ENHACING ABDUCTIVE REASONING IN DESIGN AND ENGINEERING EDUCATION VIA PROBABILISTIC KNOWLEDGE; A CASE STUDY IN AI

Abstract

As we are moving into a knowledge-based economy, frameworks addressing the translational processes revolving around value and impact permeate the development of educational curriculums in the design and engineering educational spectrum. In response to this approach this paper presents an operational framework that explores how abductive reasoning and its embodied probabilistic knowledge can bridge the gap between the challenges of accelerating technological development and current design and engineering educational practice. This is to enable students to locate, evaluate and work creatively with knowledge to generate new and improved solutions that can tackle uncertain and future real-world challenges, while delivering impact and value for society. Building from an Aristotelian perspective of productive knowledge, we will introduce probabilistic knowledge as the most suitable model to translate potentialities from current concerns to projected future value. This repositioning enables practitioners to move beyond proving reality to a generative space aiming to transform it. In this context we present abductive reasoning as a fundamental approach to deal with directional and transformational potentialities that tackle future uncertainties. Abductive research is significantly different from induction or deduction. This process moves, from rule > to result > to case (Danermark, 2001, Kirkeby, 1990). It differs from deductive processes which move from rule > to case > to result (Danermark, 2001; Kirkeby, 1990). Or inductive logic which moves from case > to result > to rule (Danermark, 2001; Kirkeby, 1990; Wigblad, 2003). In this paper, this process will be described through a case study on highly automated virtual assistants which are particularly relevant to the study of the exponential nature of artificial intelligence. The abductive reasoning started with a deviating observation on the emergent nature of Machine Learning, then, we constructed a best guess model to address it, and concluded with a hypothesis or propositions in the form of a mixed methodology, (Prospective Design), which then was applied, progressively and cross-disciplinary evaluated, and critically analysed. This approach has produced eleven publications spanning five fields; applied engineering, human factors, design theory, future studies, and industry 4.0. The combination of abductive reasoning and its embodied probabilistic knowledge enabled us to prospect the future to propose that things can be otherwise by providing guiding knowledge for transforming the future in an applied and ethical manner, delivering impact and value to society in the process. This aspect is particularly relevant in design & engineering which fundamentally revolve around the idea of the ‘new’ and operates in uncertain and exponential technological developments.