26 Artificial Intelligent Tutoring System

DR. Geeta R Thakur

epgp books

 

Module Structure:

 

25.0 Learning outcomes

 

25.1  Introduction

 

25.2  Artificial intelligence tutoring systems: Concept

 

25.3 General architecture of Artificial intelligence tutoring system

 

25.4 Advantages of Artificial intelligence tutoring system

 

25.5 Limitations of Artificial intelligent tutoring system

 

25.6 Examples of Artificial intelligence tutoring system in education

 

25.7 Let us sum up

 

25.0 LEARNING OUTCOMES

 

After going through this module you will be able to:

  • Explain the concept of Artificial tutoring system
  • Explain the various modules of Artificial intelligence tutoring system
  • Explain the advantages of Artificial intelligent tutoring system
  • Explain the limitations of Artificial intelligent tutoring system
  • Discuss various examples of Artificial intelligent tutoring system

 

25.1 INTRODUCTION

 

Students always need one to one attention. But teachers have huge class and limited time.Tutoring ensures individualized paths and effective learning. Intelligent tutoring system (ITS) enables to provide the best alternative to tutoring.ITS is software that mimics real tutor. It poses problems, provides support, redirects mistakesand provides encouragement.ITSis where a computer assisted instructional programme functions intelligently.

 

25.2 INTELLIGENT TUTORING SYSTEM: CONCEPT

 

Teachers started using machines to assist in their instructional endeavors before the invention of the computer. After microcomputer systems became more sophisticated, Computer-Assisted Instruction allowed for more flexible and comprehensive instruction. A student could skim through or skip some of the material using this type of instruction just as one could skip some pages of a book. Most CAI programs were not considered intelligent. Intelligent computer assisted instruction was introduced by Jaime Carbonell in 1970 when he designed a computer program called SCHOLAR. This program is considered the first Intelligent Tutoring System (ITS).

 

MEANING OF INTELLIGENT TUTORING SYSTEM

 

Sharon Derry(1993): “An intelligent instructional system can observe what the learner is doing during problem solving and has done over a series of problem solving session and from this information draw inferences about students’ knowledge, belief and attitudes in terms of some theory of cognition.”

 

Lee Gugerty (1993) : “Intelligent tutoring involves explicit modelling of expert representations and cognitive processes, detection of student errors, diagnosis of students’ knowledge, instruction adapted to student’s knowledge state, hints, feedback and explicit didactic instruction and doing all of the above in a timely fashion as the student solves problems.”

 

ITSs seek to mimic the methods and dialog of natural human tutors, to generate instructional interactions in real time and on demand, as required by individual students.Intelligent tutoring system contains knowledge about the domain and also knowledge about the student and how to teach that student. It aims at providing each student with a computer based tutor that has all the qualities of a master teacher. This includes deep subject matter expertise, excellent knowledge of teaching techniques, powerful communication skills and the ability to inspire and motivate students to learn.

 

Implementation of ITSs incorporates computational mechanisms and knowledge representations in the field of artificial intelligence, computational linguistics and cognitive science.

 

An ITS is an educational software containing an artificial intelligent component. The software tracks students’ work, tailoring feedback and hints along the way. By collecting information on a particular student’s performance, the software can make inferences about strengths and weaknesses, and can suggest additional work.

 

ITSis also known as ICAI (Intelligent Computer Aided Instruction) and KBTS is (Knowledge Based Tutoring System.

 

25.3 GENERAL ARCHITECTURE OF INTELLIGENT TUTORING SYSTEM

 

ITSs consist of at least three basic components (Barr &Feignbaum, 1982; Bonnet, 1985)

  • The expert knowledge module
  • The student knowledge module
  • The tutoring module

More recent research added fourth component:

  • The user interface module

 

The expert knowledge module

 

The expert knowledge module contains a description of the knowledge or behaviors that represent expertise in the subject matter domain.The ITS is teaching –often an expert system or cognitive model. It comprises the facts and rules of the particular domain to be conveyed to the student, i.e. the knowledge of the expert. Expert knowledge is represented in various ways, including semantic networks, frames and production system.

 

Expert knowledge module has the following elements:

  • Surface knowledge e.g. the description of various concepts that the student has to acquire.
  • The representational ability that has to be a critical part of expertise.
  • The ability to include implicit representational understanding from explicitly represented information.

 

The expert knowledge module or domain expert serves as:

  • The source of knowledge to be presented to the student, which includes generating questions, explanations and responses.
  • A standard for evaluating the student’s performance.
  • Evaluative tool to detect common systematic mistakes, and if possible identify any gap in the student’s knowledge.
  • Tool to assess the student’s overall progress.
  • The student knowledge module:

 

It refers to the dynamic representation of the emerging knowledge and skill of the student.The student knowledge module uses a student model containing description of student knowledge or behaviors, including his or her misconceptions and knowledge gaps. A mismatch between a student’s behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction.

