• Journal of Music and Human Behavior
• /
• v.21 no.3
• /
• pp.1-18
• /
• 2024

The purpose of this study is to explore the ethical possibilities of praxial music education from the perspective of embodied cognition. Praxial music educators rely on embodied cognition, which views cognition as meaning generated through the dynamic interplay between a living system and its environment, to present empathy and care as ethical values in music education. However, they do not specifically discuss how embodied cognition can be applied at the intersection where music transitions into ethics. Therefore, this study explores the ethical possibilities of praxial music education by discussing the conditions under which music can advance toward empathy and care for others. Since embodied cognition views cognition as participatory and relational, if musical imagination can foster empathy by understanding others' emotions through participatory music-making and listening, and if this empathy can develop into dialectical empathy generated from intersubjectivity, then praxial music education can move towards ethics. Consequently, praxial music education emphasizes the importance of participatory music-making and listening grounded in concern and care, which presents the following insights: First, participatory music-making should begin with attentive listening. Second, the emphasis should fall on the process of teaching and learning rather than outcomes. Third, the focus should be on musical knowledge that enriches life rather than solely acquiring knowledge for the sake of music. Fourth, teachers must possess both ethical attitudes and professional expertise.

• Korean Journal of Cognitive Science
• /
• v.35 no.1
• /
• pp.23-48
• /
• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.