Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems

• Published in: Cognitive systems research Vol. 87; p. 101261
• Main Author: Sychev, Oleg
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Constraint-based modeling; Intelligent tutoring systems; Reasoning modeling; Reasoning modeling; Intelligent tutoring systems; Constraint-based modeling
• ISSN: 1389-0417
• DOI: 10.1016/j.cogsys.2024.101261

Automation of teaching people new skills requires modeling of human reasoning because human cognition involves active reasoning over the new subject domain to acquire skills that will later become automatic. The article presents Thought Process Trees — a language for modeling human reasoning that was created to facilitate the development of intelligent tutoring systems, which can perform the same reasoning that is expected of a student and find deficiencies in their line of thinking, providing explanatory messages and allowing them to learn from performance errors. The methodology of building trees which better reflect human learning is discussed, with examples of design choices during the modeling process and their consequences. The characteristics of educational modeling that impact building subject-domain models for intelligent tutoring systems are discussed. The trees were formalized and served as a basis for developing a framework for constructing intelligent tutoring systems. This significantly lowered the time required to build and debug a constraint-based subject-domain model. The framework has already been used to develop five intelligent tutoring systems and their prototypes and is being used to develop more of them. •Thought Process Trees is a new language of modeling reasoning for learning.•Recommendations for effective building of trees for verifying learners’ answers.•Thought Process Trees can be formalized and executed.•The proposed language significantly reduces time of developing intelligent tutors.•It lets classify problems, define possible errors, and generate educational dialogue.