Research Highlights

Generative AI Driven Text-based Chatbot for Mental Health Screening

Update as of 3 August 2026


Conventional mental health screening tools often rely on fixed questionnaires that may lack personalization and fail to capture the complexity of individual experiences. This research introduces the Empathic Structured Exploration Model (ESEM), a conversational framework designed to enhance chatbot-based screening through adaptive and empathetic interactions. By responding to users’ emotional and contextual cues, ESEM aims to create a more natural, supportive, and engaging screening experience.

Implemented within a PHQ-9 chatbot for college students, this approach is expected to improve user engagement, encourage more open self-expression, and achieve closer alignment with established screening measures compared with conventional methods. By integrating conversational intelligence with human-centered design, ESEM has the potential to advance digital mental health screening, making it more accessible, responsive, and effective for diverse populations.

 


Team Members:

  1. PI: Prof. CHIU Dah Ming, Felizberta Lo Padilla Tong School of Social Sciences, Saint Francis University
  2. Prof. CHOW Chum Ming Meyrick, S.K. Yee School of Health Sciences, Saint Francis University
  3. Dr. SZETO So-suet Stephanie, Felizberta Lo Padilla Tong School of Social Sciences, Saint Francis University
  4. Dr. CHAN Cheong Yu, School of Arts and Humanities, Tung Wah College


Reference no.: UGC/FDS11/E05/24