More and more we hear the complaint that the gap between research and instruction is widening and a vital sense of motivation is falling between the cracks. It is our vision that intelligent computing systems will become a partner in the reintegration of discovery and learning within the inquiry process. We will address certain issues that must be faced if computer media are to have the characteristics necessary to support this integration. The development of the computer to date has required a careful attention to the syntax and semantics of the rather limited symbol systems we have induced them to use. A capacity for communicating in multiple modalities with non-uniform communities of symbol users -- for sharing in the discovery of a pluralistic universe -- will demand a quantum leap in our understanding of the pragmatic dimensions of symbol use. In the future the capacity for inquiry must permeate the living architecture of the computer system. A computer program that begins to embody these ideas will be discussed
Today, we are recognizing that students must become active learners and problem-solvers to cope with the increasing complexity of their current and future worlds. At the same time faculty are disheartened by the growing gap between their research and instructional roles. What is called for is a reintegration of the discovery and learning processes to rekindle essential motivation. For the student this implies the development of an inquiry approach to real world problems. For the instructor, it is the opportunity to merge two currently disparate functions. It is our thesis that computers, intelligent systems in particular, can play a vital role in this reintegration.
Inquiry is focused exploration. We may define it as the search for reasoned explanation or as an attempt to find laws that govern and predict outcomes. According to philosopher Charles Sanders Peirce (ref. 1), inquiry is a process involving three forms of reasoning. First, a phenomenon catches our attention. It may surprise or annoy us but it does not fit with our expectations. We guess at principles that might explain it.
Peirce refers to this process of positing a possible explanation as abductive reasoning. We can also term it hypothesis generation. Next, the results and consequences of the proposed explanation are considered. This is the process of deductive reasoning. Finally, actual consequences are compared to those projected. It is inductive reasoning that determines their fit
The information that anyone (interpreter) has about a phenomenon (object system) is expressed in symbols (signs). This relationship Peirce referred to as the sign relation. For purposes of our discussion, there are two important aspects of this relation. First, the roles within it may change. For example, a person may act as an interpreter or he may be a sign to someone else, as when he smiles or frowns. Secondly, it points out the importance of the interpreter to any discovery since abductive reasoning is done by the interpreter and, therefore, the initial hypothesis generation is subject to all of the constraints placed on it by the interpreter's knowledge base and assumptions
How then can computers assist with learning about and performing this process of inquiry? What advantages do they offer us?
As noted earlier, the problems facing us today in all facets of life from socioeconomics to ecology are characterized by complexity. Computers provide a way of handling data about complex phenomena. For some time computers have been used to store and retrieve large databases of information. Models of quantitative data have also been developed to make predictions about complex systems. However, the difficult task of developing computer programs that can facilitate inquiry has not yet been fully addressed. When developed, computer systems that facilitate inquiry can become a valuable resource for both forming deductions from complex theories and for handling qualitative and sequential data from complex phenomena
A computer program capable of forming logical models based on its environment could provide the following advantages to the faculty researcher. First, by modeling the knowledge base of the researcher it would (1) make the researcher's expressed knowledge base visible and (2) identify implicit knowledge that the researcher has and is using but which has not been incorporated into the proposed theory. Secondly, such a program might assist in hypothesis generation by identifying constraints and assumptions the researcher brings to the problem
This intelligent computer program could assist the faculty member as an instructor by making the student's knowledge base related to a specific inquiry visible. It could provide an environment for the students to perform inquiry based on the real world data gathered by the faculty researcher using the program. It would be possible to begin with the faculty performing the abductive process and pointing out a phenomenon of interest which the student would then pursue through the deductive and inductive stages. Later, the student might perform all stages of the inquiry process.
The following diagram presents a dynamic model of the inquiry process
Figure 1. Dynamics of Inquiry
When a phenomenon presents itself, our task is to explain it. We observe the features of the phenomenon ① and make a guess about its explanation (abduction). We form a "theory" which we represent in terms of observed features and events. This "expressed theory" ②, is comprised of the laws and principles believed to govern the phenomenon. Using this theory we can deduce the possible consequences and outcomes it would predict (deduction) and formulate a model of it ③. We can then compare the model with the properties of the original phenomenon, some of which may need to be elicited by further experiment (induction). When a theory is expressed, the investigator may not have included all of the necessary underlying knowledge in the expressed theory. By representing the theory computationally, this missing implicit knowledge ④ often comes to light and assists in clarifying the theory (explication).
Developing a computer program for inquiry that recognizes events, forms models of its environment, and formulates rules based on experience means careful attention to the fundamentals of the symbol systems used. The authors have developed a prototype, PC-based program designed to integrate inductive and deductive reasoning. Theme One is comprised of two components, called Index and Study. Index is a learning algorithm for sequential data. It acquires a two-level formal language that describes the qualitative features of a given domain. Study builds logical models of this domain using propositional calculus. Theme One has been applied to studies on family interaction, and a study involving its use in clinical reasoning is in process.
The development of a computer program for inquiry that uses artificial intelligence is underway. The ultimate goal of the project is development of an interactive tool for research that assists students and investigators in inquiries involving qualitative data. In the future such programs could provide an environment for students to participate in and become proficient at abductive, deductive, and inductive reasoning. It is hoped that development of a computer architecture for inquiry will assist in the reintegration of discovery and learning and restore the vitality of exploration to the educational process
1. C.S.Peirce, Collected Papers of Charles Sanders Peirce (Harvard University Press, Cambridge, MA, 1931-1960).
Awbrey, S. and Awbrey, J. (1991). "An Architecture for Inquiry: Building Computer Platforms for Discovery", in Proceedings of the Eighth International Conference on Technology and Education, G. McKye and D. Trueman (eds.), Toronto, Ontario, May 8-12, 1991.