Inquiry Driven Systems : Are There Apps For That?

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Inquiry Driven Systems : 1.1.4. Application

Where can the results be used? Knowledge about the nature of inquiry can be applied. It can be used to improve our personal competence at inquiry. It can be used to build software support for the tasks involved in inquiry.

If it is desired to articulate the loop of self-application a bit further, computer models of inquiry can be seen as building a two-way bridge between experimental science and software engineering, allowing the results of each to be applied in the furtherance of the other.

In yet another development, computer models of learning and reasoning form a linkage among cognitive psychology (the descriptive study of how we think), artificial intelligence (the prospective study of how we might think), and the logic of operations research (the normative study of how we ought to think in order to achieve the goals of reasoning).


Frequently encountered complementaries, dualities, or design trade-offs

  • Integrating data-driven (empiricist) and concept-driven (rationalist) modes of inquiry.
  • Integrating model-theoretic and proof-theoretic methods for evaluating theories.
  • Bridging the gap between qualitative and quantitative research methodologies.
  • Relationship between emergent-evolved systems and engineered systems.
  • Relationship between descriptive sciences and normative sciences.

Relationship between emergent-evolved systems and engineered systems

I am taking a systems-theoretic view of the inquiry process, but I am focused on the kinds of systems we engineer to a specific purpose, for example, computational support for scientific inference. With that aim in mind the kinds of understanding we gain from connectionist, emergent property, genetic algorithm, or self-organizing systems research typically falls short of telling us how scientific inquiry can manage to work in the frame of time that human beings have at their command.

When we set about engineering artificial systems to augment our natural capacities — the way we build microscopes and telescopes to extend the reach of our eyes — our success in doing that naturally depends on how well we understand the natural system we are trying to extend.

One form of understanding is achieved when we draw on principles embodied in a natural system that are general enough to be embodied in very different artificial systems. That is the method of analogical extension, and it turns on the recognition of an abstract principle that can be shared by otherwise diverse systems.


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Software Prototype

Sample Applications

Learning Module (\text{Slate})\! applied to data analysis tasks

Reasoning Module (\text{Chalk})\! applied to concept analysis tasks



  • Awbrey, J.L., and Awbrey, S.M. (Autumn 1995), “Interpretation as Action : The Risk of Inquiry”, Inquiry : Critical Thinking Across the Disciplines 15(1), pp. 40–52. Archive. Online.
  • Awbrey, S.M., and Awbrey, J.L. (May 1991), “An Architecture for Inquiry : Building Computer Platforms for Discovery”, Proceedings of the Eighth International Conference on Technology and Education, Toronto, Canada, pp. 874–875. Online.
  • Awbrey, J.L., and Awbrey, S.M. (August 1990), “Exploring Research Data Interactively. Theme One : A Program of Inquiry”, Proceedings of the Sixth Annual Conference on Applications of Artificial Intelligence and CD-ROM in Education and Training, Society for Applied Learning Technology, Washington, DC, pp. 9–15. Online.