Introducing the Center of Excellence: AI-Empowered Spatial Computing

Introducing the Center of Excellence: AI-Empowered Spatial Computing

Introduction

          Advances in computer technologies continue to revolutionize how humans and machines interact. Developing the next generation of the high-tech workforce includes taking full advantage of emerging technologies like spatial computing, which is the study of computing in spatial, temporal, spatio-temporal spaces across geographic and non-geographic domains that is used to understand and analyze data faster. Spatial computing is already driving business and workforce productivity using popular technologies like artificial intelligence (AI), machine learning (ML), augmented reality (AR), virtual reality (VR), mixed reality (MR), and internet of things (IoT) that can now capture and utilize location data.

          Given all the benefits from the widespread application of spatial computing, it is critical to train a workforce, especially one that includes underrepresented students, with the requisite knowledge and skills to bring spatial computing solutions into the workplace. Postsecondary programs can take years to implement, and, in that time, the leading edge of technology often advances past that knowledge base. Centers of Excellence (CoE) provide students additional education and training to supplement existing degree programs with cutting edge curriculum required for industry’s ever-changing needs. Centers of Excellence are often taught by faculty and industry partners best trained to provide students specialized knowledge to support innovation in advancing technologies as soon as possible. To meet this need for workers in spatial computing, the Center of Excellence: AI-Empowered Spatial Computing will develop postsecondary education programs and work-based learning that provide access to hi-tech careers for students underrepresented in the STEM workforce.

Project Goal

          The goal of this Center for Excellence: AI-Empowered Spatial Computing is to develop a collaborative in partnership with the US Department of Education, University of Missouri-Kansas City, University of Missouri-Columbia, Harris-Stowe State University, and regional advanced technology businesses to train traditionally underrepresented minority students in the most current applications of spatial computing for transition to the workforce. Spatial computing enhances emerging artificial intelligence technologies to simulate human immersive environments in real-time, it is the virtual link between machines and humans. As a graduate of this Center of Excellent: AI-Empowered Spatial Computing, students will gain unique industry desired spatial computing skills through classroom training, collaborative projects, and product innovation using the most advanced spatial computing technology.

Five Project Objectives

Objective 1: Form and grow a consortium of institutions of higher education (IHEs) and industry leaders focused on workplace-ready spatial computing education.

Objective 2: Build AI-empowered learning environments to teach students, especially underrepresented students, spatial computing knowledge, and skills sets.

  • Strategy 2.1: Develop designated spatial computing classes to train the students with necessary knowledge and skill sets in spatial computing.
  • Strategy 2.2: Develop labs where a key spatial computing topic is covered with one or more skill or technology concepts in each lab.
  • Strategy 2.3: Develop scaffolded practices to train students to map concepts from the lab into practice to achieve the desired training outcomes. 
  • Strategy 2.4: Develop a selected set of applications as case studies for students to apply learned topics and concepts.

Objective 3: Collaborate with industry partners to provide paid internships for students in spatial computing technologies. Initially grant funded, the consortium will work with businesses to fund competitive spatial computing internships.

  • Strategy 3.1: Identify more businesses to provide spatial computing internships.
  • Strategy 3.2: Help match students to the right internship positions.
  • Strategy 3.3: Ensure companies provide good mentorships to students.

Objective 4: Contribute to research and theories of learning with a selected set of applications that apply spatial computing and AI technologies.

  • Strategy 4.1: Ensure students understand the functional requirements for each application and know how to implement requirements by using the knowledge learned from classes.
  • Strategy 4.2: Ensure students master each individual topic and concept to implement the corresponding functional requirement for the application.
  • Strategy 4.3: Ensure students can integrate different functionalities to fulfill the requirements of the application.

Objective 5: Add midwestern geographies to the spatial computing landscape.

  • Strategy 5.1: Train students with skill in spatial computing and help them find the internships and job positions in Midwest.
  • Strategy 5.2: Produce well-trained spatial computing workforce in Midwest.
  • Strategy 5.3: Establish spatial computing ecosystem to help students and businesses to grow in Midwest.

Project Significance

          Diversifying the national STEM workforce is a continuous, complex problem that our educational system alone is unprepared to solve. Many have called on American business and industry to do their part in training the next generation to innovate using advancing technologies. The Center of Excellence: AI-empowered Spatial Computing will collaborate with advance technology businesses and industry to produce a training program that to help solve this problem.

