SBIR Phase II: AI-Augmented Mentorship: Bridging Education to Workforce Preparedness

This SBIR Phase II project tackles the critical skills gap through Collaborative Project-Based Learning (CPBL). By seamlessly integrating real-world projects with curriculum, students gain essential soft skills like problem-solving, communication, and teamwork, and hard skills relevant to specific industries, making them adaptable across fields. CPBL provides students with authentic challenges through project-based learning enhanced with industry mentors to foster project management and critical workplace skills essential for professional success. Meaningful projects not only boost engagement and understanding but, when coupled with industry partnerships, offer mini-internships and career guidance. Industry professionals further bolster this collaborative environment by sharing their expertise and facilitating team dynamics, providing invaluable mentorship and real-world context to student learning. The software also empowers academic institutions to evaluate and assess student acquisition of these core workforce skills through embedded assessments within the CPBL framework, ensuring graduates are truly prepared for the demands of the modern workplace. This innovative approach bridges the gap between education and workforce preparedness, empowering students with the skills they need for future success. To bridge the workforce skills gap, this project proposes an AI-powered collaborative project-based learning (CPBL) software platform. We will first establish a baseline by evaluating the platform's core collaborative functionalities – including group formation, progress tracking, and mentorship integration – from the perspectives of students, instructors, and industry mentors. This initial evaluation will utilize a mixed-methods approach, combining quantitative data and qualitative feedback. The findings will serve as a crucial benchmark to assess the added value of the AI component once integrated. The platform itself is designed to address the issue of limited practical skill development by enabling instructors to design CPBL activities within their curriculum, seamlessly group students based on skill sets, and track progress. It further integrates industry mentors for real-world guidance and utilizes embedded assessments to evaluate student acquisition of core competencies. Development will leverage both quantitative and qualitative analysis, with real-time user feedback driving iterative improvements throughout. The platform's AI component, distinct from existing solutions, will be designed to model student profiles, provide personalized learning support, and operate with minimal supervision. This innovative approach, measured through a mixed-methods evaluation post-AI integration, will determine the platform's overall effectiveness in promoting skill development, fostering engagement, and preparing graduates for the modern workforce. 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.