VOL. 3, NO. 6, June 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and I nformation Sciences ©2009-2012 CIS Journal. All rights reserved. http://www.cisjournal.org 930 Architectural Analysis of Multi-Agents Educational Model in Web-Learning Environments 1 Majida Ali Abed Al_Asadi, 2 Yousif A. Al_Asadi, 3 H. A. Al_Asadi 1 Lecturer, Department of Textile and Clothing Technology, University of Moratuwa, Sri Lanka 1 Lecturer, Department of Computer Science, University of Tikrit, Iraq 2 Lecturer, Department of Econmic, University of Basra, Iraq 3 Lecturer, Department of Computer Science, University of Basra, Iraq 1 majida.alasady@gmail.com , , 2 Yousif_alasadi@yahoo.com, 3 Hamid_alasadi@ieee.org ABSTRACT The article presents a structure for using intelligent multi-agents architecture for an educational system and internet teaching. In this architecture, a model of multi-agents learning environments consists of: the domain model, the communication model and the user model. They contain all the domain knowledge of the target application and the user's profiles. Reconceptualizing the computer as a constructionist medium increases the computer's educational value by allowing the development and support of communities of users. The communication model facilitates communication via more conventional media, but also enables the communication of ideas through the creation and sharing of computational objects (e.g. agents and analysis tools). An agent appears to be appropriate for the implementation of the major functions of intelligent training, support and teaching environments by providing an environment for the definition and sharing of computational components, through the World Wide Web. Keywords: Artificially intelligent; Multi-Agent–Based e-earning Systems; conventional media; learning environments; the world wide web. 1. INTRODUCTION Effective communication is a basis for teaching and learning. The dialogue between student and teacher, and between student and student, conveys experience, knowledge, understanding, ideas, and so much more. Application of artificially intelligent (AI) technologies to teaching and learning, therefore, may not necessarily be a solution to the problem of providing the kind of educational experiences desired in today's society. Capturing both the teacher's and student’s /pupil's experiences and knowledge presents an ongoing challenge for AI researchers [1]. The types of knowledge that can be represented, and how best to represent that knowledge, continue to be the foci of AI research. The ability to measure any success or failure of AI applications depends on how one defines intelligence, and artificial intelligence. It becomes even more difficult as the definitions themselves continue to evolve. The tutoring test contended that acting intelligent is intelligent. A formal, working definition of AI was coined by some researchers [2] as "Learning to do and learning to understand: a lesson and a challenge for cognitive modeling". A more recent definition defines AI as "the study of the computations that make it possible to perceive, reason, and act". Research into human learning and cognitive science is a force that continues to reshape our definition of human intelligence, and our definition of artificial intelligence. Distance education is the practical subset of education that deals with instruction in which distance and time are the criteria attributes; that is, student and teacher (and other students) are separated by distance and/or time, opening up more opportunities to learn through computers, with computers, and from computers [3]. Generally, the focus of distance education has been on using the computer primarily as a communication tool (learning through computers). The "computer-as-mind tool" approach suggests that working with a computer, in an authentic task, acts as a stimulus for learning (learning with computer) [4]. Despite criticism of the “teacher-in-the-box”, efforts to improve the humanness and adaptivity of our interactions with computers will likely improve our ability to learn from them. We have only begun to achieve the critical mass of interface technologies needed to make the computer an intelligent partner in instruction. Work on the development of intelligent agents attempts to produce software that acts as an intelligent assistant. Some intelligent agents feature a conversational component that has a human visage and displays facial gestures in essence displaying personality or character. The work of [5] shows that humans use social rules as they interact with computers. As we combine these capabilities with natural language understanding and the ability to process "spoken speech," the computer approaches the role of partner in a conversation rather than a questioner or simple conditional decision maker. These streams converge in the need for a definition of interactivity, both in distance learning through computers as communication tools and in intelligent instructional systems where we learn from the computer tutor/teacher. The intent of this article is to provide an architectural analysis of interactivity, with the goal of constructing intelligent multi-agents learning environments