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

TL;DR

This paper examines how emotion AI and intercultural pragmatics influence language learning, highlighting opportunities and challenges of AI chatbots in supporting emotional and pragmatic development in learners.

Contribution

It explores the intersection of emotion AI, intercultural pragmatics, and language learning, emphasizing the implications for personalized education and intercultural competence development.

Findings

Emotion AI can personalize language learning experiences.

AI chatbots offer non-judgmental practice but may lack cultural sensitivity.

Concerns about emotional manipulation and user profiling exist.

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