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Juhwan Ma

dblp:427/0610 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0000-4687-1491ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 50% Human-AI interaction · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › AI agent
AI self-clones
1.012026
AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me · CHI 2026
Learning and educational technologies
language learning
1.012026
AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me · CHI 2026
Human-AI interaction
conversational agents
0.312026
AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me · CHI 2026
Learning and educational technologies › personalized learning
personalized learning support
0.312026
AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me · CHI 2026

Methods — techniques the papers use, named apart from their topics

within-subject study · 1.0second language acquisition theory · 1.0
YearPublicationVenuePosition
2026 AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me
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.
Minju Park, Juhwan Ma, Dongwook Yoon
CHI3