Extending the Diagnostic Capabilities of Artificial Intelligence-Based Instructional Systems

Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context-sensitive feedback and guidance are crucial to keeping problem-solving efforts fruitful and efficient. This article reviews AIbased algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. Artificial intelligence (AI) research solutions have the potential to boost the impact of these systems by enhancing their diagnostic capabilities. For example, unlike one-on-one human tutoring, it is rare to find computer-based learning environments that not only provide opportunities for practice on complex problems (such as working on multistep algebra problems, or practicing programming by writing multiline code), but also provide contextually relevant feedback and guidance based on an analysis of individual problem-solving actions. The problem with the first extreme is that students develop skills in context and complexity that are different from their eventual application context.