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，并广泛应用于许多社会和经济部门。为了培养将参与这场技术革命的公民，至关重要的是在人工智能扫盲的基础上为K-12学习者发展学习经验，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。该项目将通过一项名为ImageSTEM的计划，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的暑期研讨会。该项目使用计算摄像头在教学视觉人工智能概念时整合计算机科学、数学和设计思维，同时从媒体艺术中汲取灵感，以增强科学学习体验。将开发的学习技术模块包括：颜色和照明，用于基于物理的视觉，以了解物理世界和图像传感之间的界面；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的视觉媒体。代表不足的学生，包括主要来自亚利桑那州凤凰城的西班牙裔/拉丁裔学生和来自佐治亚州农村地区的非裔美国学生，将获得理解，并探索未来在人工智能和STEM领域的职业。ImageSTEM项目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人工智能技术的基础，用于未来STEM在工业、教育、交通和医疗保健领域的劳动力发展。该项目由学生和教师创新技术体验计划(ITEST)资助，该计划支持建立对实践、计划要素、背景和过程的理解的项目，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。研究人员将调查计算相机技术在6-8年级科学和数学课程中的设计和部署。项目目标包括(1)在亚利桑那州立大学和佐治亚大学两个合作机构举办六个研讨会，与36名中学教师共同创建研讨会的课程材料，(2)在学年期间在现场研讨会和教师课堂上部署和测试，(3)收集证据，促进和加深现场学生对计算机视觉、机器学习和人工智能领域的计算机科学知识和实践的理解，以及(4)在参加讲习班后的一年内，评估这些经验对采纳和加强教师和学生的STEM身份的影响。参加ImageSTEM项目的教师和学生将使用混合方法对认知技能、科学身份和劳动力发展进行研究。基于情感控制理论的调查工具、半结构访谈、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，以及帮助教师了解视觉人工智能技术，以及学生如何通过视觉人工智能应用程序学习和参与解决问题的活动。项目团队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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.