 · Cited by 11 · see citing works
Babar, A., Davin, K. J., & Dornburg, A. (2026) · arXiv · doi:10.48550/arxiv.2608.27638
“As GenAI becomes increasingly embedded in education and professional practice, AI literacy is highly correlated with productive academic use of GenAI (Tzirides et al. 2024; Xiao et al. 2024; Singh et al. 2025).”
- Authors
- Singh, E., Vasishta, P., & Singla, A.
- Affiliations
- Punjab Engineering College (IN)
- Corresponding author
- Vasishta, Prihana
- Venue
- Quality Assurance in Education
- Publisher
- Emerald
- Published
- 2024-08-28
- Volume
- 33
- Issue
- 2
- Pages
- 185-202
- ISSN
- 0968-4883 (ISSN-L)
- OpenAlex
- W4401931688
- Cited by
- 67
- References
- 17
- Citations by year
- 2026: 31 · 2025: 33 · 2024: 3
- FWCI
- 25.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Quality Assurance in Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- AI literacy, artificial intelligence in education, Generation Z students, academic performance, artificial intelligence use, learning outcomes, student engagement with AI, critical thinking, problem-solving skills, teacher professional development, equitable access to AI, North India
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