Designing Intelligent Systems for Teaching Visual ... - AIED Society

International Journal of Artificial Intelligence in Education (1997), 8,44-70

Designing Intelligent Systems for Teaching Visual Concepts ALEXANDRE I. DIRENE School of Cognitive and Computing Sciences University of Sussex, Brighton, BN1 9QH, United Kingdom ______________________________ Currently at: Departamento de Informática, Centro Politécnico-UFPR Curitiba - PR, 81.531-990, Brazil The paper describes how high-level knowledge about visual images could be represented and further interpreted through system-active and systempassive tutorial interactions. The ideas lend themselves to the design and implementation of a category of Intelligent Tutoring Systems aimed at the teaching of abnormalities in highly visual domains, like medical diagnostic imaging. The involved problems are treated through (1) a domainindependent, object-oriented method for managing the complexity of design representations and (2) a model of dialogue interpretation for implementing tutorial interactions. The method and the model are both supported here by implemented, computer-based tools that integrate the multi-layer environment RUI. To evaluate the power of RUI, empirical observations have been carried out, focussing on the generality of the design methodology as well as on the usability of the interface. We draw conclusions about the suitability of object-oriented principles in the context of ITSs evolution. INTRODUCTION This paper describes how high-level knowledge about visual images can be represented and further interpreted through system-active and system-passive tutorial interactions. The ideas lend themselves to the design and implementation of a category of Intelligent Tutoring Systems (ITSs) aimed at the teaching of abnormalities in highly visual domains, like medical diagnostic imaging for chest X-rays (Figure 1) and for MR-scans of the head (Figure 2). The perspective adopted for knowledge representation is object-oriented in that pedagogic behaviour is “encapsulated” with domain expertise “inside” anatomical components to provide for the consistency of tutorial dialogues.

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Designing Intelligent Systems for Teaching Visual Concepts

Figure 1. Dialogue about cardiac X-rays We shall argue that, in highly visual domains, a learner's knowledge and expertise can be best developed through a computer tutor if he or she has access to the same surface knowledge representations created by experts, also using computer-based tools. However, new computational concepts are needed to cope with the complex phenomenon of knowledge communication (from expert to machine and from machine to student). This paper is primarily an exercise in knowledge engineering that carries a strong cognitive flavour. Related work in the field of ITS gives a comprehensive view of domain-independent design methods and tools for building computer tutors (Major, 1995; Murray, & Woolf, 1992; Woolf, 1991; Spensley, & Elsom-Cook, 1988; Scott, 1987; Sleeman, 1987; Bonar, Cunningham, & Schultz, 1986; Nicolson, & Scott, 1986; O'Shea, Bornat, du Boulay, Eisenstadt, & Page, 1984; Bunderson, 1974), but no account is taken for domains of visual concepts.

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Figure 2. Dialogue about scans of the head First, I shall discuss a domain-specific approach developed for the Radiology Tutor (Sharples, & du Boulay, 1988; Sharples, 1989) because it addresses, in computational and pre-computational forms, some aspects of the approach presented here. Sharples and du Boulay view the learning of medical visual concepts (such as pathologies), through a computer-based tutor, as the acquisition of competence in a space of feature dimensions where boundaries of pathologies are defined, and that such competence can be developed by interacting with ordered sets of images via the ITS. They point out that, in normal practice, much of a student's knowledge about pathologies tends to be acquired inductively, giving rise to over-generalisation. The immediate consequence is that these over-general beliefs cover not only example images but also non-examples (see Figure 3), requiring the ITS to take further action in order to bring the student's beliefs to a consistent state. Explicit “emendation” and “discrimination” teaching actions, they argue,

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are needed to diminish the fragility of knowledge which is acquired inductively.

Figure 3. 2-D view of a feature space VISUAL CONCEPT TUTORING AND ITS DESIGN Visual concept tutoring is not a new idea but past work has tended to concentrate on the theoretical principles of how humans acquire expertise in visual recognition (Lesgold et al, 1989; Myles-Worsley, & Johnston, 1988; Howard, 1987; Lesgold, 1984; Mervis, & Rosch, 1981; Rosch, 1978). The few implementations there have been are domain-specific (Sharples et al., 1995; Jeffery et al., 1993; Parkes, 1989; Sharples, & du Boulay, 1988; Rivers, 1988; Swett, & Miller, 1987). However, the problem of providing a domain-independent framework for describing knowledge of ITSs that teach visual concepts has been neglected. Also neglected is the question of how meta-level knowledge can be encoded in order to regulate tutorial dialogues about abnormal image features while enforcing consistency of such dialogues. 47

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From the architectural viewpoint, the design of ITSs has been greatly influenced by the four-box approach which suggests dividing the representations of such systems into domain, student, pedagogic and interface “modules.” Orthogonal to variations of architectural divisions, design methods must also take into consideration that ITSs tend to be costly and complex machines. The wide variety of subject domains has led to a considerable effort in exploring the psychological, educational and computational questions underlying learning and teaching viewpoints, making it difficult for inventors to map conceptual aspects into implemented structures. One such method, Bites (Bonar, Cunningham, & Schultz, 1986), explicitly tries to deal with complexity by means of an object-oriented architecture to provide the curriculum-independent part of an ITS, organised around “abstraction hierarchies.”

