ENHANCING 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 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. In the process, we introduce probabilistic knowledge as the most adequate model to translate potentialities into impact and 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 to tackle uncertainties.