# Animating Language Practice: Engagement with Stylized Conversational Agents in Japanese Learning

arXiv: https://arxiv.org/abs/2507.06483 · HTML: https://mlsanity.com/paper/2507.06483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 7 of 7)

- [Unveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks](https://mlsanity.com/paper/2406.06302/) (2024)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines](https://mlsanity.com/paper/2504.07840/) (2025)
- [Using Adaptive Empathetic Responses for Teaching English](https://mlsanity.com/paper/2404.13764/) (2024)
- [EDEN: Empathetic Dialogues for English learning](https://mlsanity.com/paper/2406.17982/) (2024)
- [To what extent is ChatGPT useful for language teacher lesson plan creation?](https://mlsanity.com/paper/2407.09974/) (2024)
- [An algebraic approach to translating Japanese](https://mlsanity.com/paper/2303.05834/) (2023)

## Built on by (most cited first, 1 of 1)

- [Re:Member: Emotional Question Generation from Personal Memories](https://mlsanity.com/paper/2510.19030/) (2025)

## Abstract (arXiv)

We explore Jouzu, a Japanese language learning application that integrates large language models with anime-inspired conversational agents. Designed to address challenges learners face in practicing natural and expressive dialogue, Jouzu combines stylized character personas with expressive text-to-speech to create engaging conversational scenarios. We conducted a two-week in-the-wild deployment with 52 Japanese learners to examine how such stylized agents influence engagement and learner experience. Our findings show that participants interacted frequently and creatively, with advanced learners demonstrating greater use of expressive forms. Participants reported that the anime-inspired style made practice more enjoyable and encouraged experimenting with different registers. We discuss how stylization shapes willingness to engage, the role of affect in sustaining practice, and design opportunities for culturally grounded conversational AI in computer-assisted language learning (CALL). By framing our findings as an exploration of design and engagement, we highlight opportunities for generalization beyond Japanese contexts and contribute to international HCI scholarship.