Kshitij Sharma

Kshitij Kshitij is an Associate Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), with additional affiliation to the Department of Language and Literature. His research bridges Human-Computer Interaction, Artificial Intelligence, and Educational Technology, focusing on multimodal data to understand learning and collaboration.

His research interests include Applied Machine Learning, Multimodal Learning Analytics, Human-Computer Interaction, and Educational Technology. He investigates how physiological and behavioral data—such as eye-tracking, EEG, facial expressions, and system logs—can reveal insights into user expertise, motivation, and performance. His work applies empirical and mixed-methods approaches to contexts like MOOCs, children’s coding activities, e-commerce, and business process modeling. He has pioneered the use of Extreme Value Theory for analyzing collaborative behavior in time-series data.

His recent publications demonstrate a strong trend toward integrating AI and multimodal sensing in educational settings, particularly in understanding children’s collaborative and cognitive states, designing intelligent feedback systems, and enhancing teacher dashboards. His work increasingly explores generative AI, embodied learning, and neuro-adaptive environments.

• Eye-tracking and AI for motivation and learning (Smart Learning Environments, 2020)
• Wearable sensing for learning experience (IJHCS, 2020)
• Extreme Value Theory for collaboration prediction (Journal of Learning Analytics, 2017)
• Multimodal effort profiling in children (JCAL, 2025)
• Neuro-adaptive business process modeling (CEUR, 2024)

Kshitij actively mentors students and early-career researchers, including Serena Lee-Cultura, Sofia Papavlasopoulou, and Jennifer Olsen. His collaborative work often involves interdisciplinary grants related to learning analytics, educational technology, and human-centered AI. He contributes to major initiatives such as the Multimodal Learning Analytics Handbook (Springer, 2022).

He is a core member of research teams focused on multimodal data in education, working closely with the MULTIS group at NTNU and international collaborators. His lab integrates wearable sensors, interactive surfaces, and AI to build intelligent, adaptive learning environments.