Develops an intelligent tutoring system for oxidation number assignment in chemistry, suggesting enhanced educational engagement through AI integration.
The need for improved interactive tutoring capabilities in educational software for chemistry problem solving is an important one clearly articulated by teachers and students. The purpose of this work is to incorporate concepts from the field of artificial intelligence (AI) as a route to meaningful individualized tutoring. The fundamental design shift is to replace the usual static list of problems and answers with a direct rule-based representation of chemical and pedagogical principles, and then use AI methods to simulate reasoning using these principles to tutor students. Here, we report the development of an intelligent tutoring system for oxidation number assignment using a model-tracing cognitive tutor approach. The tutoring system, accessed through the Internet using a web browser, contains several important advantages over conventional chemistry tutorial software. The system can tutor the assignment of oxidation numbers for any chemical formula entered by the student, rather than being limited to a fixed set of problems, and analyze and respond to the student’s own work, which is directly entered by the student in detail one step at a time.
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Johnson et al. (2010) studied this question.
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