Work

Automated Working Alliance Assessment in Psychological Counseling Using Gemini and XGBoost

Year: 2026

Type: article

Abstract: Session dialogue assessment based on machine learning is gradually becoming an effective solution for therapeutic alliance measurement which is an important factor for successful psychotherapy. However, most existing models assume clean and pre-structured dialogue transcripts, whereas real-world counseling documentation often contains heterogeneous case reports. This gap limits the applicability of current automated assessment models in realistic documentation scenarios. In this work, we propose a framework for automated working alliance assessment from complex, multilingual reports. First, language-specific BERT models are fine-tuned to process case reports across different languages, enabling accurate speaker role delineation and dialogue structuring. Second, Gemini-2.5-Flash is leveraged to annotate the dialogues with working alliance ratings. Third, a hybrid feature representation strategy is then developed to jointly capture linguistic style and semantic content from the counseling dialogues. Furthermore, an entropy-based mutual information analysis is conducted to identify the most informative linguistic features. Finally, the extracted hybrid features serve as inputs to XGBoost for alliance assessment. In experiments, the proposed framework shows better performance in the comparison with SOTA methods and generalization ability.

Source: Entropy

DOI: 10.3390/e28060699

Authors: Yuexi Li, Ningtao Sun, Zhuoxi Mai, Dalin Li, Guifang Fu, Xueling Yang

Institutions: Jilin University, Zhuhai College of Science and Technology, Guangdong University of Foreign Studies, Southern Medical University

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China