In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

Considering how quickly engineering design and manufacturing have advanced alongside computational developments, it may surprise you that very few engineers are trained in both engineering system design and artificial intelligence. There are countless opportunities for breakthrough improvements in how we develop new technology using AI in engineering design, but to succeed in these challenging areas, engineers must understand a new speciality--Design for Artificial Intelligence. Chris McComb, Associate Professor of Mechanical Engineering at Carnegie Mellon, and his student Glen Williams, now Principal Scientist at Re:Build Manufacturing, have developed a Design for Artificial Intelligence (DfAI) framework in collaboration with researchers at Penn State University to educate and encourage the academic and industrial engineering community to adopt AI engineering design. "Most of the time, we view AI as a tool to add onto an existing system, but to develop better systems we need to integrate AI into the engineering design process from the very beginning," McComb explains. A core challenge is motivating institutions to make investments in the long-term potential of AI technologies.

The Engineering Design Process is a series of steps that engineers follow to find a solution to a problem. Steps include problem solving processes, for example, determining your objectives and constraints, prototyping, testing and evaluating. This process is critical to the work TWI does and is something we can provide support for. While the design process is iterative, it follows a predetermined set of steps, some of which may need to be repeated before moving on to the next one. This will vary depending on the project, but allows for lessons to be learned from failures and improvements to be made.

This paper reports on the Design Compass, a classroom tool for helping students record and reflect on their design process as they work on and complete a design challenge. The Design Compass software provides an interface where students can identify and record the various design steps they used while performing them, and add digital notes and pictures to document their work. In the Design Log view, students can review steps taken, and print the record of work done, which can be shared and discussed with their instructor or classmates. The paper describes the concepts underlying the creation of the Design Compass, its features as a metacognitive tool and how it works, and provides scenarios of its use as a teaching and assessment tool with eighth-grade technology education students, and in teacher professional development workshops.