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. 2025 Jul 23:16:1628471.
doi: 10.3389/fpsyg.2025.1628471. eCollection 2025.

Fostering adolescent engagement in generative AI art therapy: a dual SEM-ANN analysis of emotional

Xiaojie Peng #  1 , Yanran Qian #  1 , Qian Bao  1
Affiliations

Fostering adolescent engagement in generative AI art therapy: a dual SEM-ANN analysis of emotional

Xiaojie Peng et al. Front Psychol. .

Abstract

Introduction: This study explores the application of generative artificial intelligence (AI) art in digital art therapy, focusing on how it influences adolescents' interest-driven participation. With mental health concerns rising among youth, understanding motivational mechanisms in AI-assisted therapeutic tools is both timely and essential.

Methods: A cross-sectional survey was conducted with 444 junior and senior high school students in Hubei Province, China. The study integrated Emotional Design Theory and the Technology Acceptance Model (TAM) to construct a predictive model. Structural equation modeling (SEM) and artificial neural network (ANN) analyses were employed to validate the model and identify key predictors of engagement.

Results: SEM results indicated that perceived usefulness (PU), perceived ease of use (PEOU), perceived fun (PF), and perceived trust (PT) significantly influenced users' attitudes toward use (ATT) (p < 0.001). ATT, PF, and PT were strong predictors of interest-driven participation, while the behavioral level had no direct effect on perceived enjoyment (PE). ANN analysis further highlighted ATT as the most influential predictor (100% normalized importance), notably exceeding PE (19.3%).

Discussion: These findings emphasize the importance of intuitive design, seamless interaction, and trust-building in sustaining adolescents' engagement with AI-based art therapy. The study provides a theoretical foundation for understanding interest formation in youth and offers practical implications for improving emotional design, digital therapeutic tools, and mental health interventions.

Keywords: TAM model; artificial neural network (ANN); digital art therapy; emotional design theory; generative AI painting; interest-driven.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Hypothesized path model.
Figure 2
Figure 2
Experimental process and representative artworks.
Figure 3
Figure 3
Model path analysis results.
See this image and copyright information in PMC

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