The Cognitive Exoskeleton and the E.F.F.E.C.T. Framework: Scaffolding Affective Shifts and Self-Regulated Learning in L2 Writing – A Exploratory Micro-Genetic Pilot Study Chunwing Wong 1*, Lamtai Lee2

Abstract

Hong Kong primary students frequently experience Foreign Language Writing Anxiety (FLWA), a state where the fear of error is theorized to inhibit the neural mechanisms required for language production. Building on recent empirical evidence that generative AI can alleviate FLWA by providing non-judgmental feedback, this study proposes a paradigm shift from "AI as a Content Generator" to "AI as a Cognitive Exoskeleton". Utilizing the E.F.F.E.C.T. Framework, we designed a Generative AI agent to scaffold the Self-Regulated Learning (SRL) cycle in L2 writing. Through an exploratory micro-genetic case study (N=5) incorporating pre- and post-intervention FLWA measurements, this Design-Based Research (DBR) investigates how "Neuro-pedagogical Prompt Engineering" is designed to support the down-regulation of the amygdala’s threat response and engage the prefrontal cortex for executive planning. Behavioral observations suggest that when AI feedback is framed as Reward Prediction Error rather than social judgment, students shift from passive correction to active inquiry, effectively transforming the writing process from a threat-induced "freeze" toward a more dopamine-driven "flow".