Intelligent tutoring system

An intelligent tutoring system (ITS) is any computer system that provides direct customized instruction or feedback to students, i.e. without the intervention of human beings, whilst performing a task. Thus, ITS implements the theory of learning by doing. An ITS may employ a range of different technologies. However, usually such systems are more narrowly conceived of as artificial intelligence systems, more specifically expert systems made to simulate aspects of a human tutor. Intelligent Tutor Systems have been around since the late 1970s, but increased in popularity in the 1990s.

The structure of an ITS system

Intelligent tutoring systems consist of four different subsystems or modules: the interface module, the expert module, the student module, and the tutor module. The interface module provides the means for the student to interact with the ITS, usually through a graphical user interface and sometimes through a rich simulation of the task domain the student is learning (e.g., controlling a power plant or performing a medical operation). The expert module references an expert or domain model containing 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. An example would be the kind of diagnostic and subsequent corrective actions an expert technician takes when confronted with a malfunctioning thermostat. The student module uses a student model containing descriptions of student knowledge or behaviors, including his misconceptions and knowledge gaps. An apprentice technician might, for instance, believe a thermostat also signals too high temperatures to a furnace (misconception) or might not know about thermostats that also gauge the outdoor temperature (knowledge gap). A mismatch between a student's behavior or knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction. To be able to do this, it needs information about what a human tutor in such situations would do: the tutor model.

An intelligent tutoring system is only as effective as the various models it relies on to adequately model expert, student and tutor knowledge and behavior. Thus, building an ITS needs careful preparation in terms of describing the knowledge and possible behaviors of experts, students and tutors. This description needs to be done in a formal language in order that the ITS may process the information and draw inferences in order to generate feedback or instruction. Therefore a mere description is not enough; the knowledge contained in the models should be organized and linked to an inference engine. It is through the latter's interaction with the descriptive data that tutorial feedback is generated.

Use in practice

All this is a substantial amount of work, even if authoring tools have become available to ease the task. This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, Cognitive Tutors, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved student learning outcomes on final exams and standardized tests. Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc. Intelligent Language Tutoring Systems (ILTS), e.g. this one, teach natural language to first or second language learners. ILTS requires specialized natural language processing tools such as large dictionaries and morphological and grammatical analyzers with acceptable coverage.

Applications

During the rapid expansion of the web boom, new computer-aided instruction paradigms, such as e-learning and distributed learning, provided an excellent platform for ITS ideas. Areas that have used ITS include natural language, machine learning, planning, multi-agent systems, ontologies, semantic Web, and social and emotional computing. Besides these other technologies such as multimedia, object-oriented systems, modeling, simulation and statistics have also been applied or combined with ITS. Non-technological areas like education sciences and psychology are also attracted by the success of ITS (Ramos, Ramos, Frasson & Ramachandran, 2009).

In recent years, ITS has moved away from the theoretical (research labs) and into a wide range of practical application. ITS have expanded across many critical and complex cognitive domains, and the results have been dramatic and far reaching. ITS systems have cemented a place within formal education and has found a home in the sphere of corporate training and organizational learning. ITS has several affordance, such as individualized learning, just in time feedback and flexibility in time and space.

While Intelligent tutoring systems were first introduced within the corporate world (need a reference), there are now many applications in the educational arena as well. Intelligent tutoring systems can be found in online environments or in a traditional classroom computer lab. and are used in K-12 classrooms as well as at the university level. There are a number of programs that target mathematics but applications can be found in health sciences, language acquisition, and other areas of formalized learning.

Reports of improvement in student comprehension, engagement, attitude, motivation and academic results have all contributed to the ongoing interest in the investment in and research of theses systems. The personalized nature of the intelligent tutoring systems affords educators an opportunity to create individualized programs. Within education there are a plethora of intelligent tutoring systems, an exhaustive list does not exist but highlighted below are several of the more influential programs.

Examples of Intelligent Tutoring Systems in Education

Algebra Tutor PAT (PUMP Algebra Tutor or Practical Algebra Tutor) developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University, engages students in anchored learning problems and uses modern algebraic tools activate students to problem solve and share results. The aim of PAT is to able to tap into a students prior knowledge and their day to day experiences with mathematics in order to promote growth. The success of PAT is well documented (exp. Miami-Dade County Public Schools Office of Evaluation and Research) from both a statistical (student results) and emotional (student and instructor feedback) perspective.

Carnegie Learning Evaluation of the Cognitive Tutor Algebra I Program A Shneyderman - Miami–Dade County Public Schools, Office of Evaluation and Research, Miami Fl. September 2001

Mathematics Tutor The Mathematics Tutor (Beal, Beck & Woolf, 1998) helps students solve word problems using fractions, decimals and percentages.The tutor records the success rates when student is working on the problems and subsequent problems that are predicted to fit student’s level will be selected and an estimated desirable time will be given to the student to solve the problem.

eTeacher eTeacher (Schiaffino et al., 2008) is an intelligent agent that supports personalized e-learning assistance. It builds student’s profile while observing student performing in online courses. Then eTeacher uses the information from student’s profile to suggest their personalized courses of action that assist their learning process.

ZOSMAT “This system was developed to be able to respond almost every needs of a real classroom. ZOSMAT can be used for the purpose of either individual learning or real classroom environment with the guidance of a human tutor during a formal education process. This characteristic of ZOSMAT distinguishes itself from other intelligent tutoring systems. ZOSMAT follows a student in each stage of the learning process and guides him/her about what he/she will need to do. ZOSMAT with a web-based feature presents an equal opportunity of education for both the student in the classroom and the student in the far end of the world. This system is a student-centered one and the progress in student’s learning process depends on his/her effort (Keles et al. 2009). “

REALP REALP was designed to help students enhance reading comprehension by providing reader-specific lexical practice and offering personalized practice in useful, authentic reading materials gathered from the Web. The system automatically build a user model according to student’s performance. After reading, the student will be given a series of exercises based on the target vocabulary found in reading (Heil- man et al. 2006).

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. http://www.cs.iit.edu/~circsim/

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 them into a proof by making assumptions of student beliefs based on their explanations. In doing, 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. aroque.bol.ucla.edu/pubs/vanLehnEtAl-its02-architectureWhy.pdf

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 student by combining Internet technology, educational research and artificial intelligence.

AutoTutor Auto Tutor assists college students in learning hardware, operating systems and Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. Auto Tutor attempts to understand learner’s input from keyboard and formulate dialog moves with feedback, prompts, correction and hints.(Graesser et al. 1999).

ActiveMath ActiveMath is a web-based, adaptive learning environment for mathematics. These systems strive for improving long-distance learning, for complementing traditional classroom teaching, and for supporting individual and life-long learning.

Examples of Intelligent Tutoring Systems in Corporate Training and Organizational Learning

SHERLOCK “SHERLOCK” is used to train Air Force technicians to diagnose problems in the electrical systems of F-15 jets. The ITS creates faulty schematic diagrams of systems for the trainee to locate and diagnose. The ITS provides diagnostic readings allowing the trainee to decide whether the fault lies in the circuit being tested or if it lies elsewhere in the system. Feedback and guidance are provided by the system and help is available if requested. Lajoie and Lesgold’s (1989)

Cardiac Tutor The Cardiac Tutor aim is support advanced cardiac support techniques to medical personnel. The tutor presents cardiac problems and using a variety of steps students must select various interventions. Cardiac Tutor provides clues, verbal advice and and feedback in order to personalize and optimize the learning. Each simulation, regardless of whether the students were successful able to help their patients, results in a detailed report which students review.

Ramos, C., Ramos, C., Frasson, C., & Ramachandran, S. (2009). Introduction to the special issue on real world applications of intelligent tutoring systems. , 2(2) 62-63. Stankov, S., Glavinic, V., & Rosic, M. (2011). Intelligent tutoring systems in e-learning environments: Design, implementation, and evaluation. Hershey: Information Science

ITS conference

The Intelligent Tutoring Systems conference was typically held every other year in Montréal (Canada) by Claude Frasson and Gilles Gauthier in 1988, 1992, 1996 and 2000; in San Antonio (US) by Carol Redfield and Valerie Shute in 1998; in Biarritz (France) and San Sebastian (Spain) by Guy Gouardères and Stefano Cerri in 2002; in Maceio (Brazil) by Rosa Maria Vicari and Fábio Paraguaçu in 2004; in Jhongli (Taiwan) by Tak-Wai Chan in 2006. The conference was recently back in Montreal in 2008 (for its 20th anniversary) by Roger Nkambou and Susanne Lajoie. ITS'2010 was held in Pittsburgh (US) by Jack Mostow, Judy Kay, and Vincent Aleven. The International Artificial Intelligence in Education (AIED) Society (http://iaied.org) publishes The International Journal of Artificial Intelligence in Education (IJAIED) and produces the International Conference on Artificial Intelligence in Education every odd numbered year. The American Association of Artificial Intelligence (AAAI)(www.aaai.org) sometimes has symposia and papers related to intelligent tutoring systems. A number of books have been written on ITS including three published by Lawrence Erlbaum Associates.

See also

• Educational technology
• Instructional technology
• Learning objects
• Serious games
• Educational data mining

Bibliography

Books

• Nkambou, Roger; Bourdeau, Jacqueline; Mizoguchi, Riichiro, eds. (2010). Advances in Intelligent Tutoring Systems. Springer. ISBN 3-642-14362-8.
• Woolf, Beverly Park (2009). Building Intelligent Interactive Tutors. Morgan Kaufmann. ISBN 978-0-12-373594-2.
• Evens, Martha; Michael, Joel (2005). One-on-one Tutoring by Humans and Computers. Routledge. ISBN 978-0-8058-4360-6.
• Polson, Martha C.; Richardson, J. Jeffrey, eds. (1988). Foundations of Intelligent Tutoring Systems. Lawrence Erlbaum. ISBN 0-8058-0053-0.
• Psotka, Joseph; Massey, L. Dan; Mutter, Sharon, eds. (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum. ISBN 0-8058-0023-9.
• Wenger, Etienne (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann. ISBN 0-934613-26-5.
• Brown, D.; Sleeman, John Seely, eds. (1982). Intelligent Tutoring Systems. Academic Press. ISBN 0-12-648680-8.

Papers

• Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology
• Intelligent Tutoring Systems: The What and the How
• Freedman, Reva; Ali, Syed S.; McRoy, Susan (2000). "What is an Intelligent Tutoring System?" (PDF). Intelligence. 11 (3): 15–16. doi:10.1145/350752.350756.
• Intelligent Tutoring Systems: Using AI to Improve Training Performance and ROI
• A Framework for Model-Based Adaptive Training

External links

• Intelligent Tutoring Systems in the AI topics website of the Association for the Advancement of Artificial Intelligence
• The 10th International Conference on Intelligent Tutoring Systems - Bridges to Learning - Pittsburgh (2010)
• The 9th International Conference on Intelligent Tutoring Systems - Intelligent Tutoring Systems: Past and Future - Montreal (2008)
• The 8th International Conference on Intelligent Tutoring Systems (2006)
• The 2007 Artificial Intelligence in Education conference.
• MERLOT - Multimedia Educational Resource for Learning and Online Teaching
• The Wolfram Demonstrations Project site