Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition

2023-08-01

International Joint Conference on Artificial Intelligence

AI Summary

This study investigates the adoption mechanisms of the lightweight AI music co-creation tool MMM-C among amateur and professional composers, focusing on usability, user experience, and technology acceptance. Methodologically, it introduces a novel “single-parameter” minimalist AI co-creative interface and conducts the first dual-layer mixed-method empirical evaluation within a real-world DAW environment (Cubase), integrating standardized instruments—System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)—alongside surveys, semi-structured interviews, and behavioral observation. Results indicate high system usability (SUS >75), strong acceptance (UTAUT2 mean >4.2/5), and broad user endorsement of novelty and ease of use; however, controllability and predictability require further refinement. Crucially, no significant performance differences were observed between amateur and professional users, empirically validating the cross-experience applicability of this lightweight co-creation paradigm.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.

Problem

• Evaluating usability and acceptance of MMM-C AI music tool
• Assessing user experience in human-AI music co-creation
• Comparing hobbyist vs professional composer interactions with MMM-C

Innovation

• AI plugin for music co-creation in DAWs
• Mixed-method study on usability and acceptance
• Novelty and ease of use reported by users