NSF Institute in AI-Augmented Learning

What is key for the cyberlearning community is that this program targets funding for a specific ~$20M institute in AI-Augmented Learning. Amy Baylor is the primary contact person for inquiries. See the solicitation and NSF press release. Proposals deadlines are January 28, 2020 for Institute proposals in one of the six specified themes, and January 30, 2020 for Planning proposals.

The primary focus of an institute in the theme of AI-Augmented Learning includes research and development of AI-driven innovations to radically improve human learning and education writ large – in formal settings (e.g., preK-12, undergraduate, graduate, vocational education), training, on-the-job, and across the lifespan as well as informal settings (e.g., museums, nature centers, libraries; TV/film; crowd-sourcing and citizen science; on-line experiences). This could be in support of cognitive, neural, perceptive and affective processes as well as well-defined learning outcomes in STEM fields, and STEM-enabling content such as literacy, self-regulation, creativity, curiosity, communication, collaboration and social skills.

Augmentation at the level of the individual learner could include intelligent support for personalized and adaptive learning with a focus on learner agency, engagement, and interest-driven exploration. In addition to standard implementations, this could include, for example: AI augmentation for persons with disabilities to provide image interpretation and description while learning; natural language technologies that automatically adapt technical material to the learner’s level of understanding; explanatory machine learning to facilitate learners in exploring new environments; and augmented perception to support learning and communication.

Augmentation in support of collaborative learning could include both human-human and human-computer partnerships with careful attention to the role of human teachers/educators, mentors and collaborators. Such collaborative intelligent learning systems could include, for example, research on the design of conversational agents, intelligent cognitive assistants, supportive multimodal dashboards, or social robots.

An important purpose of this Institute is also to work toward a grand challenge of “Education for All” through research of AI-supported learning systems to radically expand access of learning to all Americans and in response to the rapidly changing landscape of jobs and work. This is aligned with a key recommendation from America’s Strategy for STEM Education to “Expand Digital Platforms for Teaching and Learning” through next-generation learning architectures. Here, research could include the design and implementation of AI technologies through highly adaptable and distributed systems to expand access, equity, and depth of learning across diverse people, institutions, and settings. Advances in data science could provide diagnostic information to support formative, continuous, and summative assessments, drawing upon multimodal and smart and connected data such as from sensors and other cyber-physical systems. Projects should include systematic plans to address algorithmic bias, provide model transparency and support data privacy and security in the support of learning.