Presentation Beyond the Summary Trap: Reframing Literacy and Competency in AI-Mediated Language Learning more

In the era of large language models (LLMs), Computer-Assisted Language Learning (CALL) has shifted from tool-use to complex human-AI partnership. However, anecdotal evidence highlights that the prevalence of fragmented AI outputs—the "summary trap"—poses a significant threat to critical literacy and deep cognitive and emotional engagement. This talk outlines a pilot study in which a dual-framework approach to AI integration in higher education, specifically focusing on university contexts in the Philippines and Japan. By distinguishing between AI Literacy (metaliteracy and critical discernment of algorithmic bias) and AI Competency (the strategic orchestration of LLMs within a Human-in-the-Loop workflow), this research explores how learners can maintain language learner agency. Integrating perspectives from Human-Computer Interaction (HCI) and Cultural Anthropology, this talk explores the local concepts of Ginhawa (Philippines) and Ikigai (Japan) as ethical anchors for digital mindfulness, highlighting the necessity of "whole-text" pedagogy to combat cognitive narrowing, ensuring that language learners develop the analytical thinking and resilience required in a generative AI labor market.