 

ITSs are computer programs that are designed to incorporate techniques from the AI community in order to provide tutors which know what they teach, who they teach and how to teach it. ITSs similarly be thought of as attempts to produce in a computer, a behavior which if performed by a human, would be described as good teaching.

 

Student model module is helpful to:

  • Eradicate bugs in the student’s knowledge.
  • Correct ‘incomplete’ student knowledge.
  • Initiate significant changes in the tutorial strategy, to help diagnose bugs in the student’s knowledge
  • Assess the students.

 

In all, students’ model module acts as a source of information about the student, and serves as a representation of the student.

 

The tutoring module

 

It is the part of the ITS that designs and regulates instructional interactions with the student. It is closely linked to the student model, using knowledge about the student and its own tutorial goal structured to decide which pedagogic activities will be presented; hints to overcome impasses in performance, advice, support, explanations, different practice tasks, test to confirm hypothesis in the student’s model.

 

User interface module

 

It is the communicating component of the ITS which controls interaction between the student and the system. In both directions, it translates between the system’s internal representation and an interface language that is understandable to the student. When the ITS presents a topic, the interface can enhance or diminish the presentation. Since the interface is the final form in which the ITS presents itself, qualities such as ease of use, and attractiveness could be crucial.

 

Web based intelligent tutoring system:

 

 

Web based Intelligent tutoring system allows users to take a lesson without time and space constraints. It requires zero cost installation and has maximum time and place flexibility.

 

There are three types of Web based ITS:

  • Static WBT: Teacher arranges learning material in order to cover one or more topics and convert them in interactive linked HTML pages. Learners can exploit it only by following the path established by teachers.
  • Personalized WBT: Teachers using a specific kind of software named course management system are able to perform manually a set of additional tasks. They can monitor student knowledge by testing them, assign recovery material if necessary define different paths for different learning goals.
  • Adaptive WBT: It includes all features of a personalized WBT but the teacher is supported in his activity by using Artificial Intelligence technique.

 

25.4 ADVANTAGES OF INTELLIGENT TUTORING SYSTEM

 

Reduces geographical barriers:

 

The users can take the course sitting at home or at office. It is a very convenient way of learning. It reduces cost and time as well as the learner does not need to travel for the course.

 

Most of the internet educational material lack interactivity and diagnostic capability. Web based ITS provide a better option for effective e-learning.

 

With the help of web based ITS, it is possible to reach heterogeneous group of learners and satisfy their needs.

 

Improves tutoring:

 

Personal tutoring is effective in terms of effectiveness. But it is not plausible in terms of physical space, financial constraints and availability of human tutor. ITS can provide students with experiences similar to that of human tutoring that too with very low cost. Better quality tutoring can be made available to all the students irrespective of their location.

 

Variety of uses:

 

ITS are suitable in a variety of learning environments. ITS has been used for mathematics, physics course. ITS has been proved successful for radar operational skill in navy, in flight simulation, army fire training, health services. ITS can be also used to teach soft skills like selling, negotiating, collaborating etc.

 

Cost effective

 

Initially one has to incur cost for ITS for purchasing the software and hardware. In future it is proved a very cost effective alternative. This cost is also less expensive than cost incurred in funding building, other infrastructure. It can be said that investing in ITS is a onetime investment which will give returns for a very long period in the future.

 

Motivation

 

Through ITS, students are motivated to complete assignments. Students show greater satisfaction with learning than students who participate in regular classroom as they find the teaching-learning process more interactive and interesting.

 

Greater achievement rate

 

Students who learn through ITS show increase in marks. Research also proved that ITS programs develop greater problem solving capabilities.

 

Enhance learning speed

 

ITS’ students increased their cognition of concepts and moved through the assignments faster.

 

They were seen to grasp and retain the content at a better speed.

 

 

25.5 LIMITATIONS OF INTELLIGENT TUTORING SYSTEM

 

· Not for all subjects

 

Subjects like mathematics, science and logic are suited to an ITS model. The problem solving levels within the design can move through various levels of complexity. Subjects like History, Literature, Geography and Social science making the ITS design more complicated.

 

Pedagogical constraints

 

Human tutor can use a variety of teaching methods. They also adapt teaching methods according to the learners’ responses. This is not possible with ITS. The ITS uses single method of teaching.

 

Gaming the system

 

Students systematically use the “help” “trial and error” property of the system to advance through various levels of problem solving. Students might complete the task without actually engaging in critical thinking.

 

Lengthy and expensive development of ITS

 

Development of ITS requires the cooperation and input of subject matter experts, the cooperation and support of individuals across both organizations and organizational levels. The other problem is the development of software within both budget and time constraints. The long timeframe required for development and the high cost of the creation of the system components puts the constraint on implementation of ITS in real world. A high portion of that cost is a result of content component building.

