Magazine article AI Magazine

Extending the Diagnostic Capabilities of Artificial Intelligence-Based Instructional Systems

Magazine article AI Magazine

Extending the Diagnostic Capabilities of Artificial Intelligence-Based Instructional Systems

Article excerpt

Active problem solving has been shown to be one of the most effective ways to acquire complex skills. 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 Al-based algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. The article also describes the crucial role sensor-based estimates of cognitive resources such as working memory capacity and attention can play in enhancing the diagnostic capabilities of intelligent instructional systems


Computer-based educational technology has had a transformative impact in every imaginable educational context--from tablet-based educational games for preschoolers, to massively open online courses for university students and independent learners. 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. In contrast, because of the technical challenge of making sound diagnostic inferences, we typically find two extremes represented in the design of instructional systems: either systems that constrain the learner by restricting allowable actions during skill acquisition (for example, multiple choice problems) or systems that provide opportunities for open-ended practice, with the only corrective feedback coming from the natural feedback inherent to the task environment (for example, a programming problem that a student solves on his or her own--with built-in feedback from the interpreter or compiler). The problem with the first extreme is that students develop skills in context and complexity that are different from their eventual application context. The problem with the latter is that students may flounder with inadequate feedback, or have incorrect or superficial strategies reinforced in the absence of corrective feedback and guidance.

These challenges point to the need for diagnostic techniques that are (1) tied to problem solving--capable of interpreting and assessing individual actions in the context of relatively unconstrained problem-solving activities (for example, writing a computer program, flying a scenario in a flight training simulator); (2) automated--reducing the need for manual intervention; (3) objective--minimizing confounds stemming from subjective biases; and (4) fine-grained--providing feedback at the level of individual problem-solving actions, as opposed to problem-solving outcome alone.

Cognitive Tutors: A Promising AI Approach

Cognitive tutors, based on the adaptive control of thought--rational (ACT-R) cognitive architecture, represent one promising approach for boosting the diagnostic capability of interactive learning environments (Anderson et al. 1995). The development of the ACT-R theory of cognition, which describes how humans perceive, think, and act, has been led by psychologist John Anderson and colleagues at Carnegie Mellon University (Anderson and Lebiere 1998). ACT-R is instantiated in the form of a programming language with primitive constructs that embody specific assumptions about human cognition. It has been used to model human performance in a broad array of complex cognitive tasks, ranging from automobile driving (Salvucci 2001) and tactical decision making (Anderson et al. 2011) to algebra and computer programming (Corbett, Anderson, and O'Brien 1995). …

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