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EAGER: SaTC-EDU: Designing and Evaluating Curricular Modules for Inclusive Integration of Artificial Intelligence into Cybersecurity

EAGER: SaTC-EDU: Designing and Evaluating Curricular Modules for Inclusive Integration of Artificial Intelligence into Cybersecurity
EAGER:SaTC-EDU:设计和评估课程模块,以将人工智能全面融入网络安全
批准号:
2039606
负责人:
Mark Finlayson
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31

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中文摘要
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英文摘要
In today’s ever-changing threat landscape, artificial intelligence (AI) techniques have become a key technology for cybersecurity researchers and practitioners. Integrating AI into cybersecurity curricula is increasingly necessary to better prepare the future cyber workforce but is also a serious challenge. AI and cybersecurity are each difficult areas of study, appeal to different types of students, and individually require significant commitments within a fixed number of credit hours. Moreover, these challenges pose further barriers for minoritized groups (e.g., Hispanics), many of whom are already on the wrong side of the "digital divide". This EAGER project proposes to address the following questions: how can AI be integrated into an already packed cybersecurity curriculum, and how can this be done without further disadvantaging minoritized groups? Although this project will focus on minoritized groups, the educational modules will be designed to be culturally mindful and inclusive of a broader population and therefore will extend the project’s impact to any potential computer scientist who does not conform to the stereotypes and normative expectations of the field. The results of this study have the potential to expand and redefine who pursues cybersecurity, as well as how we integrate it into the curriculum. This EAGER project will pursue three thrusts designed to address the difficult problem of integrating AI effectively into cybersecurity curricula while attending carefully to the effect of these integrations on minoritized groups, focusing specifically on Hispanic students. First, the project team will design highly adaptable AI curricular modules that can easily be leveraged by non-AI cybersecurity educators and inserted into existing cybersecurity courses, with each module associated with a suite of potential insertion points. Second, these modules will include machine learning (ML) and natural language processing (NLP). Topics such as NLP have been shown to increase the appeal of computer science for diverse populations. Last, the project team will evaluate the effectiveness of these modules at the curriculum-level within existing cybersecurity programs that serve a diverse population. The specific research questions to be addressed will include: (1) How did instructors perceive the content’s efficacy? (2) What factors influence an instructor's participation in curricular change? (3) What are the obstacles or considerations for AI integration into cybersecurity curricula? (4) How do students perceive content efficacy? (5) What (if any) influence do the modules have on interest, engagement, and identity? This project answers the call for advances in education research at the intersection of cybersecurity and AI through a fully interdependent and integrated approach that draws on the expertise of the team. It also leverages widely accepted theoretical frameworks and methods to evaluate and assess the effectiveness of the work to ensure high impact and potential for future scale-up.This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.
期刊论文(1)
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会议论文
DOI: --
发表时间: 2022
期刊: 2022 ASEE Annual Conference & Exposition
影响因子: --
作者: [ARIS, A.]
通讯作者: ARIS, A.
CAREER: Learning Multi-Level Narrative Structure
  • 批准号:
    1749917
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $55.0万
  • 财政年份:
    2018
  • 负责人:
    Mark Finlayson
  • 依托单位:
CI-P: Toward Unified Tool Support for Linguistic Corpus Annotation
  • 批准号:
    1536043
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.13万
  • 财政年份:
    2014
  • 负责人:
    Mark Finlayson
  • 依托单位:
CI-P: Toward Unified Tool Support for Linguistic Corpus Annotation
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