Collaborative Research: Developing Online Laboratories for Computer Science Students to Learn How to Build Accessible Software and to Use Artificial Intelligence/Machine Learning

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This project aims to serve the national interest by developing flexible online laboratories to help undergraduate computer science students learn important skills, including design of accessible software and use of artificial intelligence. Specifically, the project aims to create, evaluate, and disseminate self-contained computer science laboratories about how to develop accessible software for individuals with cognitive impairments, hearing loss, vision impairment, and other disabilities. It also aims to create similar online laboratories to help computer science students learn about artificial intelligence/machine learning. In design of the online labs, the project intends to develop a modular format that can identify beneficial features for both students and faculty across multiple topics. The labs will be based on real-world scenarios, will use an experiential learning model, and will address foundational topics from multiple angles. Including these increasingly essential topics in curricula can be challenging, especially for institutions with resource constraints. The project’s online lab approach can help by providing learning resources that are self-contained and easily adoptable. In fact, adopting the labs will require only a web browser, and the project will provide instructors with supplemental materials such as lecture slides and instructional videos to help them successfully teach the topic. The project’s research efforts will focus on the design and dissemination of the online labs to better understand how to facilitate adoption at other institutions. The research effort will also examine how inclusion of empathy-building content (in the accessibility labs) and ethics content (in the artificial intelligence labs) impact student users. This collaborative project from Rochester Institute of Technology and Bethune-Cookman University will develop ten self-contained labs for undergraduate computer science students: five addressing artificial intelligence/machine learning and five addressing the development of accessible software. Beyond the development of these labs, major project goals include: (1) the development of a unified framework for self-contained labs that can be applied to other areas of computer science; (2) evaluation of the specific labs and the broader framework, considering both student users and faculty adopters; and (3) broad dissemination of the labs and the shared framework, including robust digital training opportunities. The project’s mixed-methods research and evaluation plans will collect data from student focus groups and surveys and instructor interviews and assessments of student learning that will be compared to outcomes of students engaged in courses that use different, established mechanisms to teach similar content. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is an Engaged Student Learning project through which the IUSE program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.