教師教育における学生による大規模な言語モデルの採用を,拡張技術受け入れの枠組みを通じてモデル化
Yulin Gong1, Chengshu Xu2,3, Suwen Luo4
1China Institute of Rural Education Development, Northeast Normal University, Changchun City, 130024, China.
Scientific reports
|September 1, 2025
まとめ
教師養成の学生
科学分野:
- 教育技術
- 教育における人工知能
背景:
- 大型言語モデル (LLM) は,執筆支援のための教育にますます統合されています.
- 教育におけるデジタル変革には 教師教育における学生のLLMの受け入れが 極めて重要です
研究 の 目的:
- 教師教育の学生によるLLMの採用に影響を与える要因を調査する.
- 動機付け,リスク,自己効能,経験などの変数で技術受け入れモデル (TAM) を拡張する.
主な方法:
- 構造方程式モデリング (SEM) を用いて調査データを分析した.
- 中国の4年生552人のデータです
主要な成果:
- 主観的な規範は 行動の意図を強く予測します
- 認識された使いやすさは,LLMに対する態度に肯定的な影響を及ぼします.
- 認識された時間のリスクは,認識された有用性に影響し,プライバシーリスクは,認識された使いやすさに影響します.
- 学習のモチベーションを高め 採用を促進します
結論:
- この発見は,教育技術におけるTAMの応用を広げています.
- 技術訓練の設計とLLMのユーザー体験の最適化のための洞察を提供します.
- 将来の教師のデジタルリテラシー訓練の枠組みを提供すること
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