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Intelligent Tutoring Systems
Intelligent Tutoring Systems
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THE TOPICS
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DIRECTORY
Tell me and I forget.
Show me and I
remember.
Involve me and I
understand.
- Chinese proverb
Good Places to Start
Software Tutors Offer Help and Customized Hints. By Katie Hafner. The New
York Times (September 16, 2004; subscription req'd.). "As she sat at a
Recent
computer screen, she kept typing 2.8, an incorrect answer. Eventually a hint
News
about THE popped up: 'Think about the sign of your answer.' When Rochelle finally typed
the correct sum, -1.8, the computer showed its appreciation by allowing her to
TOPICS
(annotated) move on to a new problem. She smiled at her small triumph. Since January,
Middle School 301 in the Bronx, where Rochelle is an eighth grader, has been
using a software program called Cognitive Tutor to help students learn math.
The software, from Carnegie Learning, a six-year-old company that got its start
at Carnegie Mellon University, is designed to give students individualized
instruction when personal attention is scarce. Although such intelligent tutoring
systems have their share of skeptics, students at schools that use them have
not only improved their performance in math but now profess to enjoy a subject
they once loathed. ... Broadly defined, an intelligent tutoring system is
educational software containing an artificial intelligence 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. When Rochelle, for instance, displayed a weakness when working with
negative numbers, the program repeatedly asked her to solve similar
problems. ... The artificial intelligence built into the Carnegie Learning program
helps set it apart. Not only does the program present drills according to a
student's weaknesses, but it watches the work step by step, detecting where
the student stumbles, and chimes in when necessary."
High-tech tools help with FCAT - Students can access online tutors and test
aids that monitor individual progress. By Beth Kormanik. The Times-Union &
Jacksonville.com (September 20, 2004)." Effective personal tutors can raise
student scores by two grade levels, [Ken Koedinger, professor of humancomputer interaction and psychology at Carnegie Mellon University] said, but
the average human tutor helps raise grade level only by one-half. His
computer-based system falls in between, raising students' scores by one grade
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level."
Artificial Intelligence. By Kristen Kennedy. Technology & Learning (November
2002). "They don't do windows -- but the next generation of AI applications can
teach, tutor, and even grade essays." This is just one of the articles that is part
of this issue's cover story: Top 10 Smart Technologies for Schools.
Experts Use AI to Help GIs Learn Arabic. By Eric Mankin. USC News (June 21,
2004). " To teach soldiers basic Arabic quickly, USC computer scientists are
developing a system that merges artificial intelligence with computer game
techniques. The Rapid Tactical Language Training System, created by the
USC Viterbi School of Engineering's Center for Research in Technology for
Education (CARTE) and partners, tests soldier students with videogame
missions in animated virtual environments where, to pass, the students must
successfully phrase questions and understand answers in Arabic. ... 'Most
adults find it extremely difficult to acquire even a rudimentary knowledge of a
language, particularly in a short time,' said CARTE director W. Lewis Johnson.
'We?re trying to build an improved model of instruction, one that can be closely
tailored to both the needs and the abilities of each individual student,' Johnson
said." Read the story and then watch the video!
Artificial intelligence alive and well. The University of Auckland News (January
19, 2005). "While statistics students at The University of Auckland are taking a
break from studies for summer, their new 'teacher' can?t wait for the new
semester to begin. Maria, an assistant teacher in Statistical Interference, is an
unusual individual. She looks to be in her mid-twenties but her age, she says,
cannot be computed in human years. With a vocabulary of 203,000 words, a
repertoire of 106,000 grammatical rules and 118,000 rules of logical inference,
Maria is capable of conversation at quite a complex level. Maria is a robot, or
artificial intelligence entity, created over two years of intense work and study by
Shahin Maghsoudi, a PhD student and member of the Artificial Intelligence
Group in the Faculty of Science. As part of his Masters degree in Computer
Science, Shahin embarked on a project to create virtual robots which could be
used as teaching assistants, helpdesk operators and web-based marketing
assistants."
