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

这项小型企业创新研究（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，以增强一个平台，帮助参与司法的年轻人发展积极变革的保护因素。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，他们中的许多人都有不良的童年经历，这样他们就可以打破再犯的循环。这些改进措施通过提出更相关的成长建议，提供量身定制的、基于优势的反馈，以打破累犯循环，支持青年向生产性社会角色过渡，从而增强青年的权能。该平台具有商业潜力，有机会在美国3143个县部署，每年总共为80万参与司法的青少年提供服务。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。这种转变将减轻我们已经不堪重负的刑罚制度的经济负担，同时释放这些青年为社会作出积极贡献的潜力。该项目整合了最先进的机器学习和大型语言模型，以加强创新的社会成长平台，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，并具有由智能算法控制的动态feed，专门的保护因子旅程，以及以叙事为中心的方法，利用基于力量的社交连接。该项目将有助于改进动态feed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，以支持生活整合故事中基于力量的反馈循环。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.