Fig. 1: Integrated design and simulation in Energy3D

In workplaces, engineering design is supported by contemporary computer-aided design (CAD) tools capable of virtual prototyping — a full-cycle process to explore the structure, function, and cost of a complete product on the computer using modeling and simulation techniques before it is actually built. In classrooms, such software tools allow students to take on a design task without regard to the expense, hazard, and scale of the challenge. Whether it is a test that takes too long to run, a process that happens too fast to follow, a structure that no classroom can fit, or a field that no naked eye can see, students can always design a computer model to simulate, explore, and imagine how it may work in the real world. Modeling and simulation can thereby push the envelope of engineering education to cover much broader fields and engage many more students, especially for underserved communities that are not privileged to have access to expensive hardware in advanced engineering laboratories. CAD tools that are equipped with such modeling and simulation capabilities provide viable platforms for teaching and learning engineering design, because a significant part of design thinking is abstract and generic, can be learned through designing computer models that work in cyberspace, and is transferable to real-world situations.

Some researchers, however, cautioned that using CAD tools in engineering practices and education could result in negative side effects, such as circumscribed thinking, premature fixation, and bounded ideation, which undermine design creativity and erode existing culture. To put the issues in a perspective, these downsides probably exist in any type of tools — computer-based or not — to various extents, as all tools inevitably have their own strengths and weaknesses. As a matter of fact, the development history of CAD tools can be viewed as a progress of breaking through their own limitations and engendering new possibilities that could not have been achieved before. To do justice to the innovative community of CAD developers and researchers at large, we believe it is time to revisit these issues and investigate how modern CAD tools can address previously identified weaknesses. This was the reason that motivated us to publish a paper in Computer Applications in Engineering Education to expound our points of view and supporting them with research findings.

Fig. 2: Sample student work presented in the paper

The view that CAD is “great for execution, not for learning” might be true for the kind of CAD tools that were developed primarily for creating 2D/3D computer drawings for manufacturing or construction. That view, however, largely overlooks three advancements of CAD technologies:

1) System integration that facilitates formative feedback: Based on fundamental principles in science, the modeling and simulation capabilities seamlessly integrated within CAD tools can be used to analyze the function of a structure being designed and evaluate the quality of a design choice within a single piece of software (Figure 1). This differs dramatically from the conventional workflow through complicated tool chaining of solid modeling tools, pre-processors, solvers, and post-processors that requires users to master quite a variety of tools for performing different tasks or tackling different problems in order to design a virtual prototype successfully. Although the needs for many tools and even collaborators with different specialties can be addressed in the workplace using sophisticated methodologies such as 4D CAD that incorporate time or schedule-related information into a design process, it is hardly possible to orchestrate such complex operations in schools. In education, cumbersome tool switching ought to be eliminated — whenever and wherever possible and appropriate — to simplify the design process, reduce cognitive load, and shorten the time for getting formative feedback about a design idea. Being able to get rapid feedback about an idea enables students to learn about the meaning of a design parameter and its connections to others quickly by making frequent inquiries about it within the software. The accelerated feedback loop can spur iterative cycles at all levels of engineering design, which are fundamental to design ideation, exploration, and optimization. We have reported strong classroom evidence that this kind of integrated design environment can narrow the so-called “design-science gap,” empowering students to learn science through design and, in turn, apply science to design. 

2) Machine learning that generates designer information: For engineering education research, a major advantage of moving a design project to a CAD platform is that fine-grained process data (e.g., actions and artifacts), can be logged continuously and sorted automatically behind the scenes while students are trying to solve design challenges. This data mining technique can be used to monitor, characterize, or predict an individual student’s behavior and performance and even collaborative behavior in a team. The mined results can then be used to compile adaptive feedback to students, create infographic dashboards for teachers, or develop intelligent agents to assist design. The development of this kind of intelligence for a piece of CAD software to “get to know the user” is not only increasingly feasible, but also increasingly necessary if the software is to become future-proof. It is clear that deep learning from big data is largely responsible for many exciting recent advancements in science and technology and has continued to draw extensive research interest. Science ran a special issue on artificial intelligence (AI) in July 2015 and, only two years later, the magazine found itself in the position of having to catch up with another special issue. For the engineering discipline, CAD tools represent a possible means to gather user data of comparable magnitudes for developing AI of similar significance. In an earlier paper, we have explained why the process data logged by CAD software possess all the 4V characteristic features — volume, velocity, variety, and veracity — of big data as defined by IBM. 

3) Computational design that mitigates design fixation: In trying to solve a new problem, people tend to resort to their existing knowledge and experiences. While prior knowledge and experiences are important to learning according to theories such as constructivism and knowledge integration, they could also blind designers to new possibilities, a phenomenon known as design fixation. In the context of engineering education, design fixation can be caused by the perception or preconception of design subjects, the examples given to illustrate design principles, and students’ own previous designs. As it may adversely affect engineering learning to a similar degree as “cookbook labs” underrepresent science learning, design fixation may pose a central challenge to engineering education (though it has not been thoroughly evaluated among young learners in secondary schools). Emerging computational design capabilities of innovative CAD tools based on algorithmic generation and parametric modeling can suggest design permutations and variations interactively and evolutionarily, equivalent to teaming students up with virtual teammates capable of helping them explore new territories in the solution space.

To read more about this paper, click here to go to the publisher’s website.