Intelligent Tutoring Systems. A brief introduction by Eric Thomas. Part of San
Diego State University's Encyclopedia of Educational Technology.
Applications of AI in Education. By Joseph Beck, Mia Stern, and Erik
Haugsjaa. ACM Crossroads (student magazine of the Association for
Computing Machinery), 1996. "In this paper, we start by providing an overview
of the main components of intelligent tutoring systems. We then provide a brief
summary of different types of ITSs. Next, we present a detailed discussion of
two components, the student model and the pedagogical module. We close by
discussing some of the open questions in ITS as well as future directions of the
field."
The Roles of Artificial Intelligence in Education: Current Progress and Future
Prospects. David McArthur, Matthew Lewis, and Miriam Bishay. (1993) RAND
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DRU-472-NSF. A very good overview with lots of basic information about
intelligent tutoring systems.
Intelligent Tutoring Systems: The What and the How. By Jim Ong and Sowmya
Ramachandran. From the February 2000 edition of Learning Circuits, a
Webzine About E-Learning from the American Society for Training &
Development (ASTD). "Imagine that each learner in a classroom or WBT
setting has a personal training assistant who pays attention to the participant's
learning needs, assesses and diagnoses problems, and provides assistance
as needed. ... Providing a personal training assistant for each learner is
beyond the training budgets of most organizations. However, a virtual training
assistant that captures the subject matter and teaching expertise of
experienced trainers provides a captivating new option. The concept, known as
intelligent tutoring systems (ITS) or intelligent computer-aided instruction
(ICAI), has been pursued for more than three decades by researchers in
education, psychology, and artificial intelligence."
A teacher who gets by on artificial intelligence" (International Herald Tribune
and Israeli Haaretz Daily, 12/20/98) and "Intelligent agents help humans learn
from computers" (CNN Interactive, 8/25/97) are just two of the exciting articles
about Pedagogical Agents and Guidebots that you'll find at CARTE's very
informative site. [CARTE = The Center for Advanced Research in Technology
for Education which is part of the Information Sciences Institute at the
University of Southern California.] Be sure that you don't miss the demos and
videos, or their many pedagogical agents and guidebots (see: research and
projects).
Readings Online
Automated Essay Evaluation: The Criterion Online Writing Service. By Jill
Burstein, Martin Chodorow, and Claudia Leacock. AI Magazine 25(3): Fall
2004, 27-36. "In this article, we describe a deployed educational technology
application: the Criterion Online Essay Evaluation Service, a web-based
system that provides automated scoring and evaluation of student essays.
Criterion has two complementary applications: (1) CritiqueWriting Analysis
Tools, a suite of programs that detect errors in grammar, usage, and
mechanics, that identify discourse elements in the essay, and that recognize
potentially undesirable elements of style, and (2) e-rater version 2.0, an
automated essay scoring system. Critique and e-rater provide students with
feedback that is specific to their writing in order to help them improve their
writing skills and is intended to be used under the instruction of a classroom
teacher. Both applications employ natural language processing and machine
learning techniques. All of these capabilities outperform baseline algorithms,
and some of the tools agree with human judges in their evaluations as often as
two judges agree with each other."
Artificial Intelligence as Tutor. By Josh Chamot. National Science Foundation
Office of Legislative and Public Affairs News Tip (May 6, 2002). "Inspired by
the methods of his rural Kentucky high-school chemistry teacher, an NSFsupported researcher has developed an artificial intelligence tutoring software
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that helps students confront complex science questions. In 1998, chemist
Benny Johnson founded Quantum Simulations, Inc. with high school mentor
Dale Holder and colleague Rebecca Renshaw to create highly interactive
tutoring software for the sciences. Many pre-existing tutoring programs store
information in a database and do not allow for student input beyond multiple
choice answers or simple responses. In contrast, the Quantum Tutors
'converse' with students, said Johnson, 'providing real-time feedback . . .
Tutors respond to student questions, give hints, show correct next steps and
even explain why a step is correct or incorrect using scientific principles.'"
