SBIR Phase I: Using ChatGPT and Machine Learning to Power Positive Change among Justice Involved Youth

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是测试人工智能（AI）的好处，以加强一个平台，帮助参与正义的青年发展积极变化的保护因素。该项目使用先进的机器学习和大型语言模型技术来优化平台的有效性，并支持这一弱势群体，其中许多人有不良的童年经历，使他们能够打破重新犯罪的循环。这些增强功能通过创建更相关的成长建议来增强青年的权能，提供量身定制的基于优势的反馈，以打破累犯的循环，并支持向生产性社会角色的过渡。该平台具有商业潜力，有机会在美国3，143个县部署，每年为80万名参与司法的青年提供服务。成功的实施可能会改变这些青年的生活轨迹，使他们能够做出积极的生活改变。这一转变将减轻我们已经捉襟见肘的刑罚系统的经济负担，同时释放这些青年为社会做出积极贡献的潜力。该项目集成了最先进的机器学习和大型语言模型，以加强创新的社会增长平台，使参与正义的青年能够做出积极的生活改变。该平台以研究验证的增长周期为基础，并具有由智能算法管理的动态提要，专门的保护因素旅程以及以叙事为中心的方法，该方法利用基于实力的社交连接。该项目将促进完善动态提要背后的算法架构，以提高用户参与度，从而促进保护因素。大型语言模型将被整合，作为一种智能增强机制，以加强生命整合故事中基于力量的反馈回路。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to test the benefits of artificial intelligence (AI) to enhance a platform which aids justice-involved youth to develop protective factors for positive change. The project uses advanced machine learning and large language model technologies to optimize the platform's effectiveness and support this vulnerable group, many of whom have adverse childhood experiences, so that they can break the cycle of re-offending. These enhancements empower youth by creating more relevant growth recommendations offering tailored, strengths-based feedback to break the cycle of recidivism and support the transition into productive societal roles. The platform has commercial potential with opportunities for deployment across 3,143 U.S. counties, which collectively serve 800,000 justice-involved youth annually. Successful implementation may alter the life trajectories of these youth, such that they can make positive life changes. This transformation would lessen the economic burden on our already stretched penal systems, while unlocking the potential of these youth to contribute positively to society. This project integrates state-of-the-art machine learning and large language models to fortify an innovative social growth platform, enabling justice-involved youth to make positive life changes. The platform is underpinned by a research-validated growth cycle, and features a dynamic feed governed by intelligent algorithms, specialized protective factor journeys, and a narrative-centric approach that leverages strength-based, social connectivity. The project will facilitate the refinement of the algorithmic architecture behind the dynamic feed to enhance user engagement, thereby promoting protective factors. Large language models will be integrated to serve as an intelligence-augmented mechanism to bolster the strength-based feedback loop within life integration stories.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.