Introspectus AI: Long-term AI-Driven Dialogue Training To Promote Self-Reflection

Introspectus AI is a generative AI-based system designed to enhance self-reflection and support positive behavior change. By leveraging multimodal information from users' daily life recordings, it provides personalized and detailed feedback, aiming to deepen self-awareness and facilitate positive behavioral adjustments. This study explores the short-term and long-term impacts of interacting with Introspectus AI, focusing on its potential to enhance reflective practices and improve the acceptance of generative AI tools. Following the user experience was defined through an initial round of workshops with four experts. The resulting system was evaluated through a long-term study involving 64 participants. The results demonstrate that AI-supported interventions significantly improved engagement in self-reflection, the need for reflection, and insight, while also increasing user acceptance of generative AI over time. These findings underscore the potential of generative AI as a practical tool for self-improvement, offering insights into its broader applicability in promoting well-being and personal growth.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757616
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
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article

Introspectus AI: Long-term AI-Driven Dialogue Training To Promote Self-Reflection

Kouta Minamizawa, Shengyin Li, Yun Suen Pai, Ximing Shen et al.
Proceedings of the ACM on Human-Computer Interaction
Intelligent Tutoring Systems and Adaptive Learning
article

Introspectus AI: Long-term AI-Driven Dialogue Training To Promote Self-Reflection

Kouta Minamizawa, Shengyin Li, Yun Suen Pai, Ximing Shen, Giulia Barbareschi, Danyang Peng, Chenyu Tu, Xiaru Meng, Guangyao Zhu
article en

Abstract

Introspectus AI is a generative AI-based system designed to enhance self-reflection and support positive behavior change. By leveraging multimodal information from users' daily life recordings, it provides personalized and detailed feedback, aiming to deepen self-awareness and facilitate positive behavioral adjustments. This study explores the short-term and long-term impacts of interacting with Introspectus AI, focusing on its potential to enhance reflective practices and improve the acceptance of generative AI tools. Following the user experience was defined through an initial round of workshops with four experts. The resulting system was evaluated through a long-term study involving 64 participants. The results demonstrate that AI-supported interventions significantly improved engagement in self-reflection, the need for reflection, and insight, while also increasing user acceptance of generative AI over time. These findings underscore the potential of generative AI as a practical tool for self-improvement, offering insights into its broader applicability in promoting well-being and personal growth.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
Waseda University (JP), University of Auckland (NZ), Keio University (JP), Auckland University of Technology (NZ), University of Duisburg-Essen (DE)
Openalex Percentile: Top 85%
Intelligent Tutoring Systems and Adaptive Learning
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