CAREER: Towards Embodied Learning for K-12 Machine Learning (ML) Education

职业：迈向 K-12 机器学习 (ML) 教育的具体学习

• 批准号: 2238675
• 负责人: Zhen Bai
• 金额: $ 73.29万
• 依托单位: University of Rochester
• 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: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238675&HistoricalAwards=false
• 关键词: CAREER; Towards; Embodied; Learning; 12

Children are increasingly impacted by technological advances in artificial intelligence (AI) that provide personalized recommendations regarding the books they read, individualized lessons, career-path plans, and friend circles. As smart learning companions (for example, animated intelligent characters/agents) become popular, children are at risk of overestimating and over-trusting AI given their tendency to anthropomorphize such intelligent systems. This CAREER project investigates the development of novel embodied learning technologies that help K-12 students demystify machine learning (ML), an integral aspect of current approaches to AI. The project will provide hands-on and collaborative learning experiences for children to make sense of the inner workings of ML, similar to how they build, act, and experiment in collaboration with friends. The learning experiences will be designed to be accessible to children, regardless of their math and computing background, with special attention to those from historically underrepresented backgrounds in STEM. The project outcomes will advance an AI-driven society by preparing 21st century learners to become critical thinkers about AI, as both consumers and future creators. It will also promote inclusion in next-generation STEM education by addressing AI inequality in life and work. AI and ML are often presented to children as a black box, focusing on workflows and capabilities (e.g., data training, image and voice recognition) which can lead to inaccurate or oversimplified understandings. Further, many K-12 students lack math and computing backgrounds that are required for understanding abstract Machine Learning (ML) concepts and methods. To address these challenges in understanding abstract ML concepts, this project will explore the design space of 3D and tangible interaction technologies to provide embodied learning experiences that draw upon children's real-life experience of object manipulation, body movement and role-play. Knowledge discovery will be accelerated through a pedagogical agent with curiosity-eliciting prompts to encourage exploratory learning. The learning experiences will be evaluated for impact in supporting knowledge acquisition, self-efficacy and interest in ML with elementary and middle school students. Findings of this project are expected to: (1) deepen knowledge of embodied and exploratory learning in supporting the understanding of abstract and complex STEM concepts through the lens of ML education; and, (2) inform the design of future learning technologies that seamlessly integrate sensorimotor enactment and situated social prompts to make K-12 ML education highly accessible to students with diverse backgrounds and skills.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）技术的进步对儿童的影响越来越大，这些技术可以根据他们阅读的书籍、个性化课程、职业道路规划和朋友圈提供个性化推荐。随着智能学习伙伴（例如动画智能角色/代理）的流行，儿童有可能高估和过度信任AI，因为他们倾向于将这种智能系统拟人化。这个CAREER项目调查了新型具体化学习技术的发展，帮助K-12学生揭开机器学习（ML）的神秘面纱，这是当前人工智能方法的一个组成部分。该项目将为孩子们提供动手和协作的学习体验，让他们了解机器学习的内部工作原理，类似于他们如何与朋友合作构建、行动和实验。学习体验的设计将面向儿童，无论他们的数学和计算机背景如何，特别关注那些在STEM领域历史上代表性不足的背景。该项目的成果将通过培养21世纪的学习者成为对人工智能具有批判性的思考者，成为消费者和未来的创造者，来推进人工智能驱动的社会。它还将通过解决生活和工作中的人工智能不平等问题，促进下一代STEM教育的包容性。人工智能和机器学习通常以黑盒子的形式呈现给儿童，专注于工作流程和功能（例如，数据训练、图像和语音识别），这可能导致不准确或过于简化的理解。此外，许多K-12学生缺乏理解抽象机器学习（ML）概念和方法所需的数学和计算背景。为了解决这些理解抽象机器学习概念的挑战，该项目将探索3D和有形交互技术的设计空间，利用儿童在物体操作、身体运动和角色扮演方面的现实体验，提供具体化的学习体验。知识发现将加速通过教学代理与好奇心引出提示，鼓励探索性学习。本研究将评估这些学习经验对中小学生学习机器学习的知识获取、自我效能感和兴趣的影响。该项目的研究结果有望：(1)通过机器学习教育的视角，加深对具体化和探索性学习的认识，以支持对抽象和复杂STEM概念的理解；(2)为未来学习技术的设计提供信息，这些技术可以无缝地整合感觉运动制定和情境社交提示，使具有不同背景和技能的学生能够高度接受K-12 ML教育。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。