Learning to create Intelligent Solutions with Machine Learning and Computer Vision: A Pathway to AI Careers for Diverse High School Students

人工智能正在推动我们国家的经济发展，重塑未来的就业和劳动力。K-12教育正面临着培养学生人工智能能力和为未来劳动力做好准备的挑战。学生需要有机会在真实的人工智能内容和体验中学习人工智能概念。该项目将利用图像分类作为一个典型的人工智能应用领域，为高中生和教师的专业发展提供有意义的学习环境。研究人员将与高中计算机科学教师合作，并从密西西比州的五个学区招募不同的高中学生，这些学区包括部落和农村学校，那里有大量未被充分代表的少数民族学生。将提供为期一年的校外时间（OST）计划，让高中教师和学生参与准备图像数据，使用机器学习（ML）训练图像模型，以及创建可以执行智能视觉任务的系统。项目活动提供人工智能技术授权和身份确认空间，旨在帮助不同的学生发展认知和非认知技能，在未来坚持大学生活，并有可能继续从事计算和人工智能方面的职业。15名高中教师和60名高中生将参加创新的AI/ML教育活动。项目团队将围绕项目中人工智能/机器学习经验和学习成果探讨以下研究问题：(1)项目经验对学生的机器学习知识和能力，以及他们追求人工智能或人工智能相关职业的兴趣和动机有何影响？(2)学生的机器学习能力发展轨迹在项目中是如何演变的，影响这些轨迹的因素是什么？(3)为不同的高中生提供可访问、公平和包容的学习体验，以培养机器学习能力和人工智能道德，设计特征是什么？(4)从应用的角度和人工智能伦理的角度来看，项目经历如何影响高中教师对ML教学的假设、价值观和能力？采用融合并行混合方法研究设计，项目组将在整个项目过程中同时收集定量和定性数据。定量数据包括学生认知和情感学习结果数据，定性数据包括学生和教师访谈、学生小组工作视频记录和学生工件。定量和定性数据都将是纵向的，在为期一年的项目中，对每一组高中教师和学生进行重复的数据收集。定量数据的分析将采用单因素方差分析、重复测量方差分析和弗里德曼秩和检验，定性数据的分析将采用演绎归纳专题分析和内容分析以及矩阵法。该项目的成果包括为计算机科学教师提供的高中人工智能/机器学习能力发展课程和附带的教学指南和资源，这些将在该项目的网站上公开发布，并在会议和出版物上传播给STEM教育工作者和研究人员的广大受众。这项开发和测试创新项目由“面向学生和教师的创新技术体验”（ITEST）项目资助，该项目支持建立对实践、项目要素、背景和过程的理解，有助于提高学生对科学、技术、工程和数学（STEM）以及信息和通信技术（ICT）职业的知识和兴趣。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。

AI is driving our nation’s economic development and reshaping future jobs and workforce. K-12 education is facing the challenge of developing students’ AI competency and preparing them for the workforce of the future. Students need opportunities to engage in learning AI concepts that are situated in authentic AI content and experiences. This project will utilize image classification as an exemplary AI application domain to provide meaningful learning contexts for both high school students and teachers’ professional development. Researchers will partner with high school computer science teachers and recruit diverse high school students from five school districts in Mississippi that include tribal and rural schools with large, underrepresented minority student populations. A year-long out-of-school-time (OST) program with hands-on innovative AI technology experiences will be offered to engage high school teachers and students in preparing image data, training image models using machine learning (ML), and creating systems that can perform intelligent vision tasks. The project activities offer AI technology empowerment as well as identity-affirming spaces that aim to help diverse students develop both cognitive and non-cognitive skills, persist in college in the future, and potentially move on to careers in computing and artificial intelligence.Fifteen high school teachers and sixty high school students will participate in innovative AI/ML education activities. The project team will explore the following research questions surrounding the AI/ML learning experiences and learning outcomes in the project: (1) What is the impact of the program experiences on students’ ML knowledge and competencies, as well as their interest and motivation to pursue AI or AI related careers? (2) How do students’ ML competency development trajectories evolve in the program and what are the factors shaping the trajectories? (3) What are the design characteristics that support accessible, equitable, and inclusive learning experiences for diverse high school students to develop ML competencies and AI ethics? (4) How do the program experiences affect high school teachers’ assumptions, values, and competencies of teaching ML from both an application perspective and an AI ethics perspective? Adopting the convergent parallel mixed methods research design, the project team will collect quantitative and qualitative data concurrently throughout the project. Quantitative data include student cognitive and affective learning outcome data, and qualitative data include student and teacher interviews, student group-work video recordings, and student artifacts. Both the quantitative and qualitative data will be longitudinal with repeated data collection during the year-long program for each cohort of high school teachers and students. One-way ANOVA, repeated measures ANOVA, and Friedman rank-sum tests will be used to analyze the quantitative data, and deductive and inductive thematic analysis and content analysis as well as matrix methods will be used to analyze the qualitative data. The outcomes of the project include the resulting high school AI/ML competency development curriculum and accompanying teaching guides and resources for computer science teachers, which will be made publicly available on the project’s website and disseminated to a large audience of STEM educators and researchers at conferences and publications. This developing and testing innovations project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.