We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 artificial intelligence (AI) system. The test questions (e.g. “Why do we shake hands?”) were translated into ConceptNet 4 inputs using a combination of the simple natural language processing tools that come with ConceptNet together with short Python programs that we wrote. The question answering used a version of ConceptNet based on spectral methods. The ConceptNet system scored a WPPSI-III VIQ that is average for a four-year-old child, but below average for 5–7 year olds. Large variations among subtests indicate potential areas of improvement. In particular, results were strongest for the Vocabulary and Similarities subtests, intermediate for the Information subtest and lowest for the Comprehension and Word Reasoning subtests. Comprehension is the subtest most strongly associated with common sense. The large variations among subtests and ordinary common sense strongly suggest that the WPPSI-III VIQ results do not show that “ConceptNet has the verbal abilities of a four-year-old”. Rather, children’s IQ tests offer one objective metric for the evaluation and comparison of AI systems. Also, this work continues previous research on psychometric AI.

Prospective Memory (PM), or remembering to perform actions in the future, is of crucial importance for everyday life. This kind of memory is often impaired in stroke survivors and can interfere with independent living. We have developed a computer-based treatment which uses visual imagery to teach participants how to remember time- and event-based prospective memory tasks better. After the treatment, participants practiced their PM skills using videos first, and later in a Virtual Reality (VR) environment. The VR environment uses Constraint-Based Modeling (CBM) to track the user actions and provide individual feedback. We report on a study with 15 stroke survivors, which shows that our treatment is highly effective.

Constraint-Based Modeling (CBM) is an effective student modeling approach which has been used successfully in a wide range of instructional domains. Within the Intelligent Computer Tutoring Group (ICTG), we have developed numerous constraint-based tutors and demonstrated their effectiveness in real courses. In this paper, however, we discuss how we use CBM in the area of cognitive rehabilitation after stroke. Our computer-based treatment is aimed at improving prospective memory. Participants are first trained on how to use visual imagery and then practice in a Virtual Reality (VR) environment. We present how we use constraints to track the participant’s progress when performing tasks in the VR environment.

We developed a new model for iList, our system that helps students learn linked list. The model is automatically extracted from past student data, and allows iList to track students' problem-solving behavior in order to provide targeted feedback. We evaluated the new model both intrinsically and extrinsically. We show that the model can match most student actions after a relatively small sequence of observations, and that iList can effectively use the new student tracker to provide feedback and help students learn.

This paper presents our findings after the first year and a half of a multi-year deployment of an ImmersaDesk/sup (R)/ to a local elementary school, investigating its effectiveness in enhancing science education. These findings deal with how VR can aid in the coordination of multiple representations, and how to integrate the technology into the existing school culture.

A distributed immersive virtual environment was deployed as a component of a pedagogical strategy for teaching third grade children that the Earth is round. The displacement strategy is based on the theory that fundamental conceptual change requires an alternative cognitive starting point which doesnt invoke the features of pre-existing models. While the VR apparatus helped to establish that alternative framework, conceptual change was strongly influenced by the bridging activities which related that experience to the target domain. Simple declarations of relevance proved ineffective. A more articulated bridging process involving physical models was effective for some children, but the multiple representations employed required too much model-matching for others.

The Round Earth Project is investigating how virtual reality technology can be used to help teach concepts that are counter-intuitive to a learner's currently held mental model. Virtual reality can be used to provide an alternative cognitive starting point that does not carry the baggage of past experiences. In particular this paper describes our work in comparing two strategies for teaching young children that the Earth is spherical when their everyday experiences tell them it is flat.

We describe HS, a production system that learns control knowledge through adaptive search. Unlike most other psychological models of skill acquisition, HS is a model of analytical, or knowledge-based, learning. HS encodes general domain knowledge in state constraints, patterns that describe those search states that are consistent with the principles of the problem domain. When HS encounters a search state that violates a state constraint it revises the production rule that generated that state. The appropriate revisions are computed by regressing the constraint through the action of the production rule. HS can learn to solve problems that it cannot solve without learning. We present a Blocks World example of a rule revision, empirical results from both initial learning experiments and transfer experiments in the domain of counting, and an informal analysis of the conditions under which this learning technique is likely to be useful.