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We are focusing on three interconnected issues that negatively impact engineering disciplinary cultures: (1) diversity and inclusion issues that continue to plague engineering programs; (2) lack of adequate preparation for professional practices; (3) and exclusionary engineering disciplinary cultures that privilege technical knowledge over other forms of knowledge [1]. Although much effort has been devoted to these issues, traditional strategic and problem-solving orientations have not resulted in deep cultural transformations in many engineering programs. We posit that these three issues that are wicked problems. Wicked problems are ambiguous, interrelated and require complex problem-scoping and solutions that are not amenable with traditional and linear strategic planning and problem-solving orientations [2]. As design thinking provides an approach to solve complex problems that occur in organizational cultures [3], we argue that these wicked problems of engineering education cultures might be best understood and resolved through design thinking. As Elsbach and Stigliani contend, “the effective use of design thinking tools in organizations had a profound effect on organizational culture” [3, p. 2279]. However, not all organizational cultures support design thinking approaches well. Despite increasing calls to teach design as a central part of professional formation (e.g., ABET, National Academy of Engineers, etc.), many engineering programs, especially larger, legacy programs have not embraced fundamental design thinking [4-5] strategies or values [6-7]. According to Godfrey and Parker, many engineering cultures are characterized by linear epistemologies, “black and white” approaches to problem solving, and strategic “top down” ways of designing [8]. In contrast, design thinking approaches are characterized by ways of thinking and designing that prioritize prototyping, multiple stakeholder perspectives, and iterative problem-solving to address complex problems. In this paper, we examine the effectiveness of design thinking as a tool to address wicked problems in engineering education cultures, and the role of engineering culture itself in shaping the application and effectiveness of design thinking. More specially, we evaluate the role of design thinking in seeking cultural transformation at a School of Electrical and Computer Engineering (ECE) at Purdue University. We analyze interviews of members of the School after they participated in six design thinking sessions. Our previous research explored the effect of design thinking sessions on participant understanding of diversity and inclusion in biomedical engineering [9]. Herein, we explore participant experiences of design thinking sessions toward cultural change efforts regarding diversity and inclusion (D&I) within professional formation in ECE. We identified three tensions (push/pull dynamics of contradictions) that emerged from the participants’ experiences in the design sessions [10]. We conclude by discussing our emerging insights into the effectiveness of design thinking toward cultural change efforts in engineering.
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Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering
Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algorithms, more accurate uncertainty quantification, smarter and faster data collection, and incorporation of diverse stakeholders into decision-making processes, improving the robustness of engineering and policy designs while focusing on the multifaceted goals and constraints in wicked problems.
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- Award ID(s):
- 1941596
- PAR ID:
- 10572466
- Publisher / Repository:
- PSE Press
- Date Published:
- Page Range / eLocation ID:
- 22 to 31
- Format(s):
- Medium: X
- Location:
- Breckenridge, Colorado, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.69997/sct.114705
