Art therapy is an excellent treatment for managing mental health, especially focusing on self-reflection, self-efficacy, and self-esteem. However, professional psychotherapy remains under-represented, and many individuals are deterred from pursuing art therapy due to a lack of artistic skills. This article introduces MIRA, a generative AI agent designed to extend the Squiggle Game into a self-support tool for everyday use. By combining sketch-based collaborative drawing with reflective feedback, MIRA lowers the barrier to creative engagement while opening up new opportunities for self-support.

We conducted a controlled study with 30 participants, comparing three conditions: an AI agent (MIRA), human-only curved drawing, and independent drawing. Analysis revealed that participants in the AI condition reported significantly higher levels of self-reflection, self-efficacy, and self-esteem compared to those in the control condition. Interviews revealed that users perceived MIRA not only as a skill compensator that alleviated performance anxiety, but also as a co-creator who encouraged more ambitious expression and a reflective partner who induced deeper reflection. Participants also emphasized the novelty and engagement of the step-by-step generative process, describing it as both engaging and healing. At the same time, the findings revealed a mixed attitude among users toward AI interventions: some participants valued the objective insights of AI, while others questioned its depth of empathy. These complex perceptions highlight the importance of carefully positioning AI agents as supportive partners rather than replacements for therapists.

In summary, this study presents the first empirical analysis of a self-support AI agent adapted from Squiggle Games. Our work demonstrates how generative AI can expand the inclusiveness of art therapy by lowering barriers to entry, supporting reflective practice, and fostering creative confidence. Finally, we draw implications for the design of reflective AI systems that st

This research investigates how artificial intelligence interprets and expresses the concept of humanity through visual art, and how people, in turn, perceive the humanity of AI based on these outputs. The project begins with the collection of approximately two thousand survey responses, in both Chinese and English, in which participants shared their personal views on the meaning of humanity. These responses were aggregated and used as input for an AI image generation process, resulting in a series of visual artworks intended to embody collective human perspectives.

To examine audience perceptions of these AI-generated works, semi-structured interviews were conducted with master-level design students who regularly engage with creative technologies. Participants were asked to interpret the artworks without prior knowledge that the images were created by AI, ensuring that responses were guided by the visual content itself rather than by preconceived notions of machine authorship.

The qualitative data were analyzed using Reflexive Thematic Analysis (RTA), through which recurring interpretive patterns were identified and grouped into six themes. To complement this analysis, participants also completed the Godspeed questionnaire, providing quantitative measures of anthropomorphism, animacy, and perceived intelligence. By correlating thematic interpretations with these perception scores, the study highlights how specific visual features—such as symbolic richness, emotional resonance, relational elements, or mechanistic qualities—influence the extent to which people attribute humanity to AI.

Findings suggest that judgments of AI’s humanity are shaped less by technical fidelity than by the capacity of visual features to evoke emotions, cultural references, and symbolic associations. Participants who linked images to personal or collective human experiences tended to ascribe greater humanity to the AI, while those who focused on surface resemblance or structural flaws were more skeptical. This indicates that perceptions of AI’s humanity depend on interpretive depth rather than superficial resemblance.

The project contributes to human–computer interaction research by demonstrating how qualitative interpretations of AI-generated art can reveal underlying frameworks through which people negotiate the boundary between human and machine creativity. It also provides practical insights for the design of AI systems in artistic and communicative domains, suggesting that fostering symbolic and emotional resonance may be more impactful than pursuing purely technical realism. Ultimately, this study positions AI art as a valuable lens for exploring evolving understandings of humanity and for guiding more responsible and human-centered development of creative AI systems