Empathy-Aware Large Language Models for AI-Mediated Gambling Addiction Interventions

Session Title

Poster session

Presentation Type

Poster Presentation

Start Date

27-5-2026 12:00 AM

Abstract

Gambling addiction remains a pressing public health challenge, yet many individuals facing problematic gambling behavior avoid traditional counseling due to stigma, accessibility barriers, or personal reluctance. Increasingly, they turn to conversational artificial intelligence (AI) for immediate and anonymous support. While large language models (LLMs) can deliver information, interventions, and coping strategies, their limited ability to express empathy and provide emotional support represents a critical and underexplored gap in AI-mediated addiction consulting. Motivated by this gap, this study investigates the following research questions: (RQ1) How can empathic capabilities be systematically embedded into LLMs for gambling addiction consulting? (RQ2) To what extent does empathic AI improve the effectiveness of addiction-related consulting outcomes? (RQ3) How does empathy-enhanced AI affect users’ emotional responses, conversational satisfaction, and perceived support compared to intervention-only AI? Drawing on theories of empathy, emotion contagion, and affective activation, we design and train models using curated consulting dialogues that integrate not only evidence-based interventions (e.g., cognitive behavioral strategies) but also empathetic responses tailored to users’ emotional needs. The approach focuses on identifying and addressing users’ empathy needs, providing emotional validation, and fostering conversational satisfaction, alongside practical guidance.

Author Bios

Liqian Bao is an Assistant Professor of Information Science & Systems at the Graves School of Business, Morgan State University. She servers at the Center for Data Analytics and Sports Gaming Research. She received the M.S. degree in Information Technology Management and Ph.D. degree in Management Information Systems from the University of Wisconsin-Milwaukee. Dr. Bao’s work focuses on multimodal data analytics, data mining, machine learning, deep learning, large language modeling, ar

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May 27th, 12:00 AM

Empathy-Aware Large Language Models for AI-Mediated Gambling Addiction Interventions

Gambling addiction remains a pressing public health challenge, yet many individuals facing problematic gambling behavior avoid traditional counseling due to stigma, accessibility barriers, or personal reluctance. Increasingly, they turn to conversational artificial intelligence (AI) for immediate and anonymous support. While large language models (LLMs) can deliver information, interventions, and coping strategies, their limited ability to express empathy and provide emotional support represents a critical and underexplored gap in AI-mediated addiction consulting. Motivated by this gap, this study investigates the following research questions: (RQ1) How can empathic capabilities be systematically embedded into LLMs for gambling addiction consulting? (RQ2) To what extent does empathic AI improve the effectiveness of addiction-related consulting outcomes? (RQ3) How does empathy-enhanced AI affect users’ emotional responses, conversational satisfaction, and perceived support compared to intervention-only AI? Drawing on theories of empathy, emotion contagion, and affective activation, we design and train models using curated consulting dialogues that integrate not only evidence-based interventions (e.g., cognitive behavioral strategies) but also empathetic responses tailored to users’ emotional needs. The approach focuses on identifying and addressing users’ empathy needs, providing emotional validation, and fostering conversational satisfaction, alongside practical guidance.