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Visit the Quantum Simulations, Inc.web site.
The Love Machine - Building computers that care. By David Diamond. Wired
Magazine (December 2003). "Intelligent tutoring systems are not new, but they
are limited; unlike flesh-and-blood tutors, they can't tell if you're bored,
frustrated, engrossed, or angry and then adjust the teaching accordingly.
That's why MIT has been working to add such capability to two systems. One,
an automated reading tutor, was developed by Jack Mostow, a Carnegie
Mellon computer science professor. The system, which is helping hundreds of
students learn to read, was used in a recent study proving the positive effects
of praise and encouragement. The other, AutoTutor, was built by University of
Memphis professor Arthur Graesser and his Tutoring Research Group and is
used by U of M students. Designed to observe and respond to a student's
cognitive state, AutoTutor relies on Latent Semantic Analysis, a natural
language parser that analyzes the sentences you type in and figures out how
much you know by contrasting your semantics against an internal model of an
ideal student. A clever animated avatar spits back information to fill in the gaps
in your understanding."
Talking Up a Good Game - Computer Simulation to Stimulate Soldiers to
Speak in Tongues. By Paul Eng. ABCNEWS.com (March 9, 2004). "The first
part of the game, says [Lewis] Johnson, acts as basically an 'intelligent
tutoring' program.' ... But what makes the program really 'intelligent' are the
computer-generated and -controlled characters, such as a virtual village leader
and a virtual 'team member' that acts as an in-game guide. These game
characters are programmed to react in ways that are unique to each individual
user."
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visit the Tactical Language Project at CARTE
Encouraging Student Reflection and Articulation using a Learning Companion.
By Bradley Goodman, Amy Soller, Frank Linton, and Robert Gaimari (1998).
International Journal of Artificial Intelligence in Education, 9(3-4). "The goal of
the research presented in this paper is to promote more effective instructional
exchanges between a student and an intelligent tutoring system The approach
taken to meet this goal involves providing a simulated peer as a partner for the
student in learning and problem solving. The learning companion described in
this paper enhances learning by initiating a dialogue with a student forcing
reflection and articulation on the student's learning."
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Intelligent Tutoring Systems with Conversational Dialogue. By Arthur C.
Graesser, Kurt VanLehn, Carolyn P. Rose, Pamela W. Jordan, and Derek
Harter (2001). AI Magazine 22(4): 39-52. "We have been working on a new
generation of intelligent tutoring systems that hold mixed-initiative
conversational dialogues with the learner. The tutoring systems present
challenging problems and questions to the learner, the learner types in
answers in English, and there is a lengthy multiturn dialogue as complete
solutions or answers evolve. This article presents the tutoring systems that we
have been developing. AutoTutor is a conversational agent, with a talking
head, that helps college students learn about computer literacy. andes, atlas,
and why2 help adults learn about physics. Instead of being mere informationdelivery systems, our systems help students actively construct knowledge
through conversations."
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visit the AutoTutor web site
Using technology for learning & teaching science. IST Results (November 3,
2004). "Researchers are demonstrating how technologies when applied to
science learning can help motivate and engage pupils and promote better takeup of scientific disciplines at school and university. The following eight IST
research projects are focusing on technology-enhanced learning methods, in
subjects as varied as astronomy, space research, physics, mathematics and
the earth sciences. ... A learner-centred approach is the tack taken by the
LeActiveMath project. It aims to design a third generation intelligent learning
environment to support Web-based active learning in maths, adapted to the
needs and context of the learner by offering interactivity and personalisation.
This 36-month project that started in January 2004 builds on its successful
forerunner, ActiveMath. Learner feedback from this earlier project revealed that
'students like a lot of interactivity in exercises and benefit from it,' says Erica
Melis, the coordinator of LeActiveMath at the German Research Center for
Artificial Intelligence. While ActiveMath had some of these features,
LeActiveMath will offer much more. ... It will provide intelligent feedback and
involve the student in tutorial dialogues that stimulate the student to think
rather than learn by heart. 'Dialogues are a natural way to communicate and
human-centred dialogues are known to improve learning,' says Melis."