Anticipated Findings

          The Center of Excellence: AI-empowered Spatial Computing will take advantage of the expertise of faculty and industry professionals to match the fast pace of innovation using advancing technologies so that students will be ready to join the workforce with desired skill sets. As the Center progresses, it is anticipated that local, regional, and national business and industry organizations will seek to adopt the learning model that increases underrepresented minority students entering the spatial computing workforce with advanced skillsets and experience. The practical learning design highlighted by the case study format as well as the cooperative internship model will prove to be an effective way for companies to build an engaged workforce prepared to contribute almost immediately.

New Learning Strategies

          The Center of Excellence: AI-empowered Spatial Computing project involves the development or demonstration of promising new learning and worker training strategies. To this end, the Center promotes AI skill development and product innovation for underrepresented minority students with regional business and industry partners and is easily replicable by other institutions, funding agencies, and businesses. For example, the Center identifies underrepresented minority students in computing degree programs and provides additional training in spatial computing at a professional entry-level in the form of a micro-credential so that students can contribute to an innovative workforce as part of a structured internship. Also, prior to that school-to-work transition, the Center prepares students to be internship-ready by providing workplace and career development training along with spatial computing micro-credential from the start of the curriculum.

          The Center’s micro-credential will be stored in an academic management system where students can keep track of their completed assignments, projects, courses, and internship milestones.  The micro-credential will consist of learning experiences that thematically connect to each other like classes and degree programs, but with greater emphasis on practical application for the workforce.  The Center’s spatial computing faculty and non-academic partners will finalize the micro-credential curriculum but sample themes are listed below:

  1. Fundamentals of spatial computing: This includes a focus on the mathematical foundations of writing procedural algorithms essential for creating human experience applications for real-time 3D environments.
  2. Human-computer interactions in spatial computing.
  3. Spatial computing design: This covers the principles of designing spatial computing experiences, including user experience design, interaction design, and visual design, including scale, proportion, and perspective to perfect the user’s experience with 3D models, virtual, and augmented reality.
  4. Types of spatial computing devices and their applications: This includes learning how hardware and software components mimic human behaviors with the use of head-mounted displays, sensors, and tracking systems applied to a wide range of applications from gaming and entertainment to education, training, and industrial product development.
  5. Spatial data analysis: This covers geographic information systems (GIS) and spatial data analysis tools for managing, analyzing, and visualizing various types of data.

Teaching Improvements

          The likely improvements to teaching and student achievement will be evident in the successful completion of the collaborative internship. Although student internship preparation is not necessarily new, it is unique to provide student training and internship development in partnership with companies hiring for employees skilled in advanced technologies. In most cases, the company recruits students to work on products with technology that existing employees are proficient in. However, students in this program will likely have advanced technical expertise that companies want, and student interns will be able to use their knowledge with confidence and at times, teaching their work colleagues new skill applications.

          The second teaching improvement is the use of faculty expertise from different IHEs representing the Center of Excellence: AI-empowered Spatial Computing project. In most cases, IHEs use their own faculty regardless of expertise, however, the Center increases the collaboration among the consortium by using experts from both each academic institution and affiliated business to provide the best possible educational experience to its students interested in engaging in AI training. Additionally, this approach to faculty participation increases the pool and expertise of spatial computing instructors. The third teaching improvement that the Center offers is a formalized collaboration between institutions and companies that establishes a unique workforce transition for students from the same spatial computing training program that is easily replicable. By providing students spatial computing training, workplace skill development, and on-the-job experience, the Center maximizes students’ preparation for transition into high-technology level positions and contribute to the much-needed AI workforce.

Dissemination of Results

          The dissemination of Center results will allow others to use the strategies to improve their own institutional effectiveness of spatial computing education as well as increase the participation of underrepresented minority students in the AI workforce. The Center will make available several products to others including the spatial computing and workforce training curriculum, and internship toolkit guide developed for business partners to structure their internships so they can take full advantage of the students’ expertise and time. The Center will share findings from the evaluations of the program’s five objectives, and provide ongoing analysis and lessons learned articles in peer-reviewed journals, conference proceedings, and book chapters. The Principal Investigators will target conferences and journals like: The Association for the Study of Higher Education; Review of Higher Education; American Educational Research Association; Review of Educational Research and Educational Researcher; Research Reports, Journal and NACE Briefs; and NSF INCLUDES National Network. Additionally, the Center will disseminate analysis through the Mid-America Regional Council’s (MARC) and both the Kansas City and St. Louis Regional Chambers of Commerce and other area regional workforce and business promotion associations. A collection of the Center’s work products from the consortium of academic and no-academic partners will be disseminated to the National Association of Regional Councils to assist other regions with broadening participation in their areas spatial computing talent pool. Additionally, the Center’s consortium will maintain a research web presence on OpenScholar described below in the Evaluation Plan.