计算机科学
脚手架
认知
数学教育
教育技术
人工智能
认知负荷
读写能力
限制
生成语法
合作学习
主动学习(机器学习)
语言习得
控制(管理)
生成模型
构造(python库)
教学方法
钥匙(锁)
教学设计
数字化学习
适应性学习
混合学习
计算机辅助教学
治疗组和对照组
元认知
自主学习
人机交互
教育研究
作者
Yulin Gong,Minkai Wang,Li He,Chengshu Xu,Yue Yu,Yulin Gong,Minkai Wang,Li He,Chengshu Xu,Yue Yu
标识
DOI:10.1177/07356331251396354
摘要
Digital game-based learning (DGBL) has demonstrated notable effectiveness in general artificial intelligence (AI) literacy education, but it often falls short in addressing personalized learning needs. Traditional game-based scaffolding lacks real-time feedback, thereby limiting the students’ deep cognitive engagement. To address these limitations, this study introduces a large language model (LLM)-based scaffolding designed to create an engaging and interactive AI learning environment. Using a quasi-experimental design with fifth-grade students, we compared an experimental group using LLM-based scaffolding (LLM-DGBL) with a control group using traditional scaffolding (TS-DGBL). The results showed that the experimental group outperformed the control group in both learning achievement and cognitive load reduction. Behavioral analysis showed that students in the experimental group engaged in more diverse and deeper self-regulated learning patterns. Moreover, interaction analysis revealed a key pattern: students with a higher cognitive load sought support from the LLM-based scaffolding more frequently than their peers with a lower cognitive load. This study presents a novel method for elementary AI literacy education. It also provides a valuable reference for applying generative AI in educational practice to support the development of adaptive learning tools tailored for young learners.
科研通智能强力驱动
Strongly Powered by AbleSci AI