We developed an interface to explain news articles through dialogue by considering the user’s comprehension level. The interface generates several pertinent questions based on the ongoing dialogue and news article, and users advance the conversation by selecting a question. Based on the user’s selected questions, the interface estimates their comprehension level of the news article and adjusts the difficulty of the generated questions accordingly. This enables a personalized dialogue tailored to each user’s comprehension needs. The results of the baseline comparison experiments confirmed the usefulness of the interface.

My research interests lie on the development of advanced user support systems, emphasizing the enhancement of user engagement and system effectiveness. The field of user support systems aims to help users accomplish complex tasks efficiently while ensuring a pleasant and intuitive interaction experience. I explore how to incorporate engaging and context-appropriate assistance into these systems to make the task completion process more effective and enjoyable for users.

The Werewolf Game is a communication game where players’ reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent’s utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent’s utterances are contextually consistent and that the character, including tone, is maintained throughout the game.