认知
弱势群体
心理学
认知心理学
计算机科学
知识管理
认知科学
人机交互
依赖关系(UML)
认知计算
认知技能
应用心理学
衡平法
人工智能
认知系统
发展心理学
作者
Jason M. Lodge,Leslie Loble
标识
DOI:10.71741/4pyxmbnjaq.31302475
摘要
This report investigates a profound new challenge driven by rapidly expanding use of artificial intelligence (AI) in schooling: the risk that students will outsource too much of the cognitive work that is crucial to establishing the knowledge, skill and ‘thinking infrastructure’ that enables both schooling success and lifelong capacity for ongoing learning and understanding.
There is a growing body of evidence that using AI can short-circuit the cognitive effort required for sustainable, deep learning, with potentially long-term consequences. This cognitive offloading from human to AI is especially risky for school students (‘novice’ learners who are building foundational knowledge and skills) when they turn to AI as a tempting substitute, not an amplifier, increase their dependency on the tool and lose access to deeper learning and critical thinking capabilities. It also introduces extra equity risks for disadvantaged students.
The report reviews the cognitive science behind this concerning shift and the growing evidence of its impact. It also outlines how these harmful effects can be counteracted through specific teaching and learning strategies and effective design of AI education technology, anchored on bolstering the central role of teachers. It includes specific recommendations for policy and teaching and learning strategies.
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