 

Weak pedagogy

 

Current ITS uses the pedagogy of immediate feedback and hint sequences that are built in to make the system “intelligent”. This pedagogy is criticized for its failure to develop deep learning in students. When students are given control over the ability to receive hints, the learning response created is negative. Some students immediately turn to the hints before attempting to solve the problem or complete the task.

 

ITS system fails to ask questions to the students to explain their actions. If the student is not learning the domain language then it becomes more difficult to gain a deeper understanding,

 

Evaluation

 

Evaluation of an intelligent tutoring system is an important phase; however, it is often difficult, costly, and time consuming. Even though there are various evaluation techniques presented in the literature, there are no guiding principles for the selection of appropriate evaluation method(s) to be used in a particular context.

 

26.6 EXAMPLES OF INTELLIGENT TUTORING SYSTEM IN EDUCATION

 

Algebra Tutor PAT (PUMP Algebra Tutor or Practical Algebra Tutor):

 

It is developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University, This ITS engages students in anchored learning problems and uses modern algebraic tools in order to engage students in problem solving and in sharing of their results.

 

 Mathematics Tutor

 

The Mathematics Tutor helps students solve word problems using fractions, decimals and percentages. The tutor records the success rates while a student is working on problems while providing subsequent, level-appropriate problems for the student to work on.

 

eTeacher

 

It is an intelligent agent that supports personalized e-learning assistance. It builds student profiles after detailed analysis of the student’s performance in online courses. eTeacher then uses the information from the student’s performance to suggest a personalized courses of action. The content is designed to assist their learning process.

 

REALP

 

REALP was designed to help students enhance their reading comprehension by providing reader-specific lexical practice.

 

It provides personalized practice with useful, authentic reading materials which are taken from the Web. The system automatically builds a user model according to student’s performance in the online courses. After reading, the student is given a series of exercises based on the target vocabulary found in reading.

 

CIRCSlM-Tutor

 

CIRCSIM-Tutor is an intelligent tutoring system that is used with first year medical students at the Illinois Institute of Technology. It uses natural dialogue based, Socratic language to help students learn about regulating blood pressure.

 

Why2-Atlas

 

Why2-Atlas is an ITS that analyses students’ explanations of physics principles. The students input their work in paragraph form and the program converts their words into a proof by making assumptions of student beliefs that are based on their explanations. In doing this, misconceptions and incomplete explanations are highlighted. The system then addresses these issues through a dialogue with the student and asks the student to correct their essay. A number of iterations may take place before the process is complete.

 

SmartTutor

 

The University of Hong Kong (HKU) developed a SmartTutor to support the needs of continuing education students. Personalized learning was identified as a key need within adult education at HKU and SmartTutor aims to fill that need. SmartTutor provides support for students by combining Internet technology, educational research and artificial intelligence.

 

AutoTutor

AutoTutor assists college students in learning about computer hardware, operating systems and the Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. AutoTutor attempts to understand learner’s input from the keyboard and then formulate dialog moves with feedback, prompts, correction and hints.

 

25.6 LET US SUM UP

 

Intelligent tutoring system is software containing an artificial intelligent component. It mimics the human tutor to generate instructional interactions in real time and on demand required by individual students. It aims to provide each student with a computer based tutor that has all qualities of a master teacher.

 

ITSs consists of four basic components i.e. the expert knowledge module, the student knowledge module, the tutoring module and user interface module.

 

ITSs reduces geographical barriers, improves tutoring. It can be used in a variety of ways and situations. ITS is a cost effective option. It motivates students; enhances learning speed and helps in greater achievement.

 

ITSs have few limitations and criticism against it. It does not suit for all subjects. It can use relevant teaching methods. Students may involve in gaming the system. The development of ITS is a lengthy process. Evaluation phase in ITS is a difficult task.

 

Algebra Tutor, Mathematics tutor, eTeacher, REALP, CIRCSIM-tutor, Why2-Atlas, Smart tutor and Auto tutor are some of the examples of Intelligent tutoring system.

 

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REFERENCES

  • Carbonell, J. R. (1970). AI in CAI: Artificial intelligence approach to computer assisted instruction.
  • IEEE Transactions on Man-Machine Systems 11(4): 190–202. Sleeman, D. H. & Brown,
  • J. S. (Eds.). (1982). Intelligent Tutoring Systems. New York: Academic Press. Wenger, E. (1987).
  • Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge . Los Altos, CA: Morgan Kaufmann.
  • Joseph Psotka, Sharon A. Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. ISBN 0-8058-0192-8.
  • Ford, L. A New Intelligent Tutoring System (2008) British Journal of Educational Technology, 39(2), 311-318
  • Bailin, A & Levin, L. Introduction: Intelligent Computer Assisted Language Instruction (1989) Computers and the Humanities, 23, 3-11