Advances in AI have enabled ESL learners to practice speaking through conversational systems. However, most tools rely on explicit correction, which can interrupt the conversation and undermine confidence. Grounded in second language acquisition and motivational psychology, we present AI Twin, a system that rephrases learner utterances into more fluent English and delivers them in the learner's voice. Embodying a more confident and proficient version of the learner, AI Twin reinforces motivation through alignment with their aspirational Ideal L2 Self. Also, its use of implicit feedback through rephrasing preserves conversational flow and fosters an emotionally supportive environment. In a within-subject study with 20 adult ESL learners, we compared AI Twin with explicit correction and a non-personalized rephrasing agent. Results show that AI Twin elicited higher emotional engagement, with participants describing the experience as more motivating. These findings highlight the potential of self-representative AI for personalized, psychologically grounded support in ESL learning.
As AI writing tools evolve from fixing surface errors to creating language with writers, new capabilities raise concerns about negative impacts on student writers, such as replacing their voices and undermining critical thinking skills. To address these challenges, we look at a parallel transition in university writing centers from focusing on fixing errors to preserving student voices. We develop design guidelines informed by writing center literature and interviews with 10 writing tutors. We illustrate these guidelines in a prototype AI tool, Writor. Writor helps writers revise text by setting goals, providing balanced feedback, and engaging in conversations without generating text verbatim. We conducted an expert review with 30 writing instructors, tutors, and AI researchers on Writor to assess the pedagogical soundness, alignment with writing center pedagogy, and integration contexts. We distill our findings into design implications for future AI writing feedback systems, including designing for trust among AI-skeptical educators.
AI-driven educational technologies are expanding rapidly, yet their design rarely reflects the linguistic and infrastructural realities of Deaf learners in low-resource contexts. This qualitative study investigates how teachers, parents, and Deaf students in Bangladesh navigate fragile visual access, inconsistent Bangla Sign Language, and unreliable technology in everyday learning. Through interviews and focus groups with 13 teachers, eight parents, and five Deaf students, we show how small disruptions in sightlines, pacing, or sign clarity can quickly collapse comprehension, making accessibility a condition that must be constantly protected. We identify opportunities for AI to act as access support by stabilizing visual information, ensuring teacher-validated Bangla Sign Language, enabling offline use, and protecting emotional safety when seeking help. We contribute a model of learning continuity that explains how visual, linguistic, and affective stability interact in Deaf education and offer concrete design directions for AI-driven learning tools in the Global South.
American Sign Language (ASL) is the primary language of many Deaf and Hard of Hearing (DHH) individuals. However, existing learning resources often lack timely, individualized feedback, leaving learners uncertain about signing accuracy. We introduce a novel egocentric ASL learning system that integrates stereo vision, error detection across four manual ASL parameters (handshape, orientation, location, movement), and large language model (LLM)–driven natural language feedback. To our knowledge, this is the first system to deliver error-aware, pedagogically grounded feedback for ASL learners. A formative study with 15 ASL teachers and 30 learners (both Deaf and hearing backgrounds) supports the motivation and design goals, while a system evaluation with 13 Deaf ASL participants (novice to advanced) practicing 230 signs provides initial evidence of system feasibility and short-term, pedagogically promising behavior within the primary user community. Across two complementary studies, we identify key design principles: prioritizing reliability over sensitivity, stratifying feedback by error severity, and leveraging egocentric alignment for natural practice. Collectively, these contributions establish a foundation for scalable ASL education and provide generalizable insights for designing AI-mediated feedback in Human-Computer Interaction (HCI).
Self-directed language learners increasingly turn to large language models (LLMs) for assistance, but face the challenge of deciding what learning tasks to delegate to LLMs and how. While prior research has examined the effectiveness of LLM in improving language proficiency, less is known about how learners negotiate agency and what values guide delegation strategies. To address this gap, we conducted a two-part study: an analysis of discussions in the r/languagelearning subreddit to map learners' LLM usage patterns and factors driving delegation, followed by a technology probe study where learners designed learning activities and experimented with LLM support. Our findings reveal three key considerations influencing delegation: accuracy, independence, and authenticity. We analyze these considerations through two types of obstacles: selection challenges in choosing appropriate strategies and execution challenges in following through on intentions. These insights inform the design of AI-assisted learning systems that preserve learner agency while supporting diverse learning goals.
Non-native English speakers performing English-related tasks at work struggle to sustain EFL learning, despite their motivation. Often, study materials are disconnected from their work context. Our formative study revealed that reviewing work-related English becomes burdensome with current systems, especially after work. Although workers rely on LLM-based assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages these on-the-fly queries using AI to generate personalized quizzes that workers can review and practice on their smartphones. We conducted a three-week deployment study with 28 EFL workers to evaluate LingoQ. Participants valued the quality-assured, work-situated quizzes and constantly engaging with the app during the study. This active engagement improved self-efficacy and led to learning gains for beginners and, potentially, for intermediate learners. Drawing on these results, we discuss design implications for leveraging workers' growing reliance on LLMs to foster proficiency and engagement while respecting work boundaries and ethics.
Reflection is essential for fostering metacognitive development. However, many learners struggle to engage in meaningful, structured reflection without further support. To support learners in reflective practices, we developed MindBuddy, a learner-centered tutor that guides students individually through reflective writing tasks and provides adaptive feedback. After an iterative user-centered development process (two pilot studies, n=81), we conducted a longitudinal field-experimental classroom study with n=34 undergraduates over a six-week period to compare two different tutoring modalities in MindBuddy (1) interactive conversational tutoring (TG1) with (2) constructive feedback-only (TG2). No significant differences in perceived skills were found, suggesting that the conversational interactions may enhance students’ confidence in their reflective abilities, similarly to adaptive feedback interaction. While differences were found for formal reflective structure, our findings suggest that conversational tutoring has the potential to increase learners’ engagement with reflective writing. Future research on whether such engagement translates into measurable performance gains is necessary.