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Visit the LeActiveMath web site.
Pitch-perfect PC - Software that turns a computer into a smart, sensitive
practice partner for music students. By Alex Markels. U.S. News & World
Report (March 17, 2003).
The F-16 Maintenance Skills Tutor. By Christopher Marsh. The Edge - The
MITRE Advanced Technology Newsletter (March 1999). "How do you keep
technicians trained to repair systems that are highly reliable? ... With the
downsizing of the Air Force, there are fewer technicians per aircraft and many
of the experienced technicians are retiring leaving fewer people to train
novices. In response to this need, research was performed in two areas:
cognitive task analysis techniques to capture troubleshooting strategies used
by experts and novices, and intelligent tutoring systems that take the results of
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the cognitive task analysis to provide a practice environment for working
authentic troubleshooting problems while coaching the student with hints and
feedback. The result of this research is the F-16 Maintenance Skills Tutor.
Using this type of tutor for 20 hours is equivalent to 3.5 to 4 years of
experience on the flight line."
City pushes computer tutor for struggling algebra students. By Maggi
Newhouse. Tribune-Review (March 8, 2004)/ available from
PittsburghLIVE.com. "About 40 percent of the city's ninth graders fail first-year
algebra every year, and Pittsburgh Public Schools officials say it's time to
expand an innovative math program used by some schools to the rest of the
district. ... The centerpiece of the Carnegie Learning method, developed by
Carnegie Mellon University researchers, is a computer program that combines
traditional algebra problems with technology that can assess a student's
progress and skill level. The Cognitive Tutor program can then use the student
information to offer individualized instruction and provide instant feedback for a
student and teacher. 'What you're seeing here is artificial intelligence,' said
Jackie Smith, an instructional support director for mathematics. 'The computer
is learning and building a profile of every single student as it diagnoses their
strengths and weaknesses.'"
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visit the Cognitive Tutor site
How to Fix America's Schools. By William C. Symonds. BusinessWeek
Magazine. (March 19, 2001) "A new generation of software is proving far more
effective than traditional programs, which are often little more than rote
learning dressed up for the Digital Age. Take the Cognitive Tutor, developed by
Carnegie Learning in Pittsburgh to teach algebra and geometry. The program
uses artificial intelligence to determine what students understand and what
they need to tackle next. Rather than drill kids on equations, it requires them to
use algebra to solve real problems. Kids using Cognitive Tutor score higher on
math tests than students in traditional algebra classes and are more than twice
as likely to complete geometry and higher algebra."
Related Web Sites
"Adaptive Training Systems (ATSs), SHAI's proprietary versions of intelligent
tutoring systems (ITSs), can dramatically reduce the cost of education while
offering many of the benefits of one-to-one instruction. ATS's dynamically and
adaptively monitor individual students in learning specific principles as they
perform exercises. ATSs use their monitoring capability to ensure each student
is always presented with material that is not so easy that he is bored, or
material that is above his ability to learn and frustrates him. This monitoring
also enables our systems to detect misconceptions and provide each student
with individualized instruction."
"ARIES, the Laboratory for Advanced Research in Intelligent Educational
Systems [at the University of Saskatchewan], is a focal point for research
projects in the areas of intelligent tutoring systems and adaptive learning
environments. The mission of the ARIES Laboratory is to advance the
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development of Learning Technologies through the integration of Artificial
Intelligence techniques and to advance Artificial Intelligence research through
attempts to solve real-world education and training problems." Be sure to
check out their many projects, both present and past.
"AutoTutor is an intelligent tutoring system developed by an interdisciplinary
research team.This team is currently being funded by the Office of Naval
Research and the National Science Foundation and is is comprised of
approximately 35 researchers from psychology, computer science, linguistics,
physics, engineering, and education. ... AutoTutor works by having a
conversation with the learner. AutoTutor appears as an animated agent that
acts as a dialog partner with the learner. The animated agent delivers
AutoTutor's dialog moves with synthesized speech, intonation, facial
expressions, and gestures. Students are encouraged to articulate lengthy
answers that exhibit deep reasoning, rather than to recite small bits of shallow
knowledge."
