At the intersection of computer science and cognitive science, we conduct applied research into human-centered explainable AI and intelligent data-intensive systems and their application in technology-enhanced learning and knowledge management domains. Following a human-centered approach, our research bridges artificial intelligence (AI) and human-computer interaction (HCI) to innovate scalable, interactive, transparent, and user-controllable tools that augment and enhance rather than replace human capabilities to understand and interact with AI systems (e.g., recommendation and decision making systems) and large datasets to solve real world problems. Topics currently being researched at our group include:

Trustworthy Human-AI Interaction

Developing user interfaces that are interactive, visual, and exploratory with the goal to preserve human control and help users understand and personalize AI systems, in particular recommendation and decision support systems, thus promoting transparency and ultimately fostering a more trustworthy and accountable experience for users.

• Explainable AI / recommender systems

• Responsible recommender systems

• Interactive recommender systems

• User modeling and personalization

• Intelligent user / explanation interfaces

• Information visualization and visual analytics

Human-Centered Learning Analytics

Developing human-centered learning analytics technologies and applications that emphasize the human factors in learning analytics and truly meets user needs. Human-centered learning analytics (HCLA) refers to (1) the systematic user involvement in the design, development, deployment, and evaluation of learning analytics and (2) blending personalization and learning analytics to design and implement smart learning, engineering and technology environments capable to continuously analyze and support the performance of learners, users, and industries, and offer them learning experiences in context and for operational excellence improvement.

• Educational and Engineering data science

• Learning and Engineering analytics & HCI

• Explainable learning analytics

• Open learning and collaboration analytics

• Analytics-enhanced personalized learning

• Trustworthy learning and engineering analytics

• Privacy-preserving data collection and management

• Assessment and feedback

• Corporate learning and operational excellence analytics