Affective Computing in Language Education: An Interdisciplinary Review of AI's Role in Mediating Learner Anxiety and Motivation
DOI:
https://doi.org/10.54097/z4a0yh52Keywords:
Artificial Intelligence, Second Language Acquisition, Affective Filter Hypothesis, Self-Determination Theory, Learner Anxiety, Affective Computing, Educational Technology.Abstract
Language learning is not just a cognitive task; it is a deeply emotional one. When learners feel anxious or unmotivated, a barrier can form that blocks learning, a phenomenon Stephen Krashen famously called the "Affective Filter Hypothesis" (AFH). Today, Artificial Intelligence (AI) tools like chatbots and adaptive apps are common in language education, raising a critical question: can these technologies help lower that filter? This review tackles this question by first establishing an analytical framework grounded in Krashen's original theory and enriched by a modern psychological perspective on motivation, Self-Determination Theory (SDT). It then examines the existing research on how AI performs in practice, looking at its role in creating "low-stakes" practice environments and boosting motivation through features like personalization and gamification. The findings are not always consistent, which pushes us to look deeper into the nuances of AI design. The review then explores the frontier of "affective computing" and confronts the unavoidable ethical challenges of AI, including data privacy and algorithmic bias. It ultimately argues that the best use of AI is not to replace teachers, but to serve as a powerful, emotion-aware tool that supports them.
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