AI-Supported Learning and Its Relationship with Academic Procrastination and Academic Anxiety among Students with Visual Impairment
DOI:
https://doi.org/10.57235/jpa.v5i2.9168Keywords:
Artificial Intelligence, Academic Procrastination, Academic Anxiety, Visual Impairment, Inclusive EducationAbstract
Artificial Intelligence (AI) has emerged as a transformative technology in higher education, offering innovative solutions to improve accessibility, personalized learning, and academic support for students with disabilities. The present study investigated the influence of AI utilization on academic procrastination and academic anxiety among students with visual impairment. A longitudinal mixed-methods research design was employed involving 24 students with visual impairment from higher educational institutions in Kanpur, Uttar Pradesh, India. Participants were selected through purposive sampling and observed over one academic semester. Quantitative data were collected using standardized measures of AI utilization, academic procrastination, and academic anxiety at three different time points, while qualitative data were gathered through semi-structured interviews, reflective audio journals, and AI usage logs. Quantitative data were analysed using descriptive statistics, Pearson correlation, hierarchical linear modelling, and multiple regression analysis, whereas qualitative data were analysed using thematic analysis. The findings revealed that AI utilization increased progressively throughout the semester, while both academic procrastination and academic anxiety demonstrated a steady decline. Correlation analysis indicated strong negative relationships between AI utilization and academic procrastination (r = –0.92) and between AI utilization and academic anxiety (r = –0.88). Regression analysis further confirmed that AI utilization significantly predicted reductions in both academic procrastination and academic anxiety. Qualitative findings supported the quantitative results by identifying accessibility, improved time management, better assignment planning, increased learning confidence, and reduced academic anxiety as the major perceived benefits of AI-assisted learning. Although a few participants expressed concerns regarding excessive dependence on AI, the overall educational benefits substantially outweighed these concerns. The study concludes that AI-assisted learning has considerable potential to promote accessible, independent, and inclusive education for students with visual impairment. By enhancing academic organization, reducing psychological barriers, and improving learner confidence, AI contributes to both academic success and emotional well-being. The findings highlight the importance of integrating AI-based educational technologies into inclusive higher education while promoting responsible and ethical AI use through appropriate institutional support and digital literacy initiatives.
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References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00444-7
Ajlouni, A., AlOmary, A., Wahba, F. A.-A., Al-Kubaisi, H., & Ibrahim, A. (2025). The Intensity of Using ChatGPT in Relation to Academic Stress: A Cross-Sectional Study. International Journal of Interactive Mobile Technologies (iJIM), 19(17), 81–100. https://doi.org/10.3991/ijim.v19i17.56495
Alsamiri, Y. A. (2025). Challenges faced by visually impaired individuals from the perspective of faculty members: a phenomenological study. Frontiers in Psychology, 16, 1651597–1651597. https://doi.org/10.3389/fpsyg.2025.1651597
Aruleba, K., Sanusi, I. T., Obaido, G., Ogbuokiri, B., & Mienye, I. D. (2025). Beyond the Prompt: Student Strategies, Ethical Reflections, and Learning with ChatGPT in Computer Science. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-025-09899-7
Chan, C. K. Y., & Colloton, T. (2024). Generative AI in Higher Education. https://doi.org/10.4324/9781003459026
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(1). https://doi.org/10.1186/s41239-023-00411-8
Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: a case of ChatGPT. Educational Technology Research and Development, 72(4), 2401–2416. https://doi.org/10.1007/s11423-024-10366-w
Darvishy, A., Anzolut, S., Pierrès, O., & Schmitt, F. (2024). User Study: Generative AI-based scientific writing assistant for students with visual impairments. AHFE International, 121. https://doi.org/10.54941/ahfe1004615
Elshaer, I. A., AlNajdi, S. M., & Salem, M. A. (2025a). Sustainable AI Solutions for Empowering Visually Impaired Students: The Role of Assistive Technologies in Academic Success. Sustainability, 17(12), 5609–5609. https://doi.org/10.3390/su17125609
Elshaer, I. A., AlNajdi, S. M., & Salem, M. A. (2025b). Measuring the Impact of Large Language Models on Academic Success and Quality of Life Among Students with Visual Disability: An Assistive Technology Perspective. Bioengineering, 12(10), 1056–1056. https://doi.org/10.3390/bioengineering12101056
Elshaer, I. A., AlNajdi, S. M., & Salem, M. A. (2025c). AI-Assistive Technology Adoption and Mental Health Disorders in Visually Impaired University Students. Electronics, 14(20), 4036–4036. https://doi.org/10.3390/electronics14204036
Eradze, M., Manna, M., Sunar, A. S., Dovigo, F., & Ianes, D. (2026). Editorial: Harnessing generative AI for inclusive education: opportunities and challenges. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1765687
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, T., Shen, Y., Li, X., & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Huang, T., & Wu, C. (2025). The chain mediating effect of academic anxiety and performance expectations between academic self-efficacy and generative AI reliance. Computers and Education Open, 9, 100275–100275. https://doi.org/10.1016/j.caeo.2025.100275
Hwang, Y., & Wu, Y. (2025). The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety. Frontiers in Psychology, 15, 1455015–1455015. https://doi.org/10.3389/fpsyg.2024.1455015
Jukiewicz, M. (2025). How generative artificial intelligence transforms teaching and influences student wellbeing in future education. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1594572
Khlaif, Z. N., Alshakhshir, R., Hamamra, B., & Joma, A. (2026). Assistive generative AI for visually impaired learners: Personalization and inclusion in higher education. International Journal of Artificial Intelligence in Education, 36, 100008–100008. https://doi.org/10.1016/j.ijaied.2026.100008
Kim, J., Yu, S., Detrick, R., & Li, N. (2024). Exploring students’ perspectives on Generative AI-assisted academic writing. Education and Information Technologies, 30(1), 1265–1300. https://doi.org/10.1007/s10639-024-12878-7
Kościelniak, M., & Bielecki, M. (2024). The Psychology of Generative AI in Higher Education: Mapping Benefits and Risks. In Qeios. https://doi.org/10.32388/x1itss
Li, L. (2025). The psychological impact of generative AI on L2 graduate students research writing: a mixed-methods investigation. Advances in Humanities Research, 12(1), 86–89. https://doi.org/10.54254/2753-7080/2025.22907
Liu, X., Liu, Y., Dai, Y., & Fu, J. (2026). Academic stress and university students’ dependency on generative artificial intelligence: a multiple mediation model using PLS-SEM. BMC Psychology, 14(1), 216–216. https://doi.org/10.1186/s40359-026-03986-9
Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1–8. https://doi.org/10.14742/ajet.8695
Maphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024). Advancing Students’ Academic Excellence in Distance Education: Exploring the Potential of Generative AI Integration to Improve Academic Writing Skills. Open Praxis, 16(2), 142–159. https://doi.org/10.55982/openpraxis.16.2.649
Min, S., & Lee, J. S. (2026). A Study on Generative AI Use Experiences of University Students with Visual Impairments: Focusing on GenAI - Augmented Accessibility in Academic Performance -. Korean Journal of Social Welfare, 78(1), 7–38. https://doi.org/10.20970/kasw.2026.78.1.001
Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593
Pierrès, O., Darvishy, A., & Christen, M. (2024). Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities. Education and Information Technologies, 30(7), 8923–8952. https://doi.org/10.1007/s10639-024-13134-8
Ram, A. M., Saravi, S., Yang, Y., Batmaz, F., Derakhshan, P., Nevisi, H., & Storey, G. (2025). The AI Scaffold and Engagement Spectrum as a Novel UDL-Aligned System for Supporting Students with Dyslexia. In Research Square. https://doi.org/10.21203/rs.3.rs-7976947/v1
Ram, A. M., Saravi, S., Yang, Y., Batmaz, F., Derakhshan, P., Nevisi, H., & Storey, G. (2026). The AI scaffold and engagement spectrum as a novel UDL-aligned system for supporting students with dyslexia. Education and Information Technologies. https://doi.org/10.1007/s10639-026-13934-0
Rappa, N. A., Nonis, K. P., Tang, K., Cooper, G., Cooper, M., & Sims, C. (2026). Can Generative AI support the learning agency of students with disability? A case study of an Australian secondary school. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70048
Riley, C., Alrefai, O., Reyes, Y. C., & Hammad, E. (2025). Human-AI Interactions: Cognitive, Behavioral, and Emotional Impacts. In ArXiv.org. https://doi.org/10.48550/arxiv.2510.17753
Roy, F. G., & Johnson, F. (2025). Artificial intelligence for individuals with disabilities in higher education institutions: a systematic review. IAES International Journal of Artificial Intelligence, 14(6), 4454–4454. https://doi.org/10.11591/ijai.v14.i6.pp4454-4460
Sharma, S. (2026). Mental health and academic adjustment among visually impaired and sighted students. International Journal of Applied Research, 12(2), 48–51. https://doi.org/10.22271/allresearch.2026.v12.i2a.13417
Skulmowski, A. (2024). Placebo or Assistant? Generative AI Between Externalization and Anthropomorphization. Educational Psychology Review, 36(2). https://doi.org/10.1007/s10648-024-09894-x
Wang, C. (2024). Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers. Technology Knowledge and Learning, 30(3), 1825–1846. https://doi.org/10.1007/s10758-024-09744-3
Wang, D., & Wang, Z. L. (2026). How academic stress leads to artificial intelligence-generated design dependency: the roles of academic procrastination and help-seeking behavior. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1794730
Wang, F., Li, N., Cheung, A., & Wong, G. K. W. (2025). In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00547-9
Xiong, Z., Li, G., Chen, J., & Li, P. (2024). The Impact of Executive Dysfunction on Anxiety in Hearing-Impaired College Students: Smartphone Addiction as a Mediator and Academic Procrastination as a Moderator. PubMed Central, 2853–2863. https://doi.org/10.2147/prbm.s470293
Xu, X., & Yu, C. (2025). Understanding self-regulated learning through Ridit analysis: an in-depth study of graduate students’ perceptions. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1609448
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00467-0
Zhao, W. X., Chen, X., & Cox, A. (2025). Exploring the Affordances of Generative AI in Academic Writing for Disabled Student. ScholarSpace (University of Hawaii at Manoa). https://doi.org/10.24251/hicss.2025.595
Zhao, X., Chen, X., & Cox, A. (2025). Exploring the affordances of generative AI in academic writing for students with disabilities: A bottom-up approach to inform GenAI policies. Policy Futures in Education, 24(1), 59–81. https://doi.org/10.1177/14782103251395436
Zhao, X., Cox, A., & Chen, X. (2025). The use of generative AI by students with disabilities in higher education. The Internet and Higher Education, 66, 101014–101014. https://doi.org/10.1016/j.iheduc.2025.101014
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