AI for Human Learning and Behavior 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.
Topics include but are not limited to:
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS, OpenAIRE
AI for Human Learning and Behavior Change welcomes submissions of the following article types: Brief Research Report, Code, Core Concept, Correction, Data Report, General Commentary, Hypothesis and Theory, Methods, Mini Review, New Discovery, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology Report.
All manuscripts must be submitted directly to the section AI for Human Learning and Behavior Change, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section AI for Human Learning and Behavior Change will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Artificial Intelligence.
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