人工知能の受け入れから多言語学習における自己効率化まで:AIACスケール開発と学習の喜びの仲介的な役割
Aiqing Yu1, Ying Ma2