CIRCLE: Center for Interdisciplinary Research on Constructive Learning
Environments. "CIRCLE is an NSF-funded research center located at the
University of Pittsburgh and Carnegie Mellon University, with multiple
partnerships among schools, industries and other research institutions.
CIRCLE's mission is to determine why highly effective forms of instruction,
such as human one-on-one tutoring, work so well, and to develop computerbased constructive learning environments that foster equally impressive
learning." Be sure to follow the links to "Projects" for that's where you'll find
systems such as:
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Andes: An Intelligent Tutoring System for Classical Physics
ITSPOKE: Spoken Dialogue for Intelligent Tutoring Systems
The EPSILON [Encouraging Positive Social Interaction while Learning ONLine] Project at the Learning Research and Development Center, University of
Pittsburgh "is an interdisciplinary effort to provide dynamic, adaptive support for
on-line learning communities. The support, in the form of an intelligent software
agent, will focus on helping students improve their social and communication
management skills. ... The EPSILON software will be driven by a
computational model of effective learning interaction. The project will explore
methods for dynamically analyzing on-line interaction during structured
learning activities. Artificial Intelligence techniques will be employed for
analyzing, studying, and characterizing on-line interaction."
"The ICICLE system (Interactive Computer Identification and Correction of
Language Errors) is an intelligent tutoring system under development in the
NLP/AI Group of the CIS Department of the University of Delaware. The
primary goal of ICICLE is to employ natural language processing and
generation to tutor deaf students on their written English."
Journals and Conferences related to ITS ... and workshops too! Compiled by
the Tutor Research Group at Worcester Polytechnic Institute.
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Journals on Intelligent Tutoring Systems & Textbooks on Intelligent Tutoring
Systems. Compiled by Noboru Matsuda, Postdoctoral Research Fellow at the
Human Computer Interaction Institute, School of Computer Science, Carnegie
Mellon University.
METUTOR: A means-end tutoring system. From Prof. Neil C. Rowe,
Department of Computer Science, U.S. Naval Postgraduate School.
"METUTOR is a tutoring environment for teaching of procedural skills. It uses
planning methods from artificial intelligence to infer what a student is doing,
and tutors intelligently when the students diverges from the correct plan. ... The
teacher's job is further simplified through with the use of the MEBUILD expertsystem shell under development, which uses an object-oriented world
description to infer most of the necessary action specification for METUTOR.
MEBUILD also allows a teacher to reuse parts of one lesson in another."
Project Listen: A Reading Tutor That Listens. By Jack Mostov, project director.
"The Reading Tutor adapts Carnegie Mellon's state-of-the art Sphinx-II speech
recognizer to analyze the student's oral reading. The Reading Tutor intervenes
when the reader asks for help, makes mistakes, gets stuck, or is likely to
encounter difficulty. The Reading Tutor responds with assistance modelled in
part after expert reading teachers, but adapted to the capabilities and
limitations of the technology. A successful computer reading tutor that uses
speech recognition to listen to children read. The computer tutor intervenes
when students ask for help or make mistakes. Links to a bibliography that
includes abstracts of articles and some full text."
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Project LISTEN was selected as one of the National Science
Foundation's nifty50: "The nifty50 are NSF-funded inventions, innovations
and discoveries that have become commonplace in our lives. This
interactive section of the Web site allows visitors to click on each
innovation and explore it in greater depth." After clicking here, click on
nifty50 and then go to number 40.
Virtual Environments for Training (VET). Information Sciences Institute,
University of Southern California. Description of a project in intelligent tutoring,
with access to many online papers.
