Scholarlink

<p>This study examined preservice teachers’ use of generative Artificial Intelligence (AI) chatbots for lesson planning during K-12 practicum placements. Using a convergent mixed-methods approach, we analyzed survey responses (<i>n</i> = 103; response rate = 12.88%) and conducted 4 semi-structured interviews to contextualize quantitative patterns. Among respondents, approximately three-quarters reported using AI chatbots for lesson-planning-related tasks, with ChatGPT being the most common tool. Exploratory analyses (<i>χ²</i> tests and a focused logistic model predicting frequent use (≥ half the time)) suggest that use frequency is driven more by perceived value and practical needs than by demographic factors. Interview data indicate that participants primarily used AI to save time and generate ideas, particularly under time pressure or when teaching less familiar topics, while noting risks related to curriculum alignment, accuracy, technical reliability, and over-reliance. Given the low response rate and likely self-selection, findings should be interpreted as indicative rather than fully generalizable. We conclude with program-level recommendations, including a minimal practicum policy toolkit (Appendix D) to support responsible and transparent AI use in teacher education.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From prompts to practice: preservice teachers candidates’ integration of ChatGPT in lesson planning during practicum

  • 期刊论文

  • 发表日期：

    2026年5月18日

查看全文
  • Wenxuan Cai,
  • Jim Hewitt

摘要

This study examined preservice teachers’ use of generative Artificial Intelligence (AI) chatbots for lesson planning during K-12 practicum placements. Using a convergent mixed-methods approach, we analyzed survey responses (n = 103; response rate = 12.88%) and conducted 4 semi-structured interviews to contextualize quantitative patterns. Among respondents, approximately three-quarters reported using AI chatbots for lesson-planning-related tasks, with ChatGPT being the most common tool. Exploratory analyses (χ² tests and a focused logistic model predicting frequent use (≥ half the time)) suggest that use frequency is driven more by perceived value and practical needs than by demographic factors. Interview data indicate that participants primarily used AI to save time and generate ideas, particularly under time pressure or when teaching less familiar topics, while noting risks related to curriculum alignment, accuracy, technical reliability, and over-reliance. Given the low response rate and likely self-selection, findings should be interpreted as indicative rather than fully generalizable. We conclude with program-level recommendations, including a minimal practicum policy toolkit (Appendix D) to support responsible and transparent AI use in teacher education.