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- PaperLanguage Learning & Technology1 Jan 2026
AI as a debate coach: A mixed-methods analysis of student self-efficacy and perceptions in an AI-assisted debate
Joan Wan-Ting Huang
A mixed-methods study examined the impact of an AI-assisted debate intervention on 48 EFL learners' debating self-efficacy. Participants engaged in traditional preparation followed by AI chatbot support for argument refinement and delivery practice. Results showed significant stepwise increases in self-efficacy, with qualitative data revealing nuanced benefits and limitations of AI in debate preparation.
Original abstract
Debate is an effective pedagogical approach, yet it presents significant challenges for EFL learners. While Artificial Intelligence (AI) chatbots are increasingly integrated into education, their application in multi-skilled tasks like debate preparation remains underexplored. This study investigated the impact of a two-phase AI-assisted intervention on 48 EFL learners' debating self-efficacy and perceptions. Phase 1 involved traditional debate preparation without AI assistance, focusing on foundational skill development through instructor-led instruction. Phase 2 introduced AI chatbots for refinement of arguments, rebuttals, and delivery practice, allowing students to enhance their debates through AI-powered scaffolding. Data were collected via self-efficacy questionnaires at three time points (pre-intervention, post-Phase 1, and post-Phase 2), a post-intervention perceptions questionnaire, written reflections, and focus group interviews. Repeated-measures ANOVAs revealed significant stepwise increases in students’ debating self-efficacy across the three time points, with the most substantial gains observed in debate skills and language use. The perceptions questionnaire corroborated these findings, demonstrating that students rated AI as most effective for refining speeches, locating evidence, and developing arguments, while perceiving it as least helpful for oral delivery practice. Furthermore, qualitative analysis yielded nuanced and contextualized insights regarding both the benefits and limitations of AI-assisted debate preparation.
- PaperLanguage Learning & Technology1 Jan 2026
Profiling learners’ affective engagement: Emotion AI, intercultural pragmatics, and language learning
Robert Godwin-Jones
This column examines the role of emotion in language learning, particularly through AI chatbots and emotion recognition technology. It discusses how Emotion AI can personalize learning by adapting to learners' affective states, but also warns of risks like emotional manipulation and cultural bias. The article highlights the importance of pragmatic and interactional competence in second language acquisition.
Original 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.