Sbir phase II: advancing mentorai to improve academic and workforce outcomes

The National Science Foundation awarded a cooperative agreement totaling $1,051,777 to Academic Web Pages, Inc. on August 1, 2026, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) for SBIR Phase II development of an AI-enabled mentoring assistant.

Academic Web Pages will develop and validate a proprietary AI system designed to augment human mentors and scale high-quality, personalized mentoring across educational and workforce development settings. The platform integrates established mentoring science with contextual data from ongoing mentor-mentee interactions while preserving human-centered engagement. Technical work includes refining domain-specific algorithms for generating tailored mentoring guidance aligned with peer-reviewed best practices, establishing a validation framework to assess accuracy and reliability, and evaluating system performance through controlled deployment. The research methodology combines iterative model training using protected and privacy-preserving mentoring datasets with retrieval mechanisms that constrain outputs to validated resources. Anticipated results include a validated AI system capable of delivering consistent, context-aware support and a reproducible methodology for assessing AI-assisted mentor effectiveness.

The period of performance runs through July 31, 2028, with work performed in Beach Haven, New Jersey. The assistance type is a cooperative agreement. Academic Web Pages, Inc. is classified as a woman-owned small business and self-certified small disadvantaged business.

Abstract

The broader and commercial impact of this sbir phase II project is to advance the accessibility and effectiveness of mentoring for students and early career professionals across educational and workforce development settings. These capabilities enable institutions to expand access to high-quality support while preserving the relational and developmental core of effective mentoring. This small business innovation research (sbir) phase II project addresses the challenge of scaling high-quality, personalized mentoring by leveraging artificial intelligence to augment human mentors. Traditional mentoring systems are constrained by limited mentor capacity and administrative burden, restricting institutions? Ability to provide timely, evidence-based support to students and early career professionals. The core intellectual merit of this project is the development and validation of a proprietary, secure, and responsibly governed AI-enabled mentoring assistant that integrates established mentoring science with contextual data derived from ongoing mentor-mentee interactions to support decision-making while preserving human-centered engagement. The research objectives include refining domain-specific algorithms for generating tailored mentoring guidance aligned with peer-reviewed best practices, establishing a validation framework to assess accuracy, reliability, and responsible use, and evaluating system performance through controlled deployment. The technical approach combines iterative model training using protected and privacy-preserving mentoring datasets, retrieval mechanisms that constrain outputs to validated resources, and quantitative and qualitative evaluation of mentor efficiency, interaction quality, and engagement outcomes. Anticipated results include a validated AI system capable of delivering consistent, context-aware support, a reproducible methodology for assessing AI-assisted mentoring systems, and empirical evidence demonstrating improved scalability and effectiveness of mentoring without compromising relational integrity or data security. 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.- subawards are not planned for this award.