协同调节网络中的生成式人工智能学习

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Jason Lodge, Paula de Barba, Jaclyn Broadbent
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引用次数: 0

摘要

生成式人工智能(AI)的出现引发了人们对学术诚信的合理担忧,尤其是这种技术可能导致评估作弊的可能性。然而,仅仅关注潜在的不当行为,掩盖了学习者和机器之间更深刻、更具变革性的互动。这篇评论文章深入探讨了学生与人工智能之间的关系,旨在强调在人工智能时代修订教学策略的必要性。我们认为,那些被广泛讨论的优先考虑人工智能素养或增强批判性思维的方法可能是不够的。相反,我们认为需要一种更全面的方法来强调自我调节学习(SRL)和学习的共同调节。SRL促进了自主性、适应性和更深入的理解,这些品质对于驾驭人工智能增强学习环境的复杂性是必不可少的。此外,我们引入了共同调节网络的概念,它强调了人类和机器之间相互交织的学习过程。通过将自我定位于该网络的核心,我们强调了个体机构在指导富有成效的人类-人工智能教育互动方面不可或缺的作用。我们的观点是,通过培养SRL和理解协同调节的动态,教育者可以更好地让学习者适应一个相互关联的人工智能驱动的世界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning with Generative Artificial Intelligence Within a Network of Co-Regulation
The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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