生成式人工智能提升大学生学习迁移能力的路径探索

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中图分类号：G64；TP18 文献标志码：A 文章编码：1672-7274（2026）02-0091-03

Exploring the Pathway to Enhancing College Students' Learning Transfer Capability via Generative Artificial Intelligence

BIN Jieyu （School of Marxism，Hunan University of Science and Technology，Liuyang 4111oo， China）

Abstract：Basedon thedistinctive features of technological breakthroughs innew-quality productivity，element reconfiguration，and businessmodel upgrading，this article delves into four strategic pathways forleveraging generative artificial intelligence to address thechallenges associated with college students'learning transfer.These pathways encompass personalized knowledge architecture construction，contextualized practical simulation， interdisciplinary resourceamalgamation，and metacognitivefedback-driven guidance.Theresearchendeavors toofer pragmatic insights for the digital transformationofeducationwithinthecontext of new-qualityproductivity，therebyfacilitating thecultivationof core competencies among college students that are aligned with future developmental demands.

Keywords： new quality productivity; generative AI college student; learning transfer ability

0 引言

学习迁移能力属于个体将一种情境当中习得的知识技能运用到另一情境的关键能力，是新质生产力背景下大学生适应产业需求、实现创新发展的核心素养，正如奥苏伯尔所说的“一切有意义的学习都是在已有学习的基础上进行的，不受学习者原有认识结构影响的新学习是不存在的[1。（剩余3276字）