Collaborative Research: CISE-MSI: DP: SCH: Privacy Preserving Tutoring System for Health Education of Low Literacy Hispanic Populations

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。该项目将为低文化程度的拉美裔乳腺癌幸存者提供一名计算机家教。乳腺癌是拉美裔癌症相关死亡的主要原因，尽管研究表明，教育可以极大地缓解压力，提高生活质量，但针对这一群体的教育干预措施很少。这个项目产生的计算机导师将模仿人类导师，教授乳腺癌生存技能和一般乳腺癌知识。由于辅导涉及与幸存者的对话，因此他们可能会泄露敏感的个人信息；因此，重要的是在辅导过程中对信息进行加密，以防止任何隐私泄露。为了使辅导部分有效，必须特别注意自然语言处理模型的使用以及在辅导和/或与技术互动时在目标人群中观察到的行为模式。为了使隐私部分有效，将探索能够高速加密和解密数据的技术，以使交互变得流畅。到目前为止，与学生交谈的计算机家教主要是在大学环境中尝试的高识字率人群，因此他们对低识字率的拉美裔美国人的影响尚不清楚。因此，这个项目将促进我们对设计人工智能支持的导师的影响的理解，以解决低识字率个人在获取信息方面的多样性和差异，以及我们对与复杂的自然语言处理模型实时工作的隐私保护算法的理解。更广泛地说，项目成果将促进少数族裔人口获取信息，并将有助于在参与的以教学为导向的机构建设研究能力和培训少数族裔学生。首先，开发一种新的智能计算机辅导系统，该系统是定制的，通过调整先前研究中为拉美裔乳腺癌幸存者创建的现有内容，使其能够有效地查询并与拉美裔乳腺癌幸存者互动。由于已经表明，许多成年拉美裔美国人的语言，以及目标人群与科技的互动，都比之前认为的更加微妙，所以我们的第一个目标还包括训练自然语言算法，并设计互动，以模拟拉美裔乳腺癌幸存者的互动。第二个目标是开发隐私保护算法，该算法利用健壮的端到端加密通信，并能够以不妨碍与计算机导师交互的速度实时加密和解密分布式数据。这一发展进程的贡献将有三个方面：(1)了解文化和教育在低文化乳腺癌幸存者和智能辅导系统之间互动中的作用；(2)开发一个框架，促进少数族裔人口智能辅导系统的实施；以及(3)为NLP模型培训和对话界面开发准确和低延迟的隐私保护机制。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project will implement a computer tutor for low literacy Hispanic breast cancer survivors. Breast cancer is the leading cause of cancer-related deaths in Hispanics, and although research has shown that education can greatly mitigate stress and improve quality of life, few educational interventions for this population exist. The computer tutor resulting from this project will mimic a human tutor that teaches about breast cancer survivorship skills and about breast cancer in general. Because tutoring involves conversation with the survivor, it is possible that they reveal sensitive personal information; therefore, it is important to encrypt the information in the tutoring session to prevent any privacy breaches. And for the tutoring component to be effective, special attention must be paid to the utilization of natural language processing models as well as models of behavior that are observed in the target population when tutoring and/or interacting with technology. For the privacy component to be effective, techniques that can encrypt and decrypt data at high speeds will be explored to make the interaction fluid. To date, computer tutors that converse with their students have been tried mainly with a highly literate population in college settings, so their impact on low literacy Hispanics is unknown. Therefore, this project will advance our understanding of the impact of designing artificial intelligence powered tutors to address diversity and disparities in the access to information by a subset of low literacy individuals, as well as our understanding of privacy preserving algorithms that work in real-time with complex natural language processing models. More broadly, project outcomes will facilitate access to information for minority populations and will serve to build research capacity and train minority students in the participating teaching-oriented institutions.The project will be carried out with two objectives in mind. First, development of a novel intelligent computer tutoring system that is customized so that it can effectively query and interact with Hispanic breast cancer survivors by adapting existing content that was created for this population in prior research. Because it has been shown that both the language of many adult Hispanics, and target population interactions with technology, are more nuanced than previously thought, our first objective also involves training natural language algorithms and designing interactions that model those of Hispanic breast cancer survivors. The second objective is to develop privacy-preserving algorithms that utilize robust end-to-end encrypted communication and can encrypt and decrypt distributed data in real time at a speed that does not hinder the interactions with the computer tutor. The contributions of this development process will be threefold: (1) to understand the role of culture and education in the interaction between low literacy Hispanic breast cancer survivors and intelligent tutoring systems; (2) to develop a framework that facilitates the implementation of intelligent tutoring systems for minority populations; and (3) to develop accurate and low latency privacy preserving mechanisms for NLP model training and dialogue interfaces.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.