Workshop:
AI-Driven Learning Systems in Digital Health: Transforming Healthcare Training Through Intelligent
Educational Technologies
Scope and Topics
The rapid advancement of artificial intelligence (AI) is transforming healthcare delivery and digital health ecosystems. However, the integration of AI into healthcare training remains underexplored, particularly in scalable and practical contexts. This workshop focuses on AI-driven learning systems, including large language model–based tools, and their role in enhancing healthcare education, training efficiency, and workforce preparedness. Participants will explore how AI-supported approaches—such as intelligent content generation, learning analytics, and cognitive load optimization—can be applied in clinical education and digital health training environments. Through demonstrations, interactive activities, and discussion, the workshop will provide practical insights into implementing AI-assisted learning frameworks. The session aims to bridge the gap between technological innovation and real-world adoption in healthcare education, particularly in resource-constrained settings.
Topics include:
• AI in healthcare education and training
• Large language models in clinical learning
• Learning analytics in digital health systems
• AI-assisted knowledge processing
• Cognitive load optimization using AI
• Data-driven training for healthcare professionals
Timeline
Submission deadline: 30 June, 2026
Notification deadline: 20 July, 2026
Camera-ready deadline: 30 July, 2026
Topics
Publication
All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library.
PervasiveHealth proceedings are indexed in leading indexing services, such as Web of Science, Compendex, Scopus, DBLP, EU Digital Library, Google Scholar, IO-Port, MathSciNet, Inspec, and Zentralblatt MATH.
Submission Guidelines
Workshop papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format.
The workshop accepts position papers of 4+ pages (published as a part of the EAI PervasiveHealth 2026 Conference Proceedings in a non-indexed Annex section).
The paper submissions must follow the Springer formatting guidelines (see Author’s kit).
Read the Publication Ethics and Malpractice Statement.
Workshop Organizers
Dr. Nida Fatima
Rawalpindi Medical University, Pakistan
Dr. Nida Fatima is a medical educator at Rawalpindi Medical University, Pakistan, with over a decade of experience in undergraduate and postgraduate teaching. She holds an MBBS, MPhil in Microbiology, and a Master’s degree in Health Professions Education. She has certifications in Artificial Intelligence (University of Maryland) and Data Science (IBM).
Her research focuses on AI in medical education, flipped classroom models, and digital learning strategies. She has recently published work on AI-supported flipped learning approaches and is the author of the book MCQ and OSCE Collection for Medical Education. Her work emphasizes scalable, data-driven solutions for healthcare education, particularly in resource-limited settings.
