Collaborative Research: Middle School Teacher and Student's Experiences with Artificial Intelligence via Computational Cameras

应用于图像和视频的人工智能（AI）技术正在改变社会，广泛应用于许多社会和经济部门。为了培养参与这场技术革命的公民，必须在人工智能素养的基础上为K-12学习者开发学习经验，以便为未来的劳动力做好充分准备。在K-12课程中支持AI概念的教学需要整合多个学科的知识。该项目将通过一个名为ImageSTEM的项目，为6-8年级的中学生开发和实施计算机视觉和机器学习领域的技术注入暑期研讨会。该计划使用计算相机将计算机科学，数学和设计思维整合到视觉AI概念的教学中，同时还从媒体艺术中汲取灵感，以增强科学学习体验。要开发的学习技术模块包括基于物理的视觉的颜色和照明，以了解物理世界和图像传感之间的接口;图像分类管道，教授视觉特征提取，机器学习分类以及数据收集和训练的基础知识;以及将机器学习用于合成生成的视觉媒体。代表性不足的学生，包括来自亚利桑那州凤凰城的西班牙裔/拉丁裔学生和来自格鲁吉亚农村地区的非裔美国学生，将获得对人工智能和STEM的理解并探索未来的职业生涯。ImageSTEM计划将传播有关必要技能和因素的知识和研究成果，这些技能和因素是基于图像的视觉AI技术的基础，用于未来工业，教育，交通和医疗保健领域的STEM劳动力发展。该项目由学生和教师创新技术体验（ITEST）计划资助，该计划支持建立对实践，计划元素，背景和过程的理解的项目，有助于增加学生对科学，技术，工程，信息和通信技术（ICT）研究人员将调查计算相机技术的设计和部署到6-8年级的科学和数学课程中。项目目标包括：（1）在亚利桑那州立大学和格鲁吉亚大学这两个合作机构举办六个讲习班，与36名中学教师共同编写讲习班的课程材料，（2）在本学年期间在现场讲习班和教师课堂上部署和测试，（3）收集证据，以推进和深化该领域对学生在计算机视觉，机器学习和人工智能领域的计算机科学知识和实践发展的理解，以及（4）评估这些经验对教师和学生在参加工作坊后的一年内采用和加强STEM身份的影响。研究认知技能，科学身份和劳动力发展将进行教师和学生参加ImageSTEM计划使用混合方法的方法。基于情感控制理论的调查工具，半结构化访谈，教师反思和研究人员观察将被用来评估STEM身份和STEM教育和职业抱负的变化。项目成果包括教师在学年期间将研讨会课程部署到中学课程中，并对教师的视觉AI技术知识做出贡献，以及学生如何通过视觉AI应用程序学习和参与解决问题的活动。该项目小组将通过在线和多媒体形式向更广泛的学校和教师社区传播基于研讨会和研究活动结果的学习模块，以产生更大的影响。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进行评估，被认为值得支持。

Artificial intelligence (AI) technology applied to images and video is transforming society with broad applications to many social and economic sectors. To develop a citizenry that will participate in this technological revolution, it is essential to develop learning experiences for K-12 learners on the foundations of AI literacy in order to adequately prepare the workforce of the future. Supporting the teaching of AI concepts in the K-12 curriculum requires integrating knowledge from multiple disciplines. This project will develop and implement technology-infused summer workshops for middle school students in grades 6-8 in the areas of computer vision and machine learning through a program called ImageSTEM. The program uses computational cameras to integrate computer science, math and design thinking in teaching visual AI concepts while also drawing inspiration from media arts to enhance science learning experiences. Learning technology modules to be developed include color and lighting for physics-based vision to understand the interface between the physical world and image sensing; image classification pipelines that teach the basics of visual feature extraction, machine learning classification, and the collection and training of data; and use of machine learning for synthetically-generated visual media. Underrepresented students, including predominantly Hispanic/Latino students from Phoenix, Arizona and African-American students from a rural area in Georgia, will gain understanding and explore future careers in AI and STEM. The ImageSTEM program will disseminate knowledge and research findings on necessary skills and factors that underlie image-based visual AI technology for future STEM workforce development in industry, education, transportation, and healthcare. This 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.Researchers will investigate the design and deployment of computational camera technology into the science and math curriculum of grades 6-8. Project objectives include (1) conducting six workshops across the two collaborating institutions of Arizona State University and the University of Georgia, co-creating the curricular material for the workshops alongside 36 middle school teachers, (2) deploying and testing in both the on-site workshops as well as in teachers’ classrooms during the academic school year, (3) gathering evidence to advance and deepen the field's understanding of students' development of computer science knowledge and practices in the areas of computer vision, machine learning, and artificial intelligence, and (4) assessing the impact of these experiences on the adoption and strengthening of STEM identities of teachers and students during the year after participation in the workshops. Research into the cognitive skills, scientific identity, and workforce development will be conducted for teachers and students participating in the ImageSTEM program using a mixed-methods approach. Affect control theory-based survey instruments, semi-structured interviews, teacher reflections, and researcher observations will be used to assess changes in STEM identities and STEM educational and career aspirations. The project outcomes include deployment of the workshop lessons into the middle school curriculum by teachers during the academic year, and contributions to teachers’ knowledge of visual AI technology, and how students learn and engage in problem-solving activities through visual AI applications. The project team will disseminate learning modules based on findings in the workshops and research activities through online and multimedia formats to the broader community of schools and teachers for greater impact.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.