We use the process and findings from a case study of design educators' practices of assessment and feedback to fuel theorizing about how to make AI useful in service of human experience. We build on Suchman's theory of situated actions. We perform a qualitative study of 11 educators in 5 fields, who teach design processes situated in project-based learning contexts. Through qualitative data gathering and analysis, we derive codes: design process; assessment and feedback challenges; and computational support. We twice invoke creative cognition's family resemblance principle. First, to explain how design instructors already use assessment rubrics and second, to explain the analogous role for design creativity analytics: no particular trait is necessary or sufficient; each only tends to indicate good design work. Human teachers remain essential. We develop a set of situated design creativity analytics--Fluency, Flexibility, Visual Consistency, Multiscale Organization, and Legible Contrast--to support instructors' efforts, by providing on-demand, learning objectives-based assessment and feedback to students. We theorize a methodology, which we call situating analytics, firstly because making AI support living human activity depends on aligning what analytics measure with situated practices. Further, we realize that analytics can become most significant to users by situating them through interfaces that integrate them into the material contexts of their use. Here, this means situating design creativity analytics into actual design environments. Through the case study, we identify situating analytics as a methodology for explaining analytics to users, because the iterative process of alignment with practice has the potential to enable data scientists to derive analytics that make sense as part of and support situated human experiences.

We investigate how to use AI-based analytics to support design education. The analytics at hand measure multiscale design, that is, students' use of space and scale to visually and conceptually organize their design work. With the goal of making the analytics intelligible to instructors, we developed a research artifact integrating a design analytics dashboard with design instances, and the design environment that students use to create them. We theorize about how Suchman's notion of mutual intelligibility requires contextualized investigation of AI in order to develop findings about how analytics work for people. We studied the research artifact in 5 situated course contexts, in 3 departments. A total of 236 students used the multiscale design environment. The 9 instructors who taught those students experienced the analytics via the new research artifact. We derive findings from a qualitative analysis of interviews with instructors regarding their experiences. Instructors reflected on how the analytics and their presentation in the dashboard have the potential to affect design education. We develop research implications addressing: (1) how indexing design analytics in the dashboard to actual design work instances helps design instructors reflect on what they mean and, more broadly, is a technique for how AI-based design analytics can support instructors' assessment and feedback experiences in situated course contexts; and (2) how multiscale design analytics, in particular, have the potential to support design education. By indexing, we mean linking which provides context, here connecting the numbers of the analytics with visually annotated design work instances.

In this edition of our Engineer Innovation podcast, we hear from Chad Jackson at Lifecycle Insights in discussion with Siemens Digital Industries Software AI expert, Justin Hodges as they explore the role of machine learning for simulation engineers. Justin breaks down what can seem a daunting area into the key benefits, real-life application examples and most importantly how you can adopt the methodology to see rewards for your simulation projects. Whilst the clearest benefit is the time saved, not only for the simulation process but potentially across the entire design-cycle, that time can then be used to determine even better outcomes and improvements for future product configurations. Chad and Justin explore some real-life examples before diving into how to roll out this methodology in your organization. Ginni Saraswati: Welcome to the Engineer Innovation podcast.

Whether you're creating a sleek new logo for your company or a magazine cover that's popping with bright colors and interesting shapes, graphic designers need the right kind of laptop to get the job done. The most important thing is powerful hardware. For tasks like 3D modeling, you're going to need a powerful CPU and a good amount of RAM. Depending on the size and complexity of the project, you may need a processor with multiple cores. Another essential piece of hardware is the graphics card.

The shape of a robot determines what types of tasks it can perform and environment it can operate in. With current technological limitations, there is no way to build and test each form, but a new system developed by researchers at MIT allows for these many forms to be simulated. After simulations, the best of them can be picked out of the group. The new system is called RoboGrammar, and the first step is to inform it what types of robot parts are available, such as wheels and joints. You then indicate the type of terrain the robot will operate on, but that's basically it.

RoboGrammar is a new system that automates and optimizes robot design. The system, developed at MIT, creates arthropod-inspired robots for traversing a variety of terrains. It could spawn more inventive robot forms with enhanced functionality.

Many of our digital interactions are increasingly being done through artificial intelligence-driven chatbots & algorithms whether we realize it or not. As we enter this A.I. driven world we ask what are the design & ethical considerations as we enter this paradigm shift for humanity. Join us for this panel discussion as we ask our panelists: Just what is A.I. & what are the different types of A.I.? What's different about designing for A.I.? Where should you start learning to design for A.I.? What are the ethical considerations for designing for A.I.? Which A.I. related platform(s) should you learn? What jobs will open up for A.I.? Schedule 6:30 pm - Doors Open 7:00 pm - Panel Starts 8:30 pm - Panel Ends 9:00 pm - Event Ends / Doors Close Moderated by: Amy Stillhorn, Founder of Big Monocle Amy is the founder & CEO ofBig Monocle, an award-winning creative agency with offices in Utah & California (San Francisco, San Jose, Redwood City & Provo) that services startups & fortune 100 clients.

That A.I. is able to beat humans in board games is common knowledge by now. How that is relevant to companies is less clear. Normally scientists could be of help here. Clearing up the mess by debunking the hype and showing how trustworthy the technology really is. But many of the good A.I. researchers are connected to big tech companies and these companies have little incentive to debunk a hype when that negatively impacts their share holder value.

Graphic design used to require physical work. To compose letterheads, business cards, brochures, magazines, books, and posters, you hunched over a desk or a light table. You cut and pasted paper or assembled metal type on a printing press. You processed 35mm film by hand, developing pictures in a darkroom with chemicals. Jason Tselentis is an educator, writer, and designer.

Today, we're on the verge of another revolution, as artificial intelligence and machine learning turn the graphic design field on its head again. These kinds of automated tools will arrive on the web first, but print design will change, too, as design-software makers inject machine learning into their layout tools and apps. Wix, another popular website builder, also offers an AI solution: Wix ADI (Artificial Design Intelligence). These web design tools might offer assistance using artificial intelligence, machine learning, and algorithms, but on the whole, they still require hands-on use.