From Algorithmic Scaffolding to Cognitive Withdrawal: A Three-Phase Classroom Experiment on AI Regulation in EFL Debates
Keywords:
AI Reliance, Algorithmic Scaffolding, Cognitive Offloading, Critical Thinking, DebateAbstract
AI complexifies the tensions of what it means to “know” a language and to perform that knowledge authentically in communicative settings. This study examines how varying levels of AI access reshape oral performance, linguistic choice, and critical engagement in EFL academic debates. 30 undergraduate students participated in a three-week structured debate intervention under escalating regulation: (1) unrestricted phone use, (2) AI reliance discouraged, and (3) complete device prohibition. Debate rounds were systematically coded for language use, response latency, and response quality. Results reveal a paradoxical trajectory of the eligible data from 18 students who participated through all debate rounds. In Week 1, unrestricted AI access produced fluent but script-dependent English delivery, characterized by delayed responses linked to live translation and reading behaviors. In Week 2, verbal warnings did not significantly reduce AI-script dependence. In Week 3, device prohibition eliminated scripted reading but led to increased hesitation and code-switching, with many immediate responses occurring in the native language rather than English. The findings introduce the construct of algorithmic scaffolding withdrawal, suggesting that abrupt removal of AI support exposes underlying linguistic insecurity rather than restoring spontaneous English fluency. The study challenges simplistic policy assumptions that banning AI enhances authenticity and argues for graduated scaffolding models that rebuild internalized language competence while preserving critical reasoning capacity.
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