Intelligent tutoring systems are
artificial intelligence-based instructional systems that adapt instruction
based on a variety of learner variables, such as dynamic measures of students'
ongoing knowledge growth, personal interest, motivation to learn, affective
states, and aspects of how they self-regulate their learning.
For a variety of problem areas, such as STEM, computer
programming, language, and culture, intelligent tutoring systems have been
created.
Complex problem-solving activities, collaborative learning
activities, inquiry learning or other open-ended learning activities, learning
through conversations, game-based learning, and working with simulations or
virtual reality environments are among the many types of instructional
activities they support.
Intelligent tutoring systems arose from a field of study
known as AI in Education (AIED).
MATHia® (previously Cognitive Tutor), SQL-Tutor, ALEKS, and
Rea soning Mind's Genie system are among the commercially successful and widely
used intelligent tutoring systems.
Intelligent tutoring systems are frequently more successful
than conventional kinds of training, according to six comprehensive
meta-analyses.
This efficiency might be due to a number of things.
First, intelligent tutoring systems give adaptive help
inside issues, allowing classroom instructors to scale one-on-one tutoring
beyond what they could do without it.
Second, they allow adaptive problem selection based on the
understanding of particular pupils.
Third, cognitive task analysis, cognitive theory, and
learning sciences ideas are often used in intelligent tutoring systems.
Fourth, the employment of intelligent tutoring tools in
so-called blended classrooms may result in favorable cultural adjustments by
allowing teachers to spend more time working one-on-one with pupils.
Fifth, more sophisticated tutoring systems are repeatedly
developed using new approaches from the area of educational data mining, based
on data.
Finally, Open Learner Models (OLMs), which are visual
representations of the system's internal student model, are often used in
intelligent tutoring systems.
OLMs have the potential to assist learners in productively
reflecting on their current level of learning.
Model-tracing tutors, constraint-based tutors,
example-tracing tutors, and ASSISTments are some of the most common intelligent
tutoring system paradigms.
These paradigms vary in how they are created, as well as in
tutoring behaviors and underlying representations of domain knowledge, student
knowledge, and pedagogical knowledge.
For domain reasoning (e.g., producing future steps in a
problem given a student's partial answer), assessing student solutions and
partial solutions, and student modeling, intelligent tutoring systems use a
number of AI approaches (i.e., dynamically estimating and maintaining a range
of learner vari ables).
To increase systems' student modeling skills, a range of
data mining approaches (including Bayesian models, hidden Markov models, and
logistic regression models) are increasingly being applied.
Machine learning approaches, such as reinforcement learning,
are utilized to build instructional policies to a lesser extent.
Researchers are looking at concepts for the smart classroom
of the future that go beyond the capabilities of present intelligent tutoring
technologies.
AI systems, in their visions, typically collaborate with
instructors and students to provide excellent learning experiences for all
pupils.
Recent research suggests that rather than designing
intelligent tutoring systems to handle all aspects of adaptation, such as
providing teachers with real-time analytics from an intelligent tutoring system
to draw their attention to learners who may need additional support, promising
approaches that adaptively share regulation of learning processes across
students, teachers, and AI systems—rather than designing intelligent tutoring
systems to handle all aspects of adaptation, for example—by providing teachers
with real-time analytics from an intelligent tutoring system to draw their
attention to learners who may need additional support.
You may also want to read more about Artificial Intelligence here.
See also:
Natural Language Processing and Speech Understanding; Workplace Automation.
Further Reading:
Aleven, Vincent, Bruce M. McLaren, Jonathan Sewall, Martin van Velsen, Octav Popescu, Sandra Demi, Michael Ringenberg, and Kenneth R. Koedinger. 2016. “Example-Tracing Tutors: Intelligent Tutor Development for Non-Programmers.” International Journal of Artificial Intelligence in Education 26, no. 1 (March): 224–69.
Aleven, Vincent, Elizabeth A. McLaughlin, R. Amos Glenn, and Kenneth R. Koedinger. 2017. “Instruction Based on Adaptive Learning Technologies.” In Handbook of Research on Learning and Instruction, Second edition, edited by Richard E. Mayer and Patricia Alexander, 522–60. New York: Routledge.
du Boulay, Benedict. 2016. “Recent Meta-Reviews and Meta-Analyses of AIED Systems.” International Journal of Artificial Intelligence in Education 26, no. 1: 536–37.
du Boulay, Benedict. 2019. “Escape from the Skinner Box: The Case for Contemporary Intelligent Learning Environments.” British Journal of Educational Technology, 50, no. 6: 2902–19.
Heffernan, Neil T., and Cristina Lindquist Heffernan. 2014. “The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching.” International Journal of Artificial Intelligence in Education 24, no. 4: 470–97.
Koedinger, Kenneth R., and Albert T. Corbett. 2006. “Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom.” In The Cambridge Handbook of the Learning Sciences, edited by Robert K. Sawyer, 61–78. New York: Cambridge University Press.
Mitrovic, Antonija. 2012. “Fifteen Years of Constraint-Based Tutors: What We Have Achieved and Where We Are Going.” User Modeling and User-Adapted Interaction 22, no. 1–2: 39–72.
Nye, Benjamin D., Arthur C. Graesser, and Xiangen Hu. 2014. “AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.” International Journal of Artificial Intelligence in Education 24, no. 4: 427–69.
Pane, John F., Beth Ann Griffin, Daniel F. McCaffrey, and Rita Karam. 2014. “Effectiveness of Cognitive Tutor Algebra I at Scale.” Educational Evaluation and Policy Analysis 36, no. 2: 127–44.
Schofield, Janet W., Rebecca Eurich-Fulcer, and Chen L. Britt. 1994. “Teachers, Computer Tutors, and Teaching: The Artificially Intelligent Tutor as an Agent for Classroom Change.” American Educational Research Journal 31, no. 3: 579–607.
VanLehn, Kurt. 2016. “Regulative Loops, Step Loops, and Task Loops.” International Journal of Artificial Intelligence in Education 26, no. 1: 107–12.
