At this stage, it is not clear to what extent an AI-ED system will need itself to have some facility at diagrammatic reasoning. Students, being students, may not yet have learned the requisite ability. For example, a student trying to solve an optimisation problem using PERT networks may have difficulty through misinterpreting the semantics of the diagram, rather than through misunderstanding the problem itself. An AI-ED system which aimed to give advice to such a student could hardly do so without itself being able to monitor diagrammatic reasoning performance. As there are individual differences in students' diagrammatic reasoning ability, it is likely to be necessary for systems to be able to dynamically generate appropriate diagrams rather than simply present pre-specified ones.
Diagrams differ greatly in their appropriateness for a particular problem.
A system ought to be able to detect when a student has adopted an inappropriate diagram and to give some explanation of why it is inappropriate. It is much too early to say whether they will be adequately achievable in the case of diagrammatic reasoning. At the moment, only speculative comments on how diagrams facilitate reasoning and problem-solving are possible. What relations are 'directly indicated' must be learned - for example, of the three complex diagrams mentioned above, relative size is important in architectural drawings, but much less so in visual programs or circuit diagrams. Diagrammatic reasoning may involve re-drawing the diagram and adding symbolic annotations.