An AI Planning-based Approach for Automated Design of Learning Routes
نویسندگان
چکیده
This paper presents an approach for the automated design of learning routes, based on the application of AI techniques. Our educational planning system is composed of two main modules: an instructor-oriented graphical authoring tool to model the key elements of a course and an automated planner to compute a learning route for a specific student profile or a customised learning route for a group of students. More specifically, the proposed architecture works in three stages: (1) the instructor defines the elements of a course such as learning concepts, tasks, teaching materials, required resources, etc. Our authoring tool helps guide the instructor during the course design; (2) an AI planning process is applied over the course structure defined in the above stage to obtain a generic learning route for a given student profile. This learning route is composed of a set of partially-ordered activities which contemplates teaching goals at different levels of competence. The purpose of this generic learning route is to validate the course design described by the instructor in the authoring tool and check the existence of a feasible implementation for the course; (3) a second AI planning process permits to obtain a customised learning route for a specific group of students. This learning route is a course of actions allocated in time which accounts for the available resources and temporal constraints of the particular teaching context where the course will be taught. Our educational planning system offers two novelties with respect to other tools for course generation: a user-friendly graphical tool aimed at providing instructors support during the specification of a course/subject and an automated planner to compute customised plans totally adapted to the particular learning context, considering time constraints, resource usage and all issues necessary to make a learning route realizable (executable plan). Additionally, our approach allows us to dynamically adapt the learning route during its execution according to the course evolution.
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