AI and Cognitive Testing: A New Conceptual Framework and Roadmap

AbstractUnderstanding how a person thinks, i.e., measuring a single individual's cognitive characteristics, is challenging because cognition is not directly observable. Practically speaking, standardized cognitive tests (tests of IQ, memory, attention, etc.), with results interpreted by expert clinicians, represent the state of the art in measuring a person's cognition. Three areas of AI show particular promise for improving the effectiveness of this kind of cognitive testing: 1) behavioral sensing, to more robustly quantify individual test-taker behaviors, 2) data mining, to identify and extract meaningful patterns from behavioral datasets; and 3) cognitive modeling, to help map observed behaviors onto hypothesized cognitive strategies. We bring these three areas of AI research together in a unified conceptual framework and provide a sampling of recent work in each area. Continued research at the nexus of AI and cognitive testing has potentially far-reaching implications for society in virtually every context in which measuring cognition is important, including research across many disciplines of cognitive science as well as applications in clinical, educational, and workforce settings.


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