Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Understanding Human-AI Collaboration in Music Therapy Through Co-Design with Therapists

Jingjing Sun, Jingyi Yang, Guyue Zhou, Yucheng Jin, Jiangtao Gong

TL;DR

This study investigates how musical AIs can support music therapy by engaging practicing therapists through semi-structured interviews and participatory co-design. Grounded in emotion-focused therapy, the researchers map a three-phase therapy workflow and identify AI techniques (Ge-Gen, Emo-Gen, Melo-Har, Genre-Tran, Tone-Tran) that accompany each stage, highlighting benefits such as increased efficiency, richer and personalized content, and higher client engagement, as well as challenges including therapy complexity, AI limitations, and role ambiguity. The key contributions are an empirical understanding of therapist practices, design implications for integrating musical AIs into therapy, and a framework linking AI capabilities to therapeutic processes. Overall, the work informs the development of human-AI collaborative music therapy systems, emphasizing therapist control, domain knowledge integration, and multi-stakeholder interfaces to ensure safe and effective clinical use.

Abstract

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements.

Understanding Human-AI Collaboration in Music Therapy Through Co-Design with Therapists

TL;DR

This study investigates how musical AIs can support music therapy by engaging practicing therapists through semi-structured interviews and participatory co-design. Grounded in emotion-focused therapy, the researchers map a three-phase therapy workflow and identify AI techniques (Ge-Gen, Emo-Gen, Melo-Har, Genre-Tran, Tone-Tran) that accompany each stage, highlighting benefits such as increased efficiency, richer and personalized content, and higher client engagement, as well as challenges including therapy complexity, AI limitations, and role ambiguity. The key contributions are an empirical understanding of therapist practices, design implications for integrating musical AIs into therapy, and a framework linking AI capabilities to therapeutic processes. Overall, the work informs the development of human-AI collaborative music therapy systems, emphasizing therapist control, domain knowledge integration, and multi-stakeholder interfaces to ensure safe and effective clinical use.

Abstract

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements.
Paper Structure (61 sections, 7 figures, 2 tables)

This paper contains 61 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. HCI Research for Music Therapy
  4. Recent Advances in Musical AI Technologies
  5. Designing Human-AI Collaboration for Therapy
  6. Methodology
  7. Overall Research Protocol
  8. Procedures
  9. Interview Procedure
  10. Co-design Workshop Procedure
  11. Ethical Considerations and Others
  12. Materials
  13. AI-music learning materials.
  14. Pre-designed cases.
  15. Participants
  16. ...and 46 more sections

Figures (7)

  • Figure 1: Typical Workflow of Music Therapy
  • Figure 2: Different stages of emotional problem-solving in music therapy in two cases: matching of music therapy methods and AI algorithms
  • Figure 3: Potential benefits of musical AIs on music therapy
  • Figure 4: Concerns about musical AIs in music therapy
  • Figure 5: Persona Design for Case 1 and Case 2
  • ...and 2 more figures