1. Intelligent Tutoring Systems Traditional CAI Fully specified presentation text Canned questions and associated answers Lack the ability to adapt to students ICAI: intelligent computer-aided instruction Reasoning Rich representation of domain User modeling Communication of information structures

2. Topics Learning Scenarios Domain Knowledge Representation Student Modeling Student Diagnosis Problem Generation User Interface

3. Learning Scenarios The situation in which the student’s learning is to take place Coaching: offer a student advice and guide him when misdirected Gaming environment: combine both coaching and discovering learning Socratic teaching method Simulation-base training Discovery learning

4. Knowledge Representation Knowledge is the key to intelligent behavior The form in which we store the knowledge is crucial to our abilities to use it No general form suitable for all knowledge Challenge determine the type of knowledge required, and suitable representation for that knowledge, to support teaching particular subjects

5. Script Representation WHY, a Socratic tutoring system Test student’s understanding of the major casual factors involved in rainfall Require a representation with different levels of abstraction Script Nodes represent processes and events, links represent such relations as X enables Y or X causes Y Each node have a hierarchically-embedded subscript Roles are bound to geographic or meteorological entities in a particular case

6. Semantic network SCHOLAR A mixed-initiative, fact-oriented system Requires a highly-structured data base in which concepts and facts are connected along many dimensions Semantic network Nodes and links represent objects and properties Generate questions, answers, errors and branching information from the semantic network of knowledge Support flexible query and reasoning

7. Knowledge Representation More technologies Simulation-base training Constraint-base reasoning Condition/action rules Multiple representation viewpoints

8. Student Modeling Overlay Modeling student’s knowledge is viewed in terms of the tutor’s domain knowledge Several approaches Semantic net with nodes and links are added as they are taught Stars with the expert knowledge base and annotates deviations that are subsequently discovered Skill modeler: student modeled by the set of skills he has mastered

9. Buggy Model Fact: the novice’s error can not be explained by the expert’s knowledge Buggy model employs both correct and “buggy” rules To understand an error, a combination of these correct and buggy rules has to be found to produce the same incorrect answer

10. Student Diagnosis Buggy model Procedural networks: partially-ordered sequences of operations Answer is evaluated by search for a path through this network of skills Problem: The number of paths grows exponentially Require an explicit enumeration of bugs

11. Student Diagnosis (cont) Error taxonomy The knowledge of the types of misconceptions in a particular domain Object-oriented approach Each knowledge class inherits diagnostic capabilities from a particular Diagnoser class

12. Problem Generation A tree-structured decision process Each level represents another decision on what to include in the problem Each branch represents one alternatives The branches can be augmented with probabilities Semantic net Encode the types of objects and relevant attributes of these objects A generative procedure fill in the particulars of the problem

13. Problem Generation (cont) Problem generation, expert problem solving and student diagnosis can be viewed as a set of constraints on their solution We can evaluate student answers by checking that al constraints are satisfied Give student feedback on wrong answers by telling him which constraints he failed to satisfy

14. User Interface Text generation in tutoring systems Most avoid true natural language mechanisms SCHOLAR incorporate rich natural language in two distinct levels: semantic and syntactic

15. User Interface (cont) Natural language parsing Rich natural language facilities Semantic grammars: look for understandable fragments in the input Using graphical or menu-based input