Artificial Intelligence, when properly integrated with innovative educational pathways and effective teachers, offers the potential for powerful predictive tools for developing an effective workforce. It also meets the skills requirements of industry, and enables equitable student success by ensuring learning meets students where they are, instead of assuming all students are on the same path. One significant challenge in education technology is the lack of access to large scale, high quality, labeled datasets that authentically represent communities of people, like learners, employees, job seekers, and even employers. The lack of data significantly limits the ability of the AI community to build and test effective models and tools. Because of the lack of transparency in data (rightfully so, data when applied to AI should be anonymous), quality educational programming supported by comprehensive support services, communities and AI powered interventions are not prevalent in the market.
In response, this session will explore the disparate components in the market that could be aligned to leverage significant scaling of innovative solutions, infrastructure, and tools available and to be developed through collective effort to reduce the friction from enrollment to completion along the learning pathway.