华东师范大学学报(教育科学版)

华东师范大学学报(教育科学版) ›› 2026, Vol. 44 ›› Issue (1): 65-79.doi: 10.16382/j.cnki.1000-5560.2026.01.006

• 教育数字化转型 • 上一篇    下一篇

使用生成式人工智能辅助学习的学生类型画像——基于全国20所高校本科生调查的实证研究

马莉萍1, 郑翔睿2, 周雪涵1   

  1. 1. 北京大学教育经济研究所
    2. 北京大学教育学院，北京 100871
  • 出版日期:2026-01-01 发布日期:2025-12-31
  • 基金资助:
    国家自然科学基金面上项目“生成式人工智能使用模式及其对大学生发展的影响：追踪调查与实验干预”（72574011）。

Portraits of College Student Types Using Generative AI for Learning: An Empirical Study Based on a Survey of Undergraduates from 20 Universities Nationwide

Liping Ma1, Xiangrui Zheng2, Xuehan Zhou1   

  1. 1. Institute of Economics of Education, Peking University Beijing 100871, China
    2. Graduate School of Education，Peking University Beijing 100871, China
  • Online:2026-01-01 Published:2025-12-31

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摘要：

基于对全国20所高校12678名本科生的调查和实证分析发现：大部分学生在利用GenAI开展思辨性和创造性活动等高阶思维活动时会具有较高的批判使用态度，并预期GenAI辅助学习将对问题解决能力的发展具有积极影响；进一步基于潜在剖面分析和计量回归分析发现，学生使用GenAI辅助学习呈现出4种差异化类型，反映出大学生在人工智能使用中技术接纳度与自我调节学习能力的双向互动：审慎体验者（占42%），具有“低接纳度-高批判性使用-能力发展低预期”的特点；劳动替代者（占24%），具有“低接纳度-低批判性使用-能力发展较高预期”的特点；均衡探索者（占21.2%），具有“高接纳度-低批判性使用--能力发展较高预期”的特点；深度使用者（占12.8%），具有“高接纳度-高批判性使用-能力发展高预期”的特点。基于上述研究发现本文进一步指出高校引导本科生正确使用生成式人工智能工具的必要性和有效策略，包括提供有针对性的教育干预、培养学生的人工智能素养等，使各类学生逐步向“高接纳度-高批判性使用-能力发展高预期”类型转化。

关键词: 生成式人工智能, 使用模式, 批判性使用, 高阶思维能力, 潜在剖面分析

Abstract:

As generative artificial intelligence (GenAI) becomes increasingly integrated into college students’ academic activities, some scholars and education administrators have expressed concerns about students’ blind reliance on GenAI and the potential weakening of higher-order thinking skills. Based on a survey of 12,678 undergraduates from 20 universities across China and empirical analysis, this study finds that most students exhibit a critical attitude when engaging in higher-order thinking tasks such as critical and creative activities with GenAI, and believe that GenAI-assisted learning positively contributes to their higher-order cognitive development. Furthermore, a latent profile analysis reveals four distinct types of GenAI-assisted learning users, reflecting the interaction between students' level of technology acceptance and their self-regulated learning abilities: (1) Cautious Experiencers (42%): low acceptance, high critical use; (2) Labor Substituters (24%): low acceptance, low critical use; (3) Balanced Explorers (21.2%): high acceptance, low critical use; (4) Deep Users (12.8%): high acceptance, high critical use. Based on these findings, the paper underscores the necessity for universities to guide undergraduates in the appropriate use of GenAI tools and proposes effective strategies such as targeted educational interventions and the cultivation of AI literacy. These measures aim to help students transition toward the ideal profile of "high acceptance – high critical use."

Key words: generative artificial intelligence, usage patterns, critical use, higher-order thinking skills, latent profile analysis