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@article{190102,
        author = {Raghuvaran A P and Dr. M. Annapoorani},
        title = {Intelligent Academic Systems and Cognitive Outcomes: A Task–Technology Fit Perspective on Artificial Intelligence Adoption in Higher Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2868-2872},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190102},
        abstract = {Artificial intelligence (AI) has rapidly evolved from a peripheral educational technology to a central component of intelligent academic systems. AI-driven tools such as intelligent tutoring systems, learning analytics platforms, automated assessment engines, and generative chatbots increasingly shape teaching, learning, and academic decision-making. While existing research on AI adoption in higher education has largely relied on perception-based acceptance models, comparatively limited attention has been paid to how the alignment between academic tasks and intelligent system capabilities determines both adoption outcomes and cognitive consequences. Addressing this gap, this paper adopts the Technology–Task Fit (TTF) framework as its primary analytical lens to examine the determinants of AI adoption and its impact on Higher-Order Thinking Skills (HOTS) in higher education.
Using a structured theoretical synthesis of empirical and systematic studies, the paper reconceptualizes AI as a form of cognitive computing and intelligent automation whose educational effectiveness depends on task alignment rather than technological sophistication alone. Extending traditional TTF outcomes, the study reframes “performance” as cognitive learning outcomes, emphasizing critical thinking, problem-solving, creativity, and metacognitive regulation. The analysis demonstrates that AI enhances higher-order cognition when it functions as a cognitive scaffold supporting human reasoning. Conversely, excessive automation leads to task–technology misfit, diminishing cognitive engagement and risking superficial learning. The paper contributes to AI and education research by integrating intelligent systems theory with learning outcomes and offers practical guidance for ethically and pedagogically aligned AI deployment in higher education.},
        keywords = {Artificial intelligence; Intelligent systems; Cognitive computing; Task–Technology Fit; Smart learning applications; Higher-Order Thinking Skills; Ethical AI in education},
        month = {January},
        }