The Algorithmic Scaffold: A Phenomenological Study of Artificial Intelligence's Impact on EFL Learners' Higher-Order Thinking Skills through the Lens of Motivational Self-Determination
Journal: Journal of Higher Education Research DOI: 10.32629/jher.v7i1.4959
Abstract
The rapid integration of Artificial Intelligence (AI) into higher education has precipitated a paradigm shift in English as a Foreign Language (EFL) pedagogy. While the utility of AI for basic linguistic acquisition is well-documented, its influence on Higher-Order Thinking Skills (HOTS) — specifically analyzing, evaluating, and creating — remains a subject of pedagogical debate. Drawing upon management motivation theories, specifically Self-Determination Theory (SDT), this study adopts a qualitative phenomenological research design to explore the lived experiences of first-year EFL undergraduates interacting with AI tools. Semi-structured interviews were conducted with 15 participants randomly selected from five distinct academic cohorts. The findings reveal a complex duality: while AI functions as a "competence multiplier" that scaffolds analytical thinking and reduces cognitive load, it simultaneously risks undermining learner autonomy by fostering an over-reliance that inhibits evaluative rigor. Drawing on Self-Determination Theory and management efficiency models, this study contends that the predominance of extrinsic efficiency-driven AI use — without intrinsic motivational regulation — may lead to the gradual erosion of evaluative and creative cognitive faculties, a phenomenon we term algorithmic dependency.
Keywords
Artificial Intelligence (AI); Higher-Order Thinking Skills (HOTS); EFL; Self-Determination Theory; phenomenology; management in education
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