JARET: A Human Assistive A.I. Agent for Goal Review and Time Management

Andrew Schwabe


Many students do not set goals or plan their time weekly (due to lack of ability, perceived difficulty, and other reasons) resulting in procrastination, stress, and lower academic performance. This paper presents the design methodology and considerations for a human assistive AI agent that helps students review and plan for study goals, reducing a large abstract problem into a set of simpler review tasks.  J.A.R.E.T. (Just A Recommender Engine for Time) uses key principles from Self-Regulated Learning and Cognitive Load Theory in an interactive system that guides students through focused goal review and planning tasks, then uses a constraint satisfaction AI agent to assemble a proposed calendar schedule designed to help achieve the student’s goals.  The AI agent uses hard and soft constraints with a value function designed and searches for a best fit that follows constraints while trying to also fit student preferences.  Results show that the design is able to reliably build recommended solutions when constraints and preferences are reasonable and not overly restrictive.


Assistive AI; Time Management; Self-Regulated Learning; Cognitive Load Theory; Agent Design; Constraint Satisfaction; Scheduling; Planning

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DOI: http://dx.doi.org/10.23887/ijerr.v4i3.41630

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