CTIS–IACLLMs’25 Emotional Intelligence in AI: Leveraging Large Language Models for Advancements in Affective Computing

CTIS’25 Workshop: Emotional Intelligence in AI: Leveraging Large Language Models for Advancements in Affective Computing is conducted under the 3rd International Conference on Computer Technology and Information Science which will be held during May 27-29, 2025 in Ordos, China.
The motivation for this conference proposal arises from several pressing factors:
The primary motivation behind this proposal springs from the rapidly expanding role of AI in everyday life and the clear need to integrate emotional intelligence into these systems. While Large Language Models (LLMs) have revolutionized natural language processing and generation, current AI tools often undervalue the nuanced emotional contexts in human interactions, leaving significant gaps in user trust and engagement.
By combining LLMs with affective computing techniques—such as facial expression recognition, speech analysis, and context-aware dialogue—the proposed conference aims to fill this gap.
Organizations increasingly see emotional intelligence in AI as a way to build empathic systems for mental health support, educational platforms, customer service, and other domains where emotionally responsive communication is critical. This resonates with society’s move toward digital and remote interactions, which heightens the importance of detecting and responding to emotional cues accurately and ethically.
Furthermore, there is a strong ethical and interdisciplinary drive within this proposal. Collecting and processing emotional data require oversight, transparent governance, and cultural sensitivity. Integrating contributions from psychology, neuroscience, linguistics, and policy ensures that emotionally intelligent AI evolves responsibly, addressing both societal and commercial demands for systems that genuinely understand and support human emotional well-being.

This proposal seeks to advance the emerging intersection of emotional intelligence and artificial intelligence, with a focus on how large language models can drive progress in affective computing. By uniting researchers, developers, and ethicists, the conference explores new techniques for detecting, interpreting, and generating emotional expressions across facial cues, speech, text, gestures, and beyond. The session will focus on the convergence of Affective Computing and LLMs in the following key areas:
1. Large Language Models in Affective Computing
- Adapting LLMs for emotion detection and interpretation in text
- Employing continual learning to enhance emotional context accuracy without extensive retraining
- Techniques for generating empathetic or emotionally responsive text
2. Multi-Modal Emotion Representation
- Integrating LLMs with facial emotion recognition
- Analyzing speech emotions using advanced audio embeddings and contextual language cues
- Interpreting gestures and bodily expressions alongside language-based reasoning
3. Generating Emotionally Expressive Outputs
- Strategies for creating facial expressions and voice inflections informed by LLM insights
- Producing empathic or tone-aware responses in conversational agents and social robots
- Synthesizing realistic multimodal emotional content using generative models
4. Interpretable and Responsible AI
- Addressing ethical considerations and bias in sentiment and emotion recognition
- Ensuring transparency in decision-making and emotion classification of large models
- Building trust in AI-driven empathetic systems through explainable predictions
5. Applied Use Cases
- Healthcare: Emotionally supportive chatbots for mental health and patient interaction
- Education: Intelligent tutoring systems that adapt to students' emotional states
- Customer Service: Contextually aware, empathetic virtual agents to enhance customer satisfaction
- Entertainment and Gaming: Responsive, emotionally dynamic digital characters

CTIS–IACLLMs’25 Session Chair /Committee Member

Assoc. Prof. Jianfeng Zhao
Ningbo University /College of Science and Technology (CST), China

Brief introduction:
Teaching Assistant (2001-2006), Lecturer (2006-2011), Associate Professor (2011-2019), School of Information Engineering, Inner Mongolia University of Science and Technology
PhD(2013-2018), School of Electronics and Information Engineering, Beihang University
Visiting Scholar (2014-2015)，Advanced Analytics Institute, University of Technology Sydney
Senior Associate Researcher (2020-2022), Hangzhou Innovation Institute, Beihang University
Associate Professor (20222-Present), College of Science and Technology (CST), Ningbo University

Areas of Expertise: Signal and Information Processing, Artificial Intelligence, Deep Networks

Prof. Jianjun Li
Inner Mongolia University of Science and Technology, China

Brief introduction:
Jianjun Li, Ph.D., professor, doctoral supervisor, Young Science and Technology Talents of Higher Education Institutions in Inner Mongolia Autonomous Region, and "Three Districts Science and Technology Talents" in Fengzhen City. Currently, his main research directions are: pattern recognition, signal detection, and fault diagnosis. He has presided over and participated in 3 projects of the National Natural Science Foundation, 5 projects of Inner Mongolia Natural Science Foundation, 3 project of Inner Mongolia Autonomous Region's key research and development and scientific and technological achievement transformation, and many horizontal scientific research projects. He has published more than 50 academic papers in this field, including 5 SCI papers and 13 EI papers, applied for 8 patents, granted 4 patents, published 1 monograph, and 8 software copyrights. CCF member, National Natural Science Foundation project reviewer, Ministry of Education Degree Center reviewer, IEEE ACESS, Multimedia Systems and other SCI journal reviewer.

Speakers
Keynote speakers, invited speakers and oral presenters are most welcomed for this special session, and welcome your nominations or self-nominations!