AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me

2026-01-16

AI Summary

This study addresses the limitations of traditional ESL speaking practice tools, which rely on explicit error correction that often disrupts conversational flow and undermines learner confidence. Drawing on second language acquisition theory and motivational psychology, the authors propose an “AI Twin” system that leverages voice cloning and natural language paraphrasing to implicitly rephrase learners’ utterances into more fluent English using their own synthesized voice. This approach maintains dialogue continuity while reinforcing learners’ identification with their “ideal L2 self.” The system uniquely integrates personalized voice avatars, affective support, and implicit corrective feedback, thereby enhancing motivation. In a controlled experiment with 20 adult ESL learners, the AI Twin significantly increased emotional engagement and was perceived as a more motivating speaking practice experience compared to conventional methods.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Multimodal Learning
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• ESL speaking practice
• explicit correction
• motivational psychology
• conversational AI
• Ideal L2 Self

Innovation

• AI Twin
• implicit feedback
• Ideal L2 Self
• personalized AI
• ESL speaking practice