The field of artificial intelligence (AI) in education has become the most challenging area in the last several years. It includes the disciplines; cognitive and social psychology, computer science, empirical psychology, software and knowledge engineering. The goal of the field is to deliver computer-based systems (or knowledge-based software) which can be used in real teaching, learning and training situations. Using AI concepts and techniques new forms of educational software can be created that allow the computer to act as an intelligent tutor. Intelligent Tutoring Systems (ITSs) are smart computer tutors and typically have an expert model, student model, instructional module, and interface [1]. ITS must organize their knowledge in a lesson-oriented manner. This organization must be dynamically adjusted by the system according to student models. Additionally, ITS enhances instructor productivity, enabling them to cope with generating more complex training systems required to provide higher skill levels to today's high-tech trainees. It also provides tailored instruction and remediation, while allowing flexibility in teaching methods, achieving many of the same benefits as one-on-one instruction.
On the other hand, Medical Intelligent Systems (MISs) are concerned with the construction of intelligent software that performs diagnosis and make therapy recommendations. Unlike other medical applications abased on other programming methods such as purely statistical methods, MISs are based on symbolic models of disease and their relationship to patient factors. Many types of MISs are in existence today and are applies to different medical tasks, e.g. generation alerts and remainders, diagnosis assistant, therapy critiquing and education [2]. In this talk we focus our discussion around the AI-based technologies which are used in developing medical tutoring/learnining systems at Ain Shams University. The talk includes the following points:
1- Brief overview of the field of AI in education.
2- Brief overview about the knowledge representation techniques which are usually used in developing knowledge-based systems.
3- Expert systems technology for developing expert systems prototypes for diagnosing heart and cancer diseases.
4- Using neural network technology in brain tumor diagnosis.
5- Discussing the main concepts of data mining technology.
6- Machine Learning techniques, namely, rough sets, genetic algorithms and visualization which are currently used in developing data mining medical systems for thrombosis and thyroid diseases.