Project Grant 2243452

STTR PHASE I: ENABLING STUDENT PROJECT COLLABORATION WITH ARTIFICIAL INTELLIGENCE AUGMENTED MENTORSHIP -THE BROADER/COMMERCIAL IMPACT OF THIS SMALL BUSINESS TECHNOLOGY TRANSFER PHASE I PROJECT IS IN IMPROVING BOTH STUDENT LEARNING AND WORKFORCE READINESS THROUGH INTERDEPENDENT LEARNING EXPERIENCES. THE PROJECT WILL CREATE PROJECT-BASED ENVIRONMENTS THAT PROMOTE SKILLS SUCH AS COMMUNICATION, CRITICAL THINKING, PROBLEM SOLVING, TIME MANAGEMENT, CREATIVITY, AND TEAMWORK ? ALL MIRRORING PROFESSIONAL WORK ENVIRONMENTS. THE TECHNOLOGY WILL ALSO PROMOTE DEVELOPMENT OF SKILLS SUCH AS PROJECT MANAGEMENT, WRITING, BUSINESS AND DATA ANALYSIS, DESIGN, AND PRESENTATION. PROJECT COLLABORATION REQUIRES THAT STUDENTS INTERACT FREQUENTLY THROUGHOUT THE PROJECT COMPLETION PROCESS, INCLUDING FREQUENT MENTOR OR TEACHER INTERACTIONS. SUCH AN INTERDEPENDENT ENVIRONMENT CREATES A REAL-WORLD DYNAMIC THAT BETTER PREPARES STUDENTS TO ENTER THE WORKFORCE. THE PLATFORM DEVELOPED BY THIS PROJECT IS LIKELY TO CREATE SIGNIFICANT SOCIETAL IMPACT WHILE PARTICIPATING IN THE FASTEST GROWING E-LEARNING SECTOR. THE PROPOSAL SEEKS TO DEVELOP A COLLABORATIVE COMMUNITY PLATFORM USING PROPRIETARY PROJECT COLLABORATION MODELS INTEGRATED WITH ARTIFICIAL INTELLIGENCE (AI) AUGMENTED MENTORSHIP TO ENHANCE STUDENT WORKFORCE READINESS. THE TECHNOLOGY WILL BE DESIGNED TO PROVIDE THE RIGHT PIECE OF INFORMATION TO THE STUDENTS AND MENTORS AT THE RIGHT TIME. BY ANALYZING AND UNIFYING ALL THE CONTENT UNDER A DOMAIN-SPECIFIC SEMANTIC REPRESENTATION, THE SYSTEM WILL BE ABLE TO AGGREGATE AND ORGANIZE ALL THE CONTENT AND IDENTIFY THE PIECE FOR INTERVENTION THAT IS CONTEXTUALLY MOST USEFUL. TO MAKE PROJECT COLLABORATION AND MENTORSHIP EASIER BETWEEN STUDENTS AND MENTORS IN A TRUSTWORTHY MANNER, MODELING WILL BE DONE UTILIZING MINIMAL SUPERVISION. THIS MODELLING WILL INCLUDE COMBINING CONTEXTUAL EMBEDDINGS FROM LANGUAGE MODELS WITH GRAPH-BASED NEURAL NETWORKS TO CAPTURE INTERACTIONS ACROSS MULTIPLE FACETS. THE TECHNOLOGY WILL BUILD UPON EXPLAINABILITY OF DEEP NEURAL NETWORKS TO PROVIDE AN APPROPRIATE LEVEL OF TRANSPARENCY INTO THE DECISION MAKING, BOTH FOR THE USERS TO LEARN TO TRUST THE PLATFORM, AS WELL AS FOR THE PLATFORM DEVELOPERS TO BUILD SYSTEMS THAT AID IN RELIABLE, TRUSTWORTHY, AND FAIR MENTORING. 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.