AI-Enhanced HTP Test Analysis and Emotion Recognition for Personalized Psychotherapy Interventions

Hanyu Huang, Weike Zeng, Tianle Wang, Yujie Liu, Chenyu Cao, Runnan Li, Wenjun Hou

Abstract: Mental health is a critical global issue, with many individuals lacking timely psychological support due to limited resources and high costs. Traditional interpretations of the House-Tree-Person (HTP) test, which rely on subjective judgment, are often time-consuming and lack scalability, necessitating more objective and efficient methods. This study explores the application of artificial intelligence (AI) technology, especially large language models (LLMs), to automate HTP test analysis and provide personalized therapeutic interventions, aiming to enhance the efficiency and accuracy of psychological assessments. We constructed a diverse HTP drawing database and developed an AI-powered system that integrates HTP analysis with music, visual, and aroma therapies. A user experiment with eight participants was conducted to evaluate the system’s performance through a Likert scale questionnaire and semi-structured interviews. Results showed that participants had high trust in the AI-assisted HTP test results, with an average satisfaction score of 3.75 for the overall effectiveness of art therapy. This demonstrates the potential of AI to enhance both assessment accuracy and therapeutic outcomes. Future work will focus on improving model interpretability, exploring ethical implications, and expanding the application scope to other psychological tools to achieve more effective and sustainable treatment outcomes.

External IDs:dblp:conf/hci/HuangZWLCLH25