Related Pages
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Assistive Technologies
Education
General Index to AI in the news - Education
Interfaces
More Readings
Friedland, Noah S. and Paul G. Allen, Gavin Matthews, Michael Witbrock,
David Baxter, Jon Curtis, Blake Shepard, Pierluigi Miraglia, Jürgen Angele,
Steffen Staab, Eddie Moench, Henrik Oppermann, Dirk Wenke, David Israel,
Vinay Chaudhri, Bruce Porter, Ken Barker, James Fan, Shaw Yi Chaw, Peter
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Yeh, Dan Tecuci, Peter Clark. 2004. Project Halo: Towards a Digital Aristotle.
AI Magazine 25(4): 29-47. Abstract: "Project Halo is a multistaged effort,
sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that
will encompass much of the world?s scientific knowledge and be capable of
applying sophisticated problem solving to answer novel questions. Vulcan
envisions two primary roles for Digital Aristotle: as a tutor to instruct students in
the sciences and as an interdisciplinary research assistant to help scientists in
their work. As a first step towards this goal, we have just completed a sixmonth pilot phase designed to assess the state of the art in applied knowledge
representation and reasoning (KR&/R). Vulcan selected three teams, each of
which was to formally represent 70 pages from the advanced placement (AP)
chemistry syllabus and deliver knowledge-based systems capable of
answering questions on that syllabus. The evaluation quantified each system?s
coverage of the syllabus in terms of its ability to answer novel, previously
unseen questions and to provide human- readable answer justifications. These
justifications will play a critical role in building user trust in the questionanswering capabilities of Digital Aristotle. Prior to the final evaluation, a 'failure
taxonomy' was collaboratively developed in an attempt to standardize failure
analysis and to facilitate cross-platform comparisons. Despite differences in
approach, all three systems did very well on the challenge, achieving
performance comparable to the human median. The analysis also provided key
insights into how the approaches might be scaled, while at the same time
suggesting how the cost of producing such systems might be reduced. This
outcome leaves us highly optimistic that the technical challenges facing this
effort in the years to come can be identified and overcome. This article
presents the motivation and longterm goals of Project Halo, describes in detail
the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge,
empirical evaluation, results, and failure analysis. The pilot?s outcome is used
to define challenges for the next phase of the project and beyond."
Graesser, Arthur C. and Kurt VanLehn, Carolyn P. Rose, Pamela W. Jordan,
and Derek Harter. 2001. Intelligent Tutoring Systems with Conversational
Dialogue. AI Magazine 22(4): 39-52. "Many of the intelligent tutoring systems
that have been developed during the last 20 years have proven to be quite
successful, particularly in the domains of mathematics, science, and
technology. They produce significant learning gains beyond classroom
environments. They are capable of engaging most students' attention and
interest for hours. We have been working on a new generation of intelligent
tutoring systems that hold mixed-initiative conversational dialogues with the
learner. The tutoring systems present challenging problems and questions to
the learner, the learner types in answers in English, and there is a lengthy
multiturn dialogue as complete solutions or answers evolve. This article
presents the tutoring systems that we have been developing. AutoTutor is a
conversational agent, with a talking head, that helps college students learn
about computer literacy. andes, atlas, and why2 help adults learn about
physics. Instead of being mere information-delivery systems, our systems help
students actively construct knowledge through conversations."
Murray, Tom. 1999. Authoring Intelligent Tutoring Systems: An analysis of the
state of the art. International Journal of Artificial Intelligence in Education
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(1999), 10: 98-129. "This paper consists of an in-depth summary and analysis
of the research and development state of the art for intelligent tutoring system
(ITS) authoring systems. A seven-part categorization of two dozen authoring
systems is given, followed by a characterization of the authoring tools and the
types of ITSs that are built for each category. An overview of the knowledge
acquisition and authoring techniques used in these systems is given. A
characterization of the design tradeoffs involved in building an ITS authoring
system is given. Next the pragmatic questions of real use, productivity findings,
and evaluation are discussed. Finally, I summarize the major unknowns and
bottlenecks to having widespread use of ITS authoring tools."
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