理论与实践的桥梁:对GenAI辅助可视化学习的多阶段研究
IEEE computer graphics and applications
|September 29, 2025
概括
结构化的生成性AI (人工智能) 暴露提高了学生的参与度和在数据可视化教育中的工具采用. 这种基于构造主义学习的方法保持了严谨性,并为人工智能增强课程提供了实际框架.
科学领域:
- 教育技术的教育技术
- 数据可视化 数据可视化
- 人工智能的人工智能
背景情况:
- 生成型人工智能正在改变技术教育,需要新的方法来教授可视化技能.
- 了解AI增强环境中的学生学习对于课程开发至关重要.
研究的目的:
- 检查结构化大语言模型 (LLM) 暴露对数据可视化教育的影响.
- 确定学生如何将AI工具集成到他们的可视化工作流程中.
主要方法:
- 这是一项混合方法研究,涉及两所大学的65名研究生 (数据科学和计算机科学).
- 一个多阶段的调查,包括预先评估,干预观察,分配反思和干预后评估.
- 利用可观察的AI Assist平台进行结构化的生成AI曝光.
主要成果:
- 结构化的生成人工智能暴露,与建设主义原则保持一致,促进了持续的学生参与和工具采用.
- 确定了学生将AI整合到可视化工作流程中的特定模式.
- 在整个干预过程中保持教学严谨.
结论:
- 生成性AI可以有效地集成到可视化课程中,以支持学生的学习.
- 提供实用的框架和基于证据的见解,用于在可视化中进行人工智能辅助学习.
- 展示了人工智能对学习成果持续影响的初步证据,持续了三周.
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