生成型人工智能工具的使用可以通过在教师之间共享的元认知和认知卸载来提高可持续教育的学术成就
Javed Iqbal1, Zarqa Farooq Hashmi2, Muhammad Zaheer Asghar3,4
1School of English Studies, Zhejiang International Studies University, Hangzhou, People's Republic of China.
Scientific reports
|May 13, 2025
概括
生成型人工智能工具对服务教师的学术成绩产生积极影响,共享的元认知和认知卸载作为关键调解者. 这项研究为将人工智能纳入教育提供了洞察力.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 教师教育教育的教师教育.
背景情况:
- 生成型人工智能工具 (GenAITU) 越来越多地融入教育,与可持续发展目标4 (SDG4) 保持一致.
- 了解GenAITU对学业成绩 (AA) 和调解因素的影响对于有效实施至关重要.
- 现役教师 (PST) 是研究教育环境中人工智能采用的关键人口.
研究的目的:
- 在SDG4框架内,调查生成人工智能工具使用 (GenAITU) 与教育服务教师 (PST) 学术成绩 (AA) 之间的关系.
- 在GenAITU-AA关系中检查共享元认知 (SMC) 和认知卸载 (COL) 的调解作用.
- 适应和应用技术的接受和使用的统一理论2 (UTAUT2) 了解GenAITU采用因素.
主要方法:
- 一项涉及中国武汉五所大学465名教师的定量研究.
- 数据采集使用7点利克特尺度调查与时间滞后设计.
- 使用部分最小平方结构方程建模 (PLS-SEM) 进行的分析,以评估变量关系和调解.
主要成果:
- 性能预期和使用行为GenAITU显著和积极与学业成绩相关.
- 发现共享的元认知和认知卸载是GenAITU使用和学业成绩之间的显著积极调解者.
- 所有四个适应的UTAUT2组件 (性能预期,努力预期,促进条件,使用行为) 都对认知卸载产生了积极的影响,而努力预期,促进条件和使用行为则影响了共享的元认知.
结论:
- 生成型人工智能工具的使用显著影响服务教师的学术成绩,共享的元认知和认知卸载发挥着关键的调解作用.
- 这些发现为教育工作者,课程设计者和大学管理人员提供了有价值的指导,帮助他们有效地将GenAITU整合到教师培训计划中.
- 成功整合GenAITU可以提高学习成果,并有助于实现SDG4.
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