A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

A Conceptual Framework for Human–AI Hybrid Adaptivity in Education
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DOI:
10.1007/978-3-030-52237-7_20
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发表时间:
2020-06-09
期刊:
Artificial Intelligence in Education
影响因子:
--
通讯作者:
Rummel N
Rummel N
中科院分区:
其他
文献类型:
--
作者:
Holstein K;Aleven V;Rummel N

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教育人工智能 (AIEd) 系统的设计和评估越来越多地考虑到现实世界教育环境中适应性的混合性质。在实践中,除了 AIEd 系统本身的属性之外,适应性通常是由 AI 系统和人类促进者(例如教师或同伴)共同制定的。尽管最近有很多研究活动,但设计此类人类人工智能系统的理论和概念指导仍然有限。在本文中,我们探讨了如何在 AIEd 系统以及与之合作的各个人类利益相关者之间共享适应性。基于对先前框架的比较,这些框架倾向于分别检查 AIEd 系统或人类教练的适应性,我们首先综合一组足以捕获人类与人工智能混合适应性的通用维度。然后,我们利用这些维度提出一个概念框架,以绘制人类和 AIEd 系统增强彼此能力的不同方式。通过示例,我们说明了如何使用该框架来描述先前的工作,并设想人类与人工智能混合教育方法的新可能性。
Educational AI (AIEd) systems are increasingly designed and evaluated with an awareness of the hybrid nature of adaptivity in real-world educational settings. In practice, beyond being a property of AIEd systems alone, adaptivity is often jointly enacted by AI systems and human facilitators (e.g., teachers or peers). Despite much recent research activity, theoretical and conceptual guidance for the design of such human–AI systems remains limited. In this paper we explore how adaptivity may be shared across AIEd systems and the various human stakeholders who work with them. Based on a comparison of prior frameworks, which tend to examine adaptivity in AIEd systems or human coaches separately, we first synthesize a set of dimensions general enough to capture human–AI hybrid adaptivity. Using these dimensions, we then present a conceptual framework to map distinct ways in which humans and AIEd systems can augment each other’s abilities. Through examples, we illustrate how this framework can be used to characterize prior work and envision new possibilities for human–AI hybrid approaches in education.
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