PFI-TT: Using Artificial Intelligence to Identify Additional Educational Resources Based on What was Discussed in Class

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI- TT) project is to create the Contextual Linkaging for Undergraduate Education (CLUE) Service that analyzes transcriptions from class recordings and automatically identifies related resources in a student’s learning ecosystem. This system will allow students to search class videos to find when a concept was discussed, indicate moments during a class when they are confused, and subsequently receive additional information on the confusing topics. By automatically identifying when key concepts were discussed during class, this project will enable educational platforms to deliver content to the learner personalized to the material presented during each class session. Instructors will receive feedback on which resources students find valuable for each topic. The CLUE Service will be designed to help educational resource providers by providing contextual linkages to resources in other educational platforms. University Chief Information Officers will find value as a way to contextually integrate the numerous learning resources they support. The proposed project builds on former NSF-funded projects and requires technical expertise in natural language processing and design of educational technologies. The CLUE Service will use computer-generated transcriptions from class captures to identify key terms and phrases discussed during class sessions. The combination of analyzed key terms and corresponding timestamps will allow contextual linkages to be created between moments in class captures and other educational resources. The envisioned prototype will be a subscribable Application Programming Interface (API) that will allow contextual linkages between educational services. Technical challenges for this project include how to best design unsupervised machine learning to either identify keywords and/or topics in a class session along with the best timestamp(s) in class to represent those keywords or topics and how to provide a resource that can be used with any video delivery system so the student can make known when they would like additional information during a streaming or recorded class session. Initially, the results of this research will be made available to participating instructors or their assistants with tools that will allow them to assess the potential value of the recommended resources to their students. This feedback will inform improvements in resource selection for the CLUE service. 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.