Intelligent Design and the Nature of Science: Philosophical and Pedagogical Points

  • Ingo Brigandt
Part of the History, Philosophy and Theory of the Life Sciences book series (HPTL, volume 1)


This chapter offers a critique of intelligent design arguments against evolution and a philosophical discussion of the nature of science, drawing several lessons for the teaching of evolution and for science education in general. I discuss why Behe’s irreducible complexity argument fails, and why his portrayal of organismal systems as machines is detrimental to biology education and any understanding of how organismal evolution is possible. The idea that the evolution of complex organismal features is too unlikely to have occurred by random mutation and selection (as recently promoted by Dembski) is very widespread, but it is easy to show students why such small probability arguments are fallacious. While intelligent design proponents have claimed that the exclusion of supernatural causes mandated by scientific methods is dogmatically presupposed by science, scientists have an empirical justification for using such methods. This justification is instructive for my discussion of how to demarcate science from pseudoscience. I argue that there is no universal account of the nature of science, but that the criteria used to judge an intellectual approach vary across historical periods and have to be specific to the scientific domain. Moreover, intellectual approaches have to be construed as practices based on institutional factors and values, and to be evaluated in terms of the activities of their practitioners. Science educators should not just teach scientific facts, but present science as a practice and make students reflect on the nature of science, as this gives them a better appreciation of the ways in which intelligent design falls short of actual science.


Science Education Evolutionary Theory Intelligent Design Extant Species Methodological Naturalism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I am indebted to Kostas Kampourakis, Alessandro Minelli, and an anonymous referee for comments on earlier versions of this essay. I thank Emma Kennedy for her thorough copyediting of the manuscript. This work was funded by the Social Sciences and Humanities Research Council of Canada (Standard Research Grant 410-2008-0400).


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of AlbertaEdmontonCanada

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