Developing Applications of Artificial Intelligence Technology To Provide Consultative Support in the Use of Research Methodology by Practitioners.

Adopting perspectives based on applications of artificial intelligence proven in industry, this paper discusses methodological strategies and issues that underlie the development of such software environments. The general concept of an expert system is discussed in the context of its relevance to the problem of increasing the accessibility of expert assistance to research practitioners. Important methodological issues and development strategies that underlie the construction of such systems are discussed. Some illustrative examples addressing the question of representation of research expertise (versus textbook-based information) are presented. Finally, implications and priorities for the longitudinal development of practitioner-supporting expert systems for the representation of the research knowledge-base and the design of knowledge-based instructional environments for improving graduate training in research are noted. Nine figures illustrate the discussion. (Contains 25 references.) (SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** DEVELOPING APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNOLOGY TO PROVIDE CONSULTIVE SUPPORT IN THE USE OF RESEARCH METHODOLOGY BY PRACTITIONERS 1 Michael R. Vitale, East Carolina University Nancy Romance, Florida Atlantic University Abstract U.S. DEPARTMENT Of EDUCATION Office of Educational Reseirch and Improvemint EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 0 This document has been feOrOdUced received I rom the person or organization originating it 0 Minor changes have been made to improve reproduction qualityU.S. DEPARTMENT Of EDUCATION Office of Educational Reseirch and Improvemint EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 0 This document has been feOrOdUced received I rom the person or organization originating it 0 Minor changes have been made to improve reproduction quality Points of view or opinions stated in this doctr ment do not necessarily represent official OERI position of policy Artificial intelligence methodology offers a potential means for insuring "real time" accessibility of consultive research expertise to research practitioners. Adopting perspectives based upon applications of artificial intelligence applications proven in industry, this paper discusses methodological strategies and issues that underlie the development of such software environments. Expert systems (Payne & McArthur, 1990) and related techniques (Carr, 1992) are applications of artificial intelligence that have been developed specifically to facilitate the transfer and accessibility of knowledge (i.e., expertise) from human experts to practitioners. The purpose of this paper is to explore methodological issues pertaining to the application of expert systems to the problem of providing consultive research expertise to research practitioners. In doing so, the paper consists of four sections. First, the general concept of an expert system is overviewed in the context of its relevance to the problem of increasing the accessibility of expert assistance to research practitioners. Second, important methodological issues and development strategies that underlie the construction of such systems are discussed. Third, some illustrative examples addressing the question of representation of research expertise (vs. textbook-based information) are presented. And, fourth, the implications (and priorities) for the longitudinal development of practitioner-supporting expert systems, for the representation of the research knowledge-base in the field, and for the design of knowledge-based instructional environments for improving graduate training in research are noted. Expert Systems: Concepts and Applications for Increasing the Accessibility of Research Ex ertise Expert systems are specialized software applications with which practitioners needing advice and guidance on specific problems can interact. The basic requirement for a well-designed expert system in such settings is that it successfully emulates the consultive assistance of a human expert. This perspective would suggest that the ideal circumstances might be those in which human experts with specialized proficiency in research methodology are always available on demand to provide guidance to non-technically oriented research practitioners. The need for expert systems then is implied by the fact that there are far too few research-methods experts to provide the ongoing level of support needed for technically-sound and efficiently-designed research in school settings by inexperienced practitioners, particularly in light of present trends toward action and school-based research. The emphasis in the present paper is primarily upon the potential of expert systems as tools that provide consultive assistance. However, the development ofany expert system requires standards of practice within a discipline that provide the foundation for the forms of procedural knowledge that are 1 Paper presented at the Annual Meeting of the American Educational Research Association. Atlanta GA, April, 1993. BEST COPY AVAILABLE 2 "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY Nancy Romance TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC). " Al Research Support Page 2 incorporated into such systems. Thus, in addition to being a tool, expert systems also provide a context for motivating a restructuring of the base of knowledge within a discipline, particularly with regard to integrating textbook-based knowledge with the advanced practices of experts. With this in mind, this paper also notes implications of expert systems methodology kradvancing paradigmatic perspectives (e.g., Kuhn, 1970) of knowledge within the field of research itself. Elaboration of the Concept of an Expert System Consultant. The utilization of an expert system (see Figure 1) by a research practitioner is equivalent to an interactive consultation with a highly experienced human expert to gain assistance in addressing a research problem (e.g., What should I do to conduct research on this topic? , What should my research questions be? ) or in solving a specific problem within a research study (e.g., Given the conclusions I want to make, how shoald I design my study? , Given_ow should I am_ m _sme tco es? ). During such a consultation, the research expert and practitioner would interact to describe the problem, clarify the specific details and constraints, and then specify the actions to be taken. In guiding the interaction, the expert's knowledge of research principles in conjunction with practical rules of thumb based upon extensive research experience would be linked to the practitioners specific knowledge of the problem solution. As part of this process, the consultant would be able to answer questions explaining why certain information is needed, how various conclusions were reached, and how possible changes in the research setting (e.g., research questions, practical constraints) might require alternate solutions (i.e., what if questions). Within an expert systems application, research practitioners would obtain similar consultive assistance through expert systems software developed to emulate the competence of such an experienced human expert (Vitale, 1992). Under conditions in which the availability of human experts is limited, the potential value of expert systems software is the capability to deliver consultive expertise to an individual with a degree of frequency or level of depth that a human expert could not provide due to limited accessibility. In developing such software (see Figure 2), the major logical requirements that must be addressed technically are the representation of research expertise (i.e., the 'knowledge-base"), the specification of knowledge-processing mechanism that control the linking of the knowledge to the specifics of the problem setting in order to identify appropriate courses of action (i.e., the "inference engine"), and the design of the interface through which the researcher practitioner and software will interact. General Characteristics of Expert System Software Environments. An important step in the development of expert systems software is the t election of a software environment for building (and delivering) the system. Although any general purpose programming language, in principle, could be used, specially designed software development environments called "shells are commonly used because they facilitate rapid prototyping and efficient system construction for a variety of methodological reasons. Among the most important is that the architecture of the shells separates the "knowledge" component of the software from its other parts (e.g., user interface, management of consultive data, explanation facility, knowledge-processing/inference component). By using shells, developers of expert systems applications are free to focus upon the encoding of expertise (as knowledge) that allows the software to emulate the performance cf an expert rather than writing computer program(s) to implement all the various components of the system. Thus, the shell itself provides the developer with a supportive environment containing all of the tools needed to produce the system with the exception of the knowledge (that the developer inserts into the system) and the nature of the interface (about which the developer decides). Some Technical Aspects of Expert Systems: Knowledge Representation and Logical Inference. When encoding expertise into the shell software, the simplest form of knowledge representation consists of production rules (see Figure 3) in the form of IF/THEN statements through which actions (i.e., THENs) are tied to patterns of conditional information (i.e., IFs). With such knowledge-based rules entered into the software shell, the expert system is able to decide what act