Artificial Tutoring Systems: What Computers Can and Can't Know

After more than four decades, development of artificially intelligent tutoring systems has been constrained by two interrelated problems: knowledge representation and natural language understanding. G. S. Maccia's epistemology of intelligent natural systems implies that computer systems will ne...

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Bibliographic Details
Published in:Journal of educational computing research 1997-01, Vol.16 (2), p.107-124
Main Author: Frick, Theodore W.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Assisted Instruction
Computer System Design
Computer Uses in Education
Computers
Expert Systems
Information Systems
Intelligent Tutoring Systems
Knowledge Representation
Natural Language Processing
Teachers
Technological Advancement
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Summary:After more than four decades, development of artificially intelligent tutoring systems has been constrained by two interrelated problems: knowledge representation and natural language understanding. G. S. Maccia's epistemology of intelligent natural systems implies that computer systems will need to develop qualitative intelligence before these problems can be solved. Recent research on how human nervous systems develop provides evidence for the significance of qualitative intelligence. Qualitative intelligence is required for understanding of culturally bound meanings of signs used in communication among intelligent natural systems. S. I. Greenspan provides neurological and clinical evidence that emotion and sensation are vital to the growth of mind—capabilities that computer systems do not currently possess. Therefore, we must view computers in education as media through which a multitude of teachers can convey their messages. This does not mean that the role of classroom teachers is diminished. Teachers and students can be empowered by these additional learning resources.
ISSN:0735-6331
1541-4140
DOI:10.2190/4CWM-6JF2-T2DN-QG8L