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

儿童越来越多地受到人工智能（AI）技术进步的影响，这些技术进步提供了关于他们阅读的书籍，个性化课程，职业道路计划和朋友圈的个性化建议。随着智能学习伙伴（例如，动画智能角色/代理）变得流行，孩子们有高估和过度信任人工智能的风险，因为他们倾向于将这种智能系统拟人化。这个CAREER项目研究了新型体现式学习技术的发展，这些技术可以帮助K-12学生揭开机器学习（ML）的神秘面纱，这是当前AI方法的一个组成部分。该项目将为儿童提供动手和协作学习体验，以了解ML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。学习经验将被设计为儿童，无论他们的数学和计算背景，特别注意那些从历史上代表性不足的背景在干。该项目的成果将通过培养21世纪世纪的学习者成为消费者和未来创造者对人工智能的批判性思考，推动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作为一个黑盒子呈现给孩子，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度简化的理解。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ML概念的挑战，该项目将探索3D和有形交互技术的设计空间，以提供具体的学习体验，这些体验借鉴了儿童对对象操作，身体运动和角色扮演的真实体验。知识发现将通过一个教学代理人与好奇心引发提示，以鼓励探索性学习加速。将评估学习经验对支持中小学生知识获取，自我效能和ML兴趣的影响。本项目的研究结果有望：（1）通过机器学习教育的透镜，深化对体现和探索性学习的认识，以支持对抽象和复杂的STEM概念的理解;并且，在本发明中，（2）为未来学习技术的设计提供信息，这些技术将感觉运动制定和情境社交提示无缝整合，使K-12 ML教育对具有不同背景和技能的学生来说非常容易获得。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.