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

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。该项目将为低识字率的西班牙裔乳腺癌幸存者实施计算机导师。乳腺癌是西班牙裔癌症相关死亡的主要原因，尽管研究表明，教育可以大大减轻压力，提高生活质量，但对这一人群的教育干预很少。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，他们可能会泄露敏感的个人信息;因此，在辅导会话中加密信息以防止任何隐私泄露是很重要的。为了使辅导组件有效，必须特别注意利用自然语言处理模型以及在辅导和/或与技术交互时在目标人群中观察到的行为模型。为了使隐私组件有效，将探索可以高速加密和解密数据的技术，以使交互流畅。到目前为止，与学生匡威的计算机导师主要是在大学环境中与高识字率的人群进行尝试，因此他们对低识字率的西班牙裔的影响是未知的。因此，该项目将促进我们对设计人工智能驱动的导师的影响的理解，以解决低识字率人群在获取信息方面的多样性和差异，以及我们对实时工作的隐私保护算法的理解复杂的自然语言处理模型。 更广泛地说，项目成果将促进少数群体获得信息，并将有助于建设研究能力和在参与的教学型机构培训少数群体学生。 首先，开发一种新的智能计算机辅导系统，该系统是定制的，以便它可以有效地查询和与西班牙裔乳腺癌幸存者进行互动，通过调整现有的内容，在以前的研究中为这一人群创建。因为已经证明，许多西班牙裔成年人的语言以及目标人群与技术的互动比以前想象的更加微妙，我们的第一个目标还涉及训练自然语言算法和设计交互，以模拟西班牙裔乳腺癌幸存者。第二个目标是开发隐私保护算法，利用强大的端到端加密通信，并可以加密和解密分布式数据在真实的时间的速度，不妨碍与计算机导师的互动。这一发展进程的贡献将是三方面的：（1）了解文化和教育在低识字率的西班牙裔乳腺癌幸存者和智能辅导系统之间的相互作用中的作用;（2）制定一个框架，促进少数民族人口智能辅导系统的实施;以及（3）为NLP模型训练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知识价值和更广泛的影响审查标准。

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.