Intelligent Computer-Aided Instruction: A Survey Organized Around System Components
---
paper_title: The Geometry Tutor

paper_content:

The tutor for doing proofs in high school geometry consists of a cot of ideal and buggy rules (IRR), a tutor, and an interface. The IBR is responsible for ehiuertly computing matcher, to all the correct and incorrect rules The interface is responsible for interacting with the student and graphically representing the proof. The tutor is responsible for directing the IBR and interface to achieve a current tutorial strategy. The strategy wo employ involves tracing the student's behavior in terms of what rules in the IBR it instantiates, correcting the student when behavior deviates below a minimum threshold, and helping the student over hurdles. While incomplete, current evidence indicates the geometry tutor is guite effective. ::: ::: The Advanced Computer Tutoring Project has been working on the development of intelligent computerhased tutors for mathematics and science subjects in the range of senior high-school to junior college. This paper describes the general framework that we have developed and its instantiation in the case of a tutor tor generating proofs in geometry. First, we will describe the general philosophy of out tutoring efforts. Second, we will describe the basic structure of the geometry tutor that we have built. Third, we will describe the ongoing efforts to evaluate the tutor.
---
paper_title: On the generation of problems

paper_content:

Abstract : The design of programs that generate questions and exercises for students is discussed. The task of direct interest is the generation of programming exercises. This is dealt with in some detail and sets of such exercises produced by programmed generators are presented.
---
paper_title: The goal structure of a socratic tutor

paper_content:

We describe the current version of the Why System, a script-based socratic tutor which uses tutoring strategies formulated as production rules. The current system is capable of carrying on a dialogue about the factors influencing rainfall by presenting different cases to the student, asking for predictions, probing for relevent factors, entrapping the student when he has not identified all necessary factors, and presenting counterexamples. The current system is incomplete because it lacks a goal structure to guide the tutorial sessions. We outline a more complete theory of the goal structure of Socratic tutors based on analysis of human tutorial dialogues. There are two top level goals: (1) refinement of the student's causal model and (2) refinement of the student's predictive abilities. The subgoals are diagnosis of bugs in the student's knowledge and correction of the bugs. This goal-driven control mechanism governs the selection of examples and teaching strategies used by the tutor.
---
paper_title: The goal structure of a socratic tutor

paper_content:

We describe the current version of the Why System, a script-based socratic tutor which uses tutoring strategies formulated as production rules. The current system is capable of carrying on a dialogue about the factors influencing rainfall by presenting different cases to the student, asking for predictions, probing for relevent factors, entrapping the student when he has not identified all necessary factors, and presenting counterexamples. The current system is incomplete because it lacks a goal structure to guide the tutorial sessions. We outline a more complete theory of the goal structure of Socratic tutors based on analysis of human tutorial dialogues. There are two top level goals: (1) refinement of the student's causal model and (2) refinement of the student's predictive abilities. The subgoals are diagnosis of bugs in the student's knowledge and correction of the bugs. This goal-driven control mechanism governs the selection of examples and teaching strategies used by the tutor.
---
paper_title: STEAMER: An interactive, inspectable, simulation-based training system

paper_content:

The Steamer project is a research effort concerned with exploring the use of AI software and hardware technologies in the implementation of intelligent computer-based training systems While the project addresses a host of research issues ranging from how people understand complex dynamic systems to the use of intelligent graphical interfaces, it is focused around the construction of a system to assist in propulsion engineering instruction The purpose of this article is to discuss the underlying ideas which motivated us to initiate the Steamer effort, describe the current status of the project, provide a glimpse of our planned directions for the future, and discuss the implications of Steamer for AI applications in other instructional domains
---
paper_title: Dynamic Student Modelling in an Intelligent Tutor for LISP Programming

paper_content:

We describe an intelligent tutor for LISP programming. This tutor achieves a set of pedagogical objectives derived from Anderson's (1983) learning theory provide instruction in the context of problem-solving, have the student generate as much of each solution as possible, provide immediate feedback on errors, and represent the goal structure of the problem-solving. The tutorial interface facilitates communication and prevents distracting low-level errors Field tests of the tutor in college classes demonstrate that it is more effective than conventional classroom instruction.
---
paper_title: Methodology for building an intelligent tutoring system

paper_content:

Over the past 6 years we have been developing a computer program to teach medical diagnosis. Our research synthesizes and extends results in artlficlal intelligence (Al), medicine, and cognitive psychology. This paper describes the progression of the research, and explalns how theories from these fields are combined in a computational model. The general problem has been to develop an "intelligent tutoring system" by adapting the MYCIN "expert system." Thls conversion requires a deeper understanding of the nature of expertise and explanatlon than origlnally requlred for developlng MYCIN, and a concomitant shift in perspective from slmple performance goals to attaining psychologlcal validity in the program''s reasoning process. Others have written extensively about the relatlon of artificlal intelligence to cognltive sclence (e.g., [Pylyshyn, 1978] [Boden, 1977]). Our purpose here is not to repeat those arguments, but to present a case study which will provide a common point for further dlscusslon. To this end, to help evaluate the state of cognitive science, we will outline our methodology and survey what resources and viewpoints have helped our research. We will also discuss pitfalls that other Al-oriented cognitive scientists may encounter. Finally, we will present some questions coming out of our work whlch might suggest possible collaboration with other fields of research.
---
paper_title: The genetic graph: a representation for the evolution of procedural knowledge

paper_content:

I shall describe a model of the evolution of rule-structured knowledge that serves as a cornerstone of our development of computer-based coaches. The key idea is a graph structure whose nodes represent rules, and whose links represent various evolutionary relationships such as generalization, correction, and refinement. I shall define this graph and describe a student simulation testbed which we are using to analyze different genetic graph formulations of the reasoning skills required to play an elementary mathematical game.
---
paper_title: Dynamic Student Modelling in an Intelligent Tutor for LISP Programming

paper_content:

We describe an intelligent tutor for LISP programming. This tutor achieves a set of pedagogical objectives derived from Anderson's (1983) learning theory provide instruction in the context of problem-solving, have the student generate as much of each solution as possible, provide immediate feedback on errors, and represent the goal structure of the problem-solving. The tutorial interface facilitates communication and prevents distracting low-level errors Field tests of the tutor in college classes demonstrate that it is more effective than conventional classroom instruction.
---
paper_title: The computer as a tutorial laboratory: the Stanford BIP project

paper_content:

The B asic Instructional Program (BIP) was developed to investigate tutorial modes of interaction in computer-assisted instruction. BIP is an interactive problem-solving laboratory that offers tutorial assistance to students solving introductory programming problems in the B asic language. This paper describes how the problem presentation sequence is individualized based on a representation of the structure of the curriculum and a model of the student's state of knowledge. The nature of the student-BIP interaction is captured in an annotated student dialogue illustrating a typical session.
---
paper_title: Teaching a complex industrial process

paper_content:

COMPUTER TRAINING FOR INDUSTRY IS OFTEN NOT CAPABLE OF PROVIDING ADVICE CUSTOM-TAILORED FOR A SPECIFIC STUDENT AND A SPECIFIC LEARNING SITUATION. IN THIS PAPER WE DESCRIBE AN INTELLIGENT COMPUTER-AIDED SYSTEM THAT PRO- VIDES MULTIPLE EXPLANATIONS AND TUTORING FACILITIES TEMPERED TO THE INDIV- IDUAL STUDENT IN AN INDUSTRIAL SETTING. THE TUTOR IS BASED ON A MATHEMATI- CALLY ACCURATE FORMULATION OF THE KRAFT RECOVERY BOILER AND PROVIDES AN INTERACTIVE SIMULATION COMPLETE WITH HELP, HINTS, EXPLANATIONS, AND TUTOR- ING. THE APPROACH IS EXTENSIBLE TO A WIDE VARIETY OF ENGINEERING AND INDUSTRIAL PROBLEMS IN WHICH THE GOAL IS TO TRAIN AN OPERATOR TO CONTROL A COMPLEX SYSTEM AND TO SOLVE DIFFICULT "REAL TIME" EMERGENCIES.
---

Title: Intelligent Computer-Aided Instruction: A Survey Organized Around System Components

Section 1: Introduction
Description 1: Set the context for computer-aided instruction (CAI) and intelligent computer-aided instruction (ICAI), outlining the historical background, current challenges, and future directions for ICAI.

Section 2: Learning Scenarios
Description 2: Discuss various learning scenarios in which ICAI can be applied, including rule-example-practice paradigms, simulation-based coaching, gaming environments, mixed-initiative dialogs, interactive simulations, and discovery learning.

Section 3: Domain Knowledge Representation
Description 3: Address the importance of a robust domain knowledge representation in ICAI systems, various knowledge representation techniques, and their significance for effective teaching and problem-solving.

Section 4: Student Modeling
Description 4: Explore different techniques for modeling the student's knowledge, capabilities, and learning behaviors, emphasizing the importance of adapting the instruction to the student's current understanding and learning pace.

Section 5: Student Diagnosis
Description 5: Present methods for diagnosing student misconceptions and errors, highlighting the link between domain knowledge representation and the accuracy of student diagnosis.

Section 6: Pedagogical Knowledge
Description 6: Delve into the strategies for effective teaching incorporated into ICAI systems, focusing on when and how to provide instructional feedback to students.

Section 7: Discourse Management
Description 7: Examine the techniques for managing the interaction and communication between the tutor and the student, ensuring effective and adaptive instructional dialogs.

Section 8: Problem Generation
Description 8: Discuss methods for generating appropriate problems and exercises for students, considering the student's current level and instructional needs.

Section 9: User Interface
Description 9: Describe the components of an effective user interface for ICAI systems, emphasizing the importance of engaging multiple senses and allowing flexible student input.

Section 10: Summary
Description 10: Summarize the key points covered in the survey, reiterating the complexities of designing effective ICAI systems and the promising future directions for research and development in this field.