Dr Fridolin Wild, the director of the lab, has joined the editorial board of Frontiers in Artificial Intelligence as associate editor. He will be overseeing in particular the area of AI for Human Learning and Behavior Change.

Frontiers, the publisher, was founded in 2007 by EPFL neuroscientists and is headquartered in Lausanne, Switzerland.

The new series in Frontiers in AI for Human Learning and Behaviour change welcomes article submissions in the full spectrum of applying AI theories, concepts, and techniques to support people in their learning and voluntary behavior change.

Research in the areas of AI in Education (AIED), Collaborative Learning, and more recently into Data Mining and Learning Analytics, is helping create better learning tools and support environments to democratize education and make it more effective. Behavior Change technologies, traditionally included in the health science domain, can help people avoid addictions and engage in healthy behaviors. With the increasing availability of powerful mobile and ubiquitous computing technologies, as well as inexpensive sensors, behavior change technologies have become commonplace and have expanded towards human behaviors in relation to environment, social engagement, safety, productivity and learning (for example, avoiding procrastination). Just like intelligent tutors, behavior change systems require understanding and modeling user activities, personalization, recommending new activities and sequences, supporting decision-making. They also often engage the user’s friends to provide social support of the activity through collaboration or competition. These topics are particularly the focus of research in the areas of Quantified Self, Persuasive Technology, Recommender Systems, Decision-Support Systems and Learning Technologies.

Changing human behavior is a learning process – in other words, Human Learning and Behavior Change are interconnected. In fact, on the one hand researchers in Persuasive Technology and Behavior Change can learn from the advances in the area of AIED, Learning Analytics and Educational Data mining, Recommender Systems; on the other hand, researchers in AIED can learn from the current work that is going on in Behavior Change and Persuasive Technologies, Quantified Self, regarding the use of context, motivation strategies, cognitive biases, etc.

Bridging these currently distinct areas in our journal section is key to enable cross-fertilization, and to provide an innovative approach to foster the work “in-between”. AI for Human Learning and Behavior Change publishes review articles, communications, and original research papers describing applications of AI technologies to learning and behavior change.


Professor Julita Vassileva, University of Saskatchewan, Faculty member ARIES lab

Associate Editors

Susanne Lajoie, McGill University
Sabine Graf, Athabasca University
Vania Dimitrova, University of Leeds
Seiji Isotani, University of Sao Paulo
Chad Lane, University of Illinois Urbana Champaign
Esma Aimeur, University of Montreal
Judith Masthof, Utrecht University
Harri Oinas-Kukkonen, University of Oulu
Alexandra Cristea, University of Durham
Sergey Sosnovsky, Utrecht University
Ralf Klamma, University of Aachen
Roger Nkambou , University of Quebec at Montreal
Fridolin Wild, Oxford Brookes
Fabrice Popineau, CentraleSupélec
Marcus Specht, TU Delft
Milos Kravcik, DFKI

Topics include but are not limited to:

• Intelligent tutoring systems
• AI or data-driven methods for modeling pedagogical knowledge and instructional planning
• AI methods and techniques for modeling collaborative learning processes, cohorts of learners and learning social networks
• Learning goals, learning sequences, recommendation of learning activities • Affective and motivational aspects in AIED systems
• Data-driven design or adaptation of learning environments
• Data-powered collaborative learning environments
• Peer-help, peer-mentoring, peer-review systems
• Pedagogical / persuasive agents
• Adaptive / personalized Incentives and motivations for participation in collaborative (learning or not) environments
• Gamified (learning or not) environments, games with a purpose
• Adaptive / personalized persuasive strategies and persuasive systems design in different domains (environment, learning, social engagement, etc.)
• Self-monitoring and Persuasive systems for behavior change in health and medicine
• Transparency and accountability – open learner / user models, self-monitoring, generating explanations of pedagogical and persuasive strategies and recommendations
• Ethical issues of persuasive and behavior change systems