Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning

• URL: http://arxiv.org/abs/2603.20479v1
• Date: Fri, 20 Mar 2026 20:22:54 GMT
• Title: Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning
• Abstract summary: Learning another language can be a highly emotional process.<n>This column explores aspects of emotion as they arise in technology use.<n> Emotion AI, the algorithmically driven interpretation of users' affective signals, has been seen as enabling greater personalized learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Learning another language can be a highly emotional process, typically characterized by numerous frustrations and triumphs, big and small. For most learners, language learning does not follow a linear, predictable path, its zigzag course shaped by motivational (or demotivating) variables such as personal characteristics, teacher/peer relationships, learning materials, and dreams of a future L2 (second language) self. While some aspects of language learning (reading, grammar) are relatively mechanical, others can be stressful and unpredictable, especially conversing in the target language. That experience necessitates not only knowledge of structure and lexis, but also the ability to use the language in ways that are appropriate to the social and cultural context. A new opportunity to practice conversational abilities has arrived through the availability of AI chatbots, with both advantages (responsive, non-judgmental) and drawbacks (emotionally void, culturally biased). This column explores aspects of emotion as they arise in technology use and in particular how automatic emotion recognition and simulated human responsiveness in AI systems interface with language learning and the development of pragmatic and interactional competence. Emotion AI, the algorithmically driven interpretation of users' affective signals, has been seen as enabling greater personalized learning, adapting to perceived learner cognitive and emotional states. Others warn of emotional manipulation and inappropriate and ineffective user profiling

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