Imagine a software agent that calls your computer its home, helps you to study better, be your friend, and motivates you to act in your long-term best interest, consistent with your own deepest values, personal, social, and other standards.

My research aims to build, deploy, and evaluate a suite of such agents called anthropomorphic agents that mimic and do better on human-like traits to assist learners in their regulatory tasks. Further, these agents will self-regulate, as well as causally model their own anthropomorphic traits such as initiative and subject matter competence in order to inspire high level of trust, shared perspective, and entitativity, thus increasing the learners’ willingness to view them as competent partner for co-regulation and to inspire learners to make the optimal regulatory changes.

To sustain and to succeed in performing such tasks, the agents rely on big data learning analytics techniques where:

1.  sensors continuously observe study episodes of learners,
2.  distributed ontologies continuously instantiate study activities,
3.  causal models continuously establish relationships among study experiences,
4.  high performance platforms continuously offer real-time computing resources, and
5.  pedagogical models continuously look for ways to motivate, engage, classify, learn, and promote subject knowledge and study skills.

These agents aim to attune themselves to the needs of an individual learner or a group of learners, over life, to optimise learning experiences.