Collaborative Research: Integrating Language-Based AI Across the High School Curriculum to Create Diverse Pathways to AI-Rich Careers

合作研究:将基于语言的人工智能整合到高中课程中,为人工智能丰富的职业创造多样化的途径

基本信息

  • 批准号:
    2241671
  • 负责人:
  • 金额:
    $ 38.59万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-06-01 至 2026-05-31
  • 项目状态:
    未结题

项目摘要

Artificial Intelligence (AI) is transforming numerous industries and generating enormous wealth. K-12 is the critical stage for youth to develop knowledge of and interest in AI. This project will leverage the interdisciplinarity of AI to create learning opportunities for secondary students from diverse backgrounds. Focusing on natural language-based AI, this project will develop and research a novel AI Across the Curriculum program that integrates AI concepts and practices into the existing high school curriculum. The project team will develop and test a two-hour introductory module and three five-hour modules for mathematics, English language arts (ELA), and history, as well as a 60-hour professional development program for teachers to develop the competencies required to implement the modules. Teachers in math, ELA, and history will implement the modules in a coordinated fashion to offer learning experiences that are coherent across the different disciplines to their students. During the project, 12 teachers and 900 students will directly benefit from participation in the program. The output of the project will advance national prosperity through AI workforce development by enabling high schools to provide high-quality AI education to all students, especially African Americans, Latinx, and females, who are the underrepresented and underserved groups in the field of AI. The project will be led by an interdisciplinary team of AI developers and educators, STEM and humanities educators, learning scientists and designers, and experts on diversity, equity, and inclusion at the Concord Consortium, Carnegie Mellon University, and North Carolina State University. The team will partner with the San Joaquin County Office of Education in California and the Maryland Center for Computing Education and work closely with two school districts, one in CA and one in MD, that serve student populations underrepresented and underserved in the field of AI. Researchers will address three research questions: 1) How do students’ social and disciplinary identities shape their participation in learning of AI knowledge and AI-rich careers? Guided by the intersectional identity theory, the project will capture eight focal students’ learning processes with repeated interviews, video, audio, and screencast recordings, and computer logs. These data will be analyzed using the personal narratives framework and ethnomethodological and conversation-analytic approaches. 2) What and how are new ideas generated by teachers as they seek to coordinate their efforts to integrate AI across the curriculum? Based on the community of practice theory, the project will capture teachers’ idea generation and transaction processes with Professional Development (PD) recordings, online communications, and interviews. These data will be analyzed using the idea authorship framework. 3) To what extent, for whom, and under what conditions does the AI Across the Curriculum program support students to develop knowledge of and interest in AI-rich careers? The demographic and academic backgrounds of 900 students and 12 teachers will be collected via surveys to determine the impact of this approach. An AI & Machine Learning Core Concepts Questionnaire and an AI-Rich Careers Questionnaire will be administered before and after the curriculum. These data will be analyzed quantitatively to determine to what extent, for whom, and under what conditions the modules are beneficial. Through research publications and professional learning resources, the project will increase the capacity of educators and researchers to advance AI education. All technologies, curriculum modules, assessments, and PD materials will be freely available to the public.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.
人工智能(AI)正在改变许多行业并创造巨大的财富。K-12是青少年发展人工智能知识和兴趣的关键阶段。该项目将利用人工智能的跨学科性,为来自不同背景的中学生创造学习机会。该项目专注于基于自然语言的人工智能,将开发和研究一种新的AI跨课程计划,将AI概念和实践融入现有的高中课程。该项目团队将开发和测试一个两小时的入门模块和三个五小时的数学,英语语言艺术(ELA)和历史模块,以及一个60小时的专业发展计划,为教师发展所需的能力来实施这些模块。数学,ELA和历史教师将以协调的方式实施这些模块,为学生提供跨不同学科连贯的学习体验。在该项目期间,12名教师和900名学生将直接受益于参与该计划。该项目的产出将通过人工智能劳动力发展促进国家繁荣,使高中能够为所有学生提供高质量的人工智能教育,特别是非洲裔美国人,拉丁裔和女性,他们是人工智能领域代表性不足和服务不足的群体。该项目将由一个跨学科团队领导,该团队包括人工智能开发人员和教育工作者,STEM和人文教育工作者,学习科学家和设计师,以及康科德联盟,卡内基梅隆大学和北卡罗来纳州州立大学的多样性,公平性和包容性专家。该团队将与加州的圣华金县教育办公室和马里兰州计算教育中心合作,并与两个学区密切合作,一个在加利福尼亚州,一个在马里兰州,为在人工智能领域代表性不足和服务不足的学生群体提供服务。研究人员将解决三个研究问题:1)学生的社会和学科身份如何塑造他们参与学习人工智能知识和人工智能丰富的职业?在交叉认同理论的指导下,该项目将通过重复的访谈、视频、音频和屏幕播放录音以及计算机日志来捕捉八名重点学生的学习过程。这些数据将使用个人叙述框架和民族方法学和对话分析方法进行分析。2)教师在寻求协调他们的努力以将AI整合到整个课程中时,会产生什么以及如何产生新的想法?该项目以实践社区理论为基础,通过专业发展(PD)录音、在线交流和访谈,捕捉教师的想法产生和交易过程。这些数据将使用想法作者框架进行分析。3)在何种程度上,为谁,在什么条件下,AI跨课程计划支持学生发展知识和兴趣,在AI丰富的职业生涯?将通过调查收集900名学生和12名教师的人口统计和学术背景,以确定这种方法的影响。AI机器学习核心概念问卷和AI丰富的职业问卷将在课程之前和之后进行。将对这些数据进行定量分析,以确定这些模块在多大程度上、对谁以及在什么条件下是有益的。通过研究出版物和专业学习资源,该项目将提高教育工作者和研究人员推进人工智能教育的能力。所有的技术,课程模块,评估和PD材料将免费提供给公众。这个奖项反映了NSF的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

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Shiyan Jiang其他文献

What would the matrix do?: a systematic review of K-12 AI learning contexts and learner-interface interactions
矩阵会做什么?:对 K-12 人工智能学习环境和学习者界面交互的系统回顾
Data wrangling practices and collaborative interactions with aggregated data
数据整理实践以及与聚合数据的协作交互
Students’ perceptions of using ChatGPT in a physics class as a virtual tutor
学生对在物理课上使用 ChatGPT 作为虚拟导师的看法
Design and Application of Automatic Feedback Scaffolding in Forums to Promote Learning
论坛自动反馈脚手架促进学习的设计与应用
  • DOI:
    10.1109/tlt.2022.3156914
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Qi Wang;Carolyn Penstein Rose;Ning Ma;Shiyan Jiang;Haogang Bao;Yanyan Li
  • 通讯作者:
    Yanyan Li
Visualizing qualitative data: unpacking the complexities and nuances of technology-supported learning processes
可视化定性数据:揭示技术支持的学习过程的复杂性和细微差别

Shiyan Jiang的其他文献

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