Important Dates
Manuscript Submission Deadline October 30, 2025
First Review Notification December 1, 2026
Revised Manuscript Due February 1, 2026
Final Decision Notification March 1, 2026
Aim and Scope
Mental health has become a critical global concern, with far-reaching implications for individual well-being, societal cohesion, and public healthcare systems. Traditional mental health services face challenges in scalability, personalization, and accessibility. In response, artificial intelligence—particularly systems capable of emotional awareness—offers promising avenues to support mental health interventions at scale.
The rapid advancement of Large Language Models (LLMs) has transformed the landscape of natural language processing, enabling more sophisticated and human-like interactions. However, these models often lack the affective depth and the genuine empathy that are crucial in emotionally sensitive contexts like mental health. Integrating affective computing principles into LLMs represents a promising direction to address this gap, enabling emotionally intelligent systems that are context-aware, ethically aligned, and capable of building trust in high-stakes interactions.
This special issue aims to explore interdisciplinary approaches that integrate affective computing, psycho-cognitive disciplines, and LLMs to advance empathetic AI in mental health applications. We welcome contributions that investigate theoretical foundations, novel algorithms, and real-world systems that enhance the emotional awareness and relational intelligence of LLMs. In particular, the issue will emphasize clinical application and deployment strategies, as well as domain-specific customizations that cater to unique healthcare scenarios. Our goal is to foster research that paves the way for emotionally responsive and trustworthy AI agents capable of making meaningful contributions to mental health care.
Topics of Interest
We invite original research articles, theoretical contributions, and system-level studies on topics including (but not limited to):
- Emotion detection and affect modeling in large-scale language models.
- Affective and empathetic dialogue generation.
- Personalization and contextual adaptation for emotional support systems.
- Multimodal affective computing in LLM-integrated systems (text, audio, visual).
- LLM-based mental health screening and early detection tools.
- Human-in-the-loop training for empathetic AI.
- Computational models of empathy, compassion, and rapport in language generation.
- Cross-linguistic and cultural generalization of affective LLMs.
- Evaluation frameworks for empathy, engagement, and emotional appropriateness.
- Ethical, privacy, and fairness considerations in emotionally-sensitive applications.
- Deployment studies of AI mental health assistants or therapeutic agents.
- Clinical applications and deployment: Real-world case studies and integration strategies in healthcare settings.
- Domain-specific customization: Tailored affective LLM solutions for specific patient groups (e.g., elderly, adolescents, chronic disease management).
Submission Guidelines
For author information and guidelines on submission criteria, visit the TAC Author Information page. When submitting your paper, please be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the IEEE Author Portal submission system and must select the article type: “Can AI Care? Affective LLMs for the Future of Mental Health.”
In addition to submitting your paper to TAC, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE’s data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
All submissions will undergo rigorous peer review in accordance with IEEE TAC standards.
Guest Editors
Kai He, National University of Singapore, Singapore (Lead Guest Editor)
Jialun Wu, Northwestern Polytechnical University, China
Zeyu Gao, University of Cambridge, UK
Yefeng Zhen, Westlake University, China
Erik Cambria, Nanyang Technological University, Singapore
