Abstract:

The Internet promised to be a boon for learning — a global library of human knowledge that would allow anyone to learn anything. However, very quickly, that resource became a confusing jumble.

How could those of us interested in educational technology improve this situation, bringing the signal out of the noise? We propose using KEPLAIR (Knowledge-based Environment for Personalized Learning using an Artificial Intelligence Recommender), an online platform, currently in initial development, designed to help its users find learning opportunities and materials. Using learning goals chosen by the learner, KEPLAIR will browse the Internet to harvest materials. Then it will filter the result and make recommendations to match the learner’s cognitive level, pre-existing knowledge about the topic, and preferred physical and social environments. Depending on what learners want, KEPLAIR’s recommendations might include a book or video, an online course, a club or community, or even a tutor or learning coach. The intent is not for KEPLAIR to teach, test, or even promote a predetermined curriculum, nor will it require learners to be part of any formal school or learning organisations. KEPLAIR’s purpose is simply to help learners reach their self-chosen goals by highlighting appropriate, attractive, and useful materials so they stand out from the background noise. This will be done in a highly personalized way for each single user, taking into proper account the many aspects involved in recommending, such as needs, background, abilities, aims, interests, tastes, preferences, attitudes, behaviors, motivations, expectations, context, and community.

Obviously, this undertaking poses significant technological, social, and learning challenges. To implement KEPLAIR’s vision, development has begun on an ontology that includes four major learning classes: Goal/Pathway; Learner Profile; Social, Physical, & Digital Environment; and Learning Resource. Based on such an ontology, the AI will draw on semantic analysis of online materials from formal educational institutions, open educational resources (OER), and pre-existing pathways, environments and learning objects. It will engage in conversational dialog with users and user-initiated and user-controlled data uploads to create detailed learner profiles and learning pathways.

This paper will introduce KEPLAIR’s basic structure and mechanisms, offering opportunities to reflect on and respond to the strategies KEPLAIR’s international design team is considering. It will also report on the initial proof-of-concept project currently underway at the University of Bari in Italy.

Keywords:

Recommending, Personalization, Ontologies.