AI Institute for Adult Learning and Online Education (ALOE)

Based on decades of study of human learning, it is known that much of human learning is mediated by others (guided by teachers), is a social/collaborative process, and uses a range of cognitive strategies. Childhood and adult learning lie on a spectrum of cognitive abilities. Two characteristics that distinguish adult learning from childhood learning are first, while much of K-12 learning pertains to closed, well-defined problems with clear answers, adult learning - especially adult learning in the workplace - often addresses open-ended, ill-defined problems that may have no clear answer or that may admit multiple answers. Second, while most K-12 learning is general-purpose and teacher-guided, adult learning (especially in the workplace) is task-specific and self-directed, hence the proliferation of educational resources (often online) in support of adult learning. Georgia Research Alliance (GRA) will establish a National Artificial Intelligence Institute titled “NSF AI Institute for Adult Learning and Online Education (ALOE)”, the goal of which is to make education more equitable through enhanced availability, greater affordability, and enhanced potential for success. Enhanced availability is to be achieved through the use of online educational resources for blended learning; greater affordability is to be accomplished through low-cost virtual teaching assistants that amplify teachers’ reach, while enhanced potential for success is to be achieved through cognitive and social support provided by virtual teaching assistants. The Artificial Intelligence (AI) project aims to serve the national interest through the development of transformative AI-driven models of online adult learning that blend higher and continuing education to radically improve human learning. A comprehensive and well-organized plan is proposed that uses AI simultaneously to transform online adult learning and to drive foundational research in AI. GRA is a 30-year-old private, nonprofit corporation that collaborates with state government, business community, and university system to advance science and technology that generates direct economic benefits. The ALOE AI Institute involves a large interdisciplinary research team that includes two non-profit organizations (Georgia Research Alliance, IMS Global), three industrial companies (Boeing, IBM, Wiley) and seven educational institutions (Arizona State University, Drexel University, Georgia Institute of Technology, Georgia State University, Harvard University, Technical College System of Georgia, University of North Carolina at Greensboro). Additionally, Accenture, the multinational consulting company, is partnering with NSF to provide funding for the Institute.Overall, the goals of the project are consistent with NSF AI Institutes’ vision to advance foundational research, conduct use-inspired research, and grow the next generation of diverse talent by leveraging multiple organizations. With regard to foundational research, major synergistic contributions are anticipated in four areas:(i) cognitively-grounded AI (AI virtual assistants that are grounded in cognitive theories of adult learning such as active learning); (ii) AI-based personalization at scale (collection of learning data from millions of adult learners and development of novel machine learning and natural language processing techniques for analyzing the data); (iii) human-AI Collaboration: development of novel techniques for interactive visualization that enables teachers and learners to build a mutual theory of mind; (iv) responsible AI: discovery of principles for designing sociotechnical systems for online adult education in which AI agents work ethically to benefit humans. With regard to use-inspired research, responsible fundamental AI research grounded in theories of human cognition and learning will be conducted. At least two distinct thrusts are in place: (i) development of AI teaching and learning assistants that enhance cognitive, teacher and social presence in online adult learning to help make it efficient and effective; (ii) learning analytics for personalization of large-scale online learning for adult education. The methodology employed, learning engineering, is an iterative design approach that brings the rigor of engineering to the discipline of education. Beginning with human-centered design of AI technologies, where the human could be a learner, a teacher, or a different stakeholder in the learning process, the process continues with the deployment of AI technologies and collection and analysis of large-scale data about learners and learning. The process then continues to the assessment of learning behaviors and outcomes followed by the refinement of human-centered AI technologies. A detailed plan is provided for assessment of impact on both learning and teaching through a mixed methods approach. Randomized controlled trials will be used to evaluate how the use of AI technologies facilitates and impacts learning. Quasi-experimental studies will be carried out to compare learning effectiveness and efficiency of online versus in-person classes. A plan for evaluation of the process of project execution is to be overseen by an experienced evaluator who will employ a values-engaged, educative approach which seeks to capture the viewpoints, interests, and values of all stakeholders, including those often underrepresented in the evaluation context. The National Artificial Intelligence Institutes Program is a multi-agency effort to establish institute-scale AI research with the potential for long-term payoffs in AI. In addition to advancing foundational research and conducting use-inspired research, the program supports efforts to grow the next generation of AI talent, enhance multidisciplinary AI research, leverage multiple organizations and provide a nexus point for collaborative efforts in AI research and development.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.