SERA 2025 Special Sessions
Special Session 1: Artificial Intelligence for Health Literacy Transformation (AIHLT 2025)
Topic Description:
In recent years, the intersection of health literacy and artificial intelligence (AI) has emerged as a transformative area of research, aiming to bridge gaps in healthcare communication, accessibility, and informed decision-making. Health literacy, the ability to obtain, process, and understand basic health information, remains a critical factor in achieving equitable healthcare outcomes. The integration of AI in this domain offers unprecedented opportunities to enhance individual and community-level understanding through tailored interventions, predictive analytics, and adaptive communication strategies. The Artificial Intelligence for Health Literacy Transformation Special Session at the The 23rd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2025) seeks to bring together interdisciplinary researchers and practitioners to address challenges and showcase innovations at the nexus of health literacy and AI. This session aims to explore how AI-driven methods and tools can improve the design, delivery, and assessment of health communication, while also addressing disparities in health literacy. The session will highlight advancements in natural language processing (NLP), machine learning, and usercentered design to foster equitable healthcare outcomes.
The scope of AIHLT 2025 includes, but is not limited to, the following topics: • AI-enhanced tools for health literacy assessment and improvement • NLP models for simplifying medical jargon and enhancing comprehension • Personalized health education using AI-based recommendation systems • Machine learning approaches for predicting and mitigating health literacy disparities • Interactive AI-driven systems for patient education and engagement • Real-time monitoring and analysis of health literacy trends using AI • AI for multilingual and multicultural health communication • Explainable AI in health communication to build trust and transparency • Applications of conversational agents and chatbots for health literacy • Adaptive learning systems for improving individual and community health literacy • Data-driven approaches to addressing misinformation and promoting health awareness • The role of AI in enhancing digital health tools for diverse populations • Ethical considerations in deploying AI for health literacy improvements • Statistical analysis and evaluation of AI systems in health communication • Applications in public health campaigns, mental health literacy, and chronic disease management.
Special Session Organizers:
Dr Amel Fraisse, University Of Lille – France, Email: amel.fraisse@univ-lille.fr
Dr Jinie Pak, University Of Towson – USA, Email: jpak@towson.edu
Dr Yeong-Tae Song, University Of Towson – USA, Email: ysong@towson.edu
Dr Mouheb Mehdoui, University Of Lille – France, Email: mouheb.mehdoui.etu@univ-lille.fr
Dr Mounir Zrigui, University Of Monastir – Tunisie, Email: mounir.zrigui@fsm.rnu.tn
Dr Widad Mustafa El Hadi, University Of Lille – France, Email: widad.mustafa@univ-lille.fr
Special Session II: Artificial Intelligence for Health Literacy Transformation (AIHLT 2025)
Topic Description:
In recent years, the intersection of health literacy and artificial intelligence (AI) has emerged as a transformative area of research, aiming to bridge gaps in healthcare information exchange, accessibility, and informed decision-making. Health literacy, the ability to obtain, process, and understand basic health information, remains a critical factor in achieving equitable healthcare outcomes. The integration of AI in this domain offers unprecedented opportunities to enhance individual and community-level understanding. By improving the accessibility and clarity of health information, AI supports informed decision-making, empowers individuals to better navigate healthcare systems, and addresses varied informational needs.
The Artificial Intelligence for Health Literacy Transformation Special Session at The 23rd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2025) seeks to bring together interdisciplinary researchers and practitioners to address challenges and showcase innovations at the nexus of health literacy and AI. This session aims to explore how AI-driven methods and tools can improve the design, delivery, and assessment of health information, while also addressing disparities in health literacy. The session will highlight advancements in Information Science, Natural Language Processing (NLP), Machine Learning (ML), and user-centered design to foster equitable healthcare outcomes.
The scope of AIHLT 2025 includes, but is not limited to, the following topics:
- AI aims to improve health information accessibility
- Multilingual health information processing
- Health Information processing in minority languages
- AI-enhanced tools for health literacy assessment and improvement
- NLP models for simplifying medical jargon and enhancing comprehension
- Personalized health education using AI-based recommendation systems
- Machine learning approaches for predicting and mitigating health literacy
Disparities
- Interactive AI-driven systems for patient education and engagement
- Real-time monitoring and analysis of health literacy trends using AI
- AI for multilingual and multicultural health communication
- Explainable AI in health communication to build trust and transparency
- Applications of conversational agents and chatbots for health literacy
- Adaptive learning systems for improving individual and community health
Literacy
- Data-driven approaches to addressing misinformation and promoting health Awareness
- The role of AI in enhancing digital health tools for diverse populations
- Ethical considerations in deploying AI for health literacy improvements
- Statistical analysis and evaluation of AI systems in health communication
- Applications in public health campaigns, mental health literacy, and chronic disease management
Special Session Organizers:
Amel Fraisse, Université de Lille – GERIICO Lab – France, Email: amel.fraisse@univ-lille.fr