Human/AI Co-Orchestration of Dynamically-Differentiated Collaborative Classrooms

动态差异化协作课堂的人机/人工智能协同编排

• 批准号: 1822861
• 负责人: Vincent Aleven
• 金额: $ 75万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822861&HistoricalAwards=false
• 关键词: Human; AI; Co; Orchestration; Dynamically

This project will create and demonstrate new technology that supports dynamically-differentiated instruction for the classroom of the future. This new vision centers on carefully-designed partnerships between teachers, students, and artificial intelligence (AI). AI-powered learning software will support students during problem-solving practice, providing either individual guidance (using standard intelligent tutoring technology) or guidance so students can effectively collaborate and tutor each other. These learning activities are constantly adjusted to fit each student's needs, including switching between individual or collaborative learning. The teacher "orchestrates" (instigates, oversees, and regulates) this dynamic process. New tools will enhance the teacher's awareness of students' classroom progress. The goal is to have highly effective and efficient learning processes for all students, and effective "orchestration support" for teachers. We will implement and test this vision in the context of AI-enhanced mathematics learning in middle school. The proposed work will greatly enhance current understanding of how to design effective AI-based "co-orchestration" tools that draw on complementary strengths of teachers, students/peers, and AI agents to make the vision of the dynamically-differentiated classroom feasible. It will provide insight into the new classroom dynamics that arise. The work may ultimately contribute to more individualized K-12 education. The work will create a testbed that could be used to explore and rigorously test a wide range of interesting hypotheses regarding co-orchestration and dynamic differentiation of individual and collaborative learning. Effective orchestration of dynamically-differentiated instruction poses significant challenges in terms of design and technical implementation. Although existing AI-based learning technologies support forms of dynamic differentiation of instruction, they tend not to support dynamic combinations of individual and collaborative learning; in fact, most only support one of these two learning modes. In addition, existing teacher support tools tend to focus only on enhancing teacher awareness, not on supporting teachers' in-the-moment decision-making and action, and not on supporting dynamic interleaving of individual and collaborative learning. In the proposed work, we tackle this challenge by integrating and extending four strands of work: intelligent tutoring systems technology; a learning environment to support combinations of individual and collaborative learning; adaptive technology support for mutual peer tutoring; and a mixed reality tool ("smart glasses") to support teacher/AI co-orchestration. Building on this foundation, we create and demonstrate technology support for dynamically-differentiated instruction by three strands of work. First, we create AI-based tutoring software capable of supporting both individual learners and students doing mutual peer tutoring. Second, we develop a tool to support the intelligent, real-time co-orchestration, between students, teachers, and AI agents, of dynamically differentiated combinations of collaborative learning and individual learning. We do so through design-based research, prototyping, and classroom piloting. Third, we evaluate the newly-created technology for dynamically-differentiated collaborative classroom in schools, to gain an initial understanding of its benefits and challenges, and the changes in classroom practices and learning outcomes that it brings about.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.

该项目将创建和展示支持未来课堂动态差异化教学的新技术。这一新愿景的核心是教师、学生和人工智能（AI）之间精心设计的伙伴关系。基于人工智能的学习软件将在解决问题的实践中为学生提供支持，提供个人指导（使用标准智能辅导技术）或指导，以便学生能够有效地协作和相互辅导。这些学习活动不断调整，以适应每个学生的需求，包括个人或合作学习之间的切换。教师“编排”（煽动，监督和调节）这一动态过程。新的工具将提高教师对学生课堂进度的认识。目标是为所有学生提供高效和高效的学习过程，并为教师提供有效的“协调支持”。我们将在中学AI增强数学学习的背景下实施和测试这一愿景。拟议的工作将大大提高目前对如何设计有效的基于人工智能的“协同编排”工具的理解，这些工具利用教师、学生/同伴和人工智能代理的互补优势，使动态差异化课堂的愿景切实可行。它将提供洞察新的课堂动态出现。这项工作最终可能有助于更加个性化的K-12教育。这项工作将创建一个测试平台，可用于探索和严格测试有关个人和协作学习的协同编排和动态分化的各种有趣的假设。动态差异化教学的有效编排在设计和技术实施方面提出了重大挑战。虽然现有的基于人工智能的学习技术支持教学的动态分化形式，但它们往往不支持个人学习和协作学习的动态组合;事实上，大多数只支持这两种学习模式之一。此外，现有的教师支持工具往往只注重提高教师的认识，而不是支持教师的决策和行动的时刻，而不是支持个人和协作学习的动态交织。在拟议的工作中，我们通过整合和扩展四个方面的工作来应对这一挑战：智能辅导系统技术;支持个人和协作学习组合的学习环境;相互同伴辅导的自适应技术支持;以及支持教师/AI协同编排的混合现实工具（“智能眼镜”）。在此基础上，我们通过三个方面的工作创建并展示了动态差异化教学的技术支持。首先，我们创建了基于AI的辅导软件，能够支持个人学习者和学生进行相互的同伴辅导。其次，我们开发了一种工具来支持学生，教师和AI代理之间的智能，实时的协同编排，动态区分协作学习和个人学习的组合。我们通过基于设计的研究，原型设计和课堂试点来实现这一目标。第三，我们对学校动态差异化协作课堂的新技术进行评估，以初步了解其好处和挑战，以及它带来的课堂实践和学习成果的变化。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。

项目成果

Designing for human–AI complementarity in K‐12 education

K-12 教育中人类与人工智能互补性的设计

• DOI: 10.1002/aaai.12058
• 发表时间: 2022
• 期刊: AI Magazine
• 影响因子: 0.9
• 作者: Holstein, Kenneth;Aleven, Vincent
• 通讯作者: Aleven, Vincent

Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom

配对：课堂中个人学习和协作学习之间动态过渡的人机协作原型设计

• DOI: 10.1145/3544548.3581398
• 发表时间: 2023
• 期刊: CHI ‘23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Yang, Kexin Bella;Echeverria, Vanessa;Lu, Zijing;Mao, Hongyu;Holstein, Kenneth;Rummel, Nikol;Aleven, Vincent
• 通讯作者: Aleven, Vincent

Exploring Policies for Dynamically Teaming Up Students through Log Data Simulation

通过日志数据模拟探索学生动态组队的策略

• 发表时间: 2021
• 期刊: Fourteenth International Conference on Educational Data Mining (EDM 2021
• 作者: Yang, K;Echeverria, V.;Wang, X.;Lawrence, L.;Holstein, K.;Rummel, K.;Aleven, V.
• 通讯作者: Aleven, V.

A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

• DOI: 10.1007/978-3-030-52237-7_20
• 发表时间: 2020-06-09
• 期刊: Artificial Intelligence in Education
• 作者: Holstein K;Aleven V;Rummel N
• 通讯作者: Rummel N

Technology ecosystem for orchestrating dynamic transitions between individual and collaborative AI-tutored problem solving

用于协调个人和协作人工智能辅导问题解决之间的动态转换的技术生态系统

• 发表时间: 2022
• 期刊: AIED 2023
• 作者: Yang, K.;Lu, Z.;Echeverria, V.;Sewall, J.;Lawrence, L.;Rummel, N.;& Aleven, V.
• 通讯作者: & Aleven, V.