Intelligent Tutoring Systems: Using Al to improve Training Performance and ROI
Intelligent Tutoring Systems: By James Ong and Sowmya Ramachandran
Intelligent Tutoring Systems:“…The sad truth is that most training methods and technologies generally produce, at best, “trained novices.” That is, they introduce facts and concepts to students, present them with relatively simple questions to test this new knowledge, and provide them with a few opportunities to practice using this knowledge in exercises or scenarios. However, becoming truly proficient requires extensive practice solving realistically-complex problems in a wide range situations, combined with coaching and feedback from managers, more experienced peers, or other types of experts…”
“…How intelligent tutoring systems (ITS) work:
intelligent Tutoring Systems: Many traditional instructional methods present learners with facts and concepts followed by test questions. These methods are effective in exposing people to large amounts of information and testing their recall. However, they often instill “inert knowledge” that learners can recall but may not apply correctly when needed. By contrast, ITS systems use simulations and other highly interactive learning environments that require people to apply their knowledge and skills. These active, situated learning environments help them retain and apply knowledge and skills more effectively in operational settings. In order to provide hints, guidance, and instructional feedback to learners, ITS systems typically rely on three types of knowledge, organized into separate software modules:
- The “expert model” represents subject matter expertise and provides the ITS with knowledge of what it’s teaching
- The “student model” represents the student’s knowledge, skills, and other attributes that affect how the student should be taught. This model lets the ITS know who it’s teaching.
- The “instructor model” enables the ITS to know how to teach, by encoding instructional strategies used by the tutoring system.
Intelligent Tutoring Systems: Here’s how each of these components works.
An expert model is a computer representation of a domain expert’s subject matter knowledge and problem-solving ability. This knowledge enables the ITS to compare the learner’s actions and selections with those of an expert in order to evaluate what the user does and doesn’t know.
A variety of artificial intelligence (AI) techniques are used to capture how a problem can be solved. For example, some ITS systems capture subject matter expertise in rules. That enables the tutoring system to generate problems on the fly, combine and apply rules to solve the problems, assess each learner’s understanding by comparing the software’s reasoning with theirs, and demonstrate the software’s solutions to the participant’s. Though this approach yields a powerful tutoring system, developing an expert system that provides comprehensive coverage of the subject material is difficult and expensive.
A common alternative to embedding expert rules is to supply much of the knowledge needed to support tutoring in each scenario definition. For example, scenario-based tutoring systems enable the course developer to create templates that specify allowable sequences of actions and states. This method avoids the need to encode the ability to solve all possible problems within an expert system. Instead, it requires only the ability to specify the far fewer number of actions that are appropriate in each scenario. Which technique is appropriate depends on the nature of the task and its underlying knowledge and skills.
The student model evaluates each learner’s performance to determine his or her knowledge, perceptual abilities, and reasoning skills. Dr. Valerie Shute at the Educational Testing Service presents the following simple example of a hypothetical arithmetic tutoring system. Imagine that three learners, Jack, Jill, and Bill, are presented with addition problems that they answer as follows:
Though all three students answered incorrectly, different underlying misconceptions caused each person’s errors: Jack fails to carry, Jill always carries (sometimes unnecessarily), and Bill has trouble with single-digit addition. In this example, the student supplies an answer to the problem, and the tutoring system infers the student’s misconceptions from this answer. By maintaining and referring to a detailed model of each student’s many strengths and weaknesses, the ITS can provide carefully selected instruction and practice opportunities to make the most of the student’s learning time.
In more complex domains, the tutoring system can analyze each learner’s actions during simulated scenarios to estimate their knowledge and skills. For example, the Tactical Action Officer (TAO) ITS, developed for the U.S. Navy by Stottler Henke, teaches the use of tactical rules of engagement in realistic scenarios to the officers who control the ship’s weapons and sensors. This system applies finite state machines, specified graphically, that look for sequences of actions and states that indicate principles the student does or doesn’t understand. The simulation window in the left part of Figure 1 lets each student command the ship in simulated battles. The report card at right is displayed at the end of the scenario. It lists appropriate and inappropriate actions carried out by the student, along with associated principles. Red bulleted items describe actions the learner performed incorrectly, and green bullet items describe correct actions.
The instructor model encodes instructional methods that are appropriate for the target domain and the learner. Based on its knowledge of a person’s skill strengths and weaknesses, participant expertise levels, and student learning styles, the instructor model selects the most appropriate instructional intervention. For example, if a student has been assessed a beginner in a particular procedure, the instructor module might show some step-by-step demonstrations of the procedure before asking the user to perform the procedure on his or her own. It may also provide feedback, explanations, and coaching as the participant performs the simulated procedure. It might even pose questions, using Socratic teaching methods, to encourage students to reflect upon their actions and reasoning. As a learner gains expertise, the instructor model may “decide” to present increasingly complex scenarios. It may also decide to take a back seat and let the person explore the simulation freely, intervening with explanations and coaching only upon request. Additionally, the instructor model may also choose topics, simulations, and examples that address the user’s competence gaps…”
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