We describe RUI (Representations for Understanding Images), (1) an object-oriented method and software tools for managing the complexity of ITS design, and (2) a domain-independent model of dialogue interpretation, integrated with the method, for implementing tutorial interactions. Besides the fact that visual interpretation tasks in different domains have much in common (Rosch, 1978), computer tutors for visual concepts differ from more traditional tutoring systems in that the skills to be communicated to students are closely linked to the interpretation of image patterns as a primary task. Therefore, these systems must include facilities for students to manipulate and display large stocks of visual images. Likewise, design methods and tools for producing these tutors must provide experts with mechanisms for creating and assigning highlevel, symbolic descriptions to such images. RUI (Direne, 1993; Direne, 1994) meets the above requirements. It is fully implemented as three domain-independent tools, each for a different level of abstraction (see Figure 4). The design steps involve two levels: conceptual and production. The third level, instructional, aims at the communication of expertise. The next sections will describe these levels.

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Figure 4. Communication layers in RUI THE INSTRUCTIONAL LEVEL In trying to understand what constitutes radiological expertise, Lesgold et al. (1989) suggest that it involves substantial amounts of both principled knowledge and experiential knowledge. Principled knowledge refers to the distinct bodies of medical knowledge which are already formalised (e.g. anatomy, theories of medical disease and the projective geometry of radiography) whereas experiential knowledge involves the integration of these bodies of knowledge in clinical practice to produce accurate diagnoses. Compared to the basic learning mechanism of Anderson’s ACT* theory (Anderson, 1984), the knowledge compilation process, and more recently to the ACT-R theory of visual attention (Anderson, 1993), it is reasonable to assume that principled knowledge corresponds to the idea that knowledge is first acquired declaratively through instruction. Later, it is transformed into procedures through practice (experiential knowledge). This section describes how these two

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types of knowledge can be communicated to students by interacting with RUI's Learning Tool. At the instructional level, the Learning Tool allows a student to acquire expertise by means of two distinct interfaces: the Image Database Browser (IDB) and the Guided Tutorial Mode (GTM). The IDB aims at the communication of principled knowledge while the GTM aims at the communication of experiential knowledge. In using the IDB, the systempassive interface, students, possibly assisted by human experts, search and select example images based on anatomical information, observe individual feature values as well as feature locations, make interactive measurements of ratios and compare complete diagnoses to acquire the underlying principled knowledge necessary for classifying abnormalities. In using the GTM, the system-active interface, students stretch their experiential knowledge by engaging in Socratic-like dialogues (see Figures 1 and 2) where, constantly challenged by the system, they make use of their conceptual principles to form more accurate and complete diagnoses. It is important to remark that, up to the present moment, RUI's Learning Tool should be understood as an experimental ITS for supplementary teaching, for example, to help expert radiologists in their teaching tasks. Thus, experts can choose an image themselves from the IDB and advise the trainee to explore it according to all the facilities available (see next subsection) or even switch to the GTM and interact with it under the chosen image. This means that the main long-term pedagogic decisions are left to human experts, and that there is no strict order of which interface should be used first by the trainee, the IDB or the GTM. The IDB and Free Exploration The IDB interface is conceived as a learning-by-discovery environment. Free exploration encourages students to become autonomous learners by allowing them to compose their own questions, hypothesise about the concepts of a domain and draw conclusions from hypotheses. In visual domains, this includes understanding the nature of visual concepts translated into features such as shape, size and location of anatomical components. Principled knowledge expresses the ability to recognise abnormalities based on deformations and variations of such anatomical structures, projected onto 2-D image regions (Lesgold, Rubinson, Glasser, Klopfer, & Wang, 1989; Lesgold, 1984). In order to display an overview of what a subgroup of images look like in a given class of abnormality, the IDB allows the student to search for images, according to set criteria. This interactive facility is mainly guided 50


by pointing an anatomic component with the mouse and by selecting possible feature values from a pop-down menu. It permits one to compose search conditions according to conjunctions of the referred features and their values, or even store the currently selected subgroup of images for performing further searching over the same subgroup.

Figure 5. Snapshot of the Learning Tool After the desired features are selected, if the “SEARCH” button is pressed (see Figure 5), the IDB will look for all the example images in the current class that match the conjunction of such features. The IDB then makes these images (if any) available under the inspection modes (see below). For instance, Figure 5 shows the third exemplar image, in a total of 6 images, obtained with the search command, applied according to the following search condition: AND . To inspect the example images of a class of abnormality in more detail, or even a subgroup of these images, the IDB offers several options:

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O1: Button “FF>>>” is used to move forwards in the subgroup of images; O2: Button “