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 This paper presents a new approach to the analysis and design of intelligent ( ) tutoring systems ITS , based on re active principles and cognitive models, this way leading to multiagent architecture. In these kinds of models, the analysis problem is tre ated bottom-up, as opposed to that of traditional ( ) artificial intelligence AI , i.e., top down. We present one ITS example ( called Makatsin a me aning tutor in TOTONACA, a Mexican pre-Co- ) lumbian language , constructed according to this approach, which te aches the skills necessary to solve the truss analysis problem by the method of joints. This learning domain is an integration skill. The classical ITS work is based on explicit goals and an internal representation of the environment. The new approach has re active agents which have no representation of their environment and act using a stimulus rresponse behavior type. In this way they can respond to the present state of the environment in which they are embedded. With these elements, errors, and teaching plans, e ach agent behaves as an expert assistan t that is able to handle different te aching This work was supported in part by the Universidad Autonoma Me tropolitana,

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