Ninaus Manuel, Sailer Michael
Institute of Psychology, University of Graz, Graz, Austria.
LEAD Graduate School and Research Network, University of Tübingen, Tübingen, Germany.
Front Psychol. 2022 Aug 25;13:956798. doi: 10.3389/fpsyg.2022.956798. eCollection 2022.
Recent advancements in artificial intelligence make its use in education more likely. In fact, existing learning systems already utilize it for supporting students' learning or teachers' judgments. In this perspective article, we want to elaborate on the role of humans in making decisions in the design and implementation process of artificial intelligence in education. Therefore, we propose that an artificial intelligence-supported system in education can be considered a closed-loop system, which includes the steps of (i) data recording, (ii) pattern detection, and (iii) adaptivity. Besides the design process, we also consider the crucial role of the users in terms of decisions in educational contexts: While some implementations of artificial intelligence might make decisions on their own, we specifically highlight the high potential of striving for hybrid solutions in which different users, namely learners or teachers, are provided with information from artificial intelligence transparently for their own decisions. In light of the non-perfect accuracy of decisions of both artificial intelligence-based systems and users, we argue for balancing the process of human- and AI-driven decisions and mutual monitoring of these decisions. Accordingly, the decision-making process can be improved by taking both sides into account. Further, we emphasize the importance of contextualizing decisions. Potential erroneous decisions by either machines or humans can have very different consequences. In conclusion, humans have a crucial role at many stages in the process of designing and using artificial intelligence for education.
人工智能领域的最新进展使其在教育中的应用更具可能性。事实上,现有的学习系统已经在利用它来支持学生学习或辅助教师进行判断。在这篇观点文章中,我们想要详细阐述人类在教育领域人工智能设计与实施过程中的决策作用。因此,我们提出教育领域的人工智能支持系统可被视为一个闭环系统,它包括以下步骤:(i)数据记录,(ii)模式检测,以及(iii)适应性调整。除了设计过程,我们还考虑了用户在教育环境决策方面的关键作用:虽然一些人工智能的应用可能会自行做出决策,但我们特别强调寻求混合解决方案的巨大潜力,即向不同用户(即学习者或教师)透明地提供来自人工智能的信息,以便他们做出自己的决策。鉴于基于人工智能的系统和用户的决策准确性都并非完美,我们主张平衡人类驱动和人工智能驱动的决策过程,并对这些决策进行相互监督。相应地,通过兼顾双方可以改进决策过程。此外,我们强调决策情境化的重要性。机器或人类做出的潜在错误决策可能会产生截然不同的后果。总之,在为教育设计和使用人工智能的过程中,人类在许多阶段都起着至关重要的作用。