An intelligent tutoring system for visual classification problem solving
Abstract
Objective This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving.Materials and methods The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model.Results In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system.Conclusion Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin.
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SlideTutor was developed as an intelligent tutoring system incorporating a virtual microscope and WSIs.
“Building on this concept, SlideTutor was developed as an intelligent tutoring system incorporating a virtual microscope and WSIs.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [54]
SlideTutor provides step-by-step feedback to guide learners of feature recognition, differential diagnosis, and final decision-making, closely mimicking one-on-one instruction.
“It provides step-by-step feedback to guide learners of feature recognition, differential diagnosis, and final decision-making, closely mimicking one-on-one instruction [54].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [54]
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- Authors
- Crowley, R. S., & Medvedeva, O.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Crowley, Rebecca S.
- ORCID
- Medvedeva, Olga
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2006-01
- Volume
- 36
- Issue
- 1
- Pages
- 85-117
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2114436459
- Cited by
- 98
- References
- 64
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 4 · 2021: 6 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 4 · 2016: 1 · 2015: 3 · 2014: 3 · 2013: 3 · 2012: 10
- FWCI
- 7.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- Funders
- National Institutes of Health (5r01lm007891-06)
- U.S. National Library of Medicine (LM007885, LM007891, P41LM007885)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- intelligent tutoring systems, visual categorization, diagnostic problem solving, inflammatory skin diseases, microscopic diagnosis, expert systems, solution graphs, student modeling, pathology education
- MeSH terms
- Artificial Intelligence, Humans, Medical Informatics Applications, Pathology, Pattern Recognition, Visual, Problem Solving, Skin Diseases, User-Computer Interface
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