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@article{197911,
        author = {Yamini Krishnakumar Menon},
        title = {From Deskilling to Reskilling: Understanding Student Adaptation to Generative AI in Higher Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7118-7125},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197911},
        abstract = {The widespread adoption of generative artificial intelligence (AI) technologies like ChatGPT prompts significant inquiries regarding their effects on skill acquisition and educational results in higher education. This study investigates the impact of augmentative vs substitutive AI utilization on skill adaption mechanisms—deskilling, upskilling, and reskilling—and how these mechanisms subsequently affect learning outcomes. The study utilized a quantitative, cross-sectional survey with 122 higher education students from various fields, applying descriptive statistics, correlation analysis, multiple regression, and moderation analysis. Research indicates that the use of augmentative AI substantially fosters upskilling and reskilling, hence improving analytical capabilities and flexibility, but substitutive use correlates with deskilling. Upskilling and reskilling positively correlate with learning performance, but deskilling negatively impacts it when accounting for other factors. Contextual variables (task complexity, time constraint) did not significantly influence these associations, demonstrating their resilience across learning settings. The paper provides a mechanism-based paradigm for comprehending AI-mediated learning and presents consequences for educators, institutions, and politicians aiming for responsible AI integration.},
        keywords = {Deskilling, Generative AI, Higher Education, Learning outcomes, Reskilling, Skill Adaptation, Upskilling.},
        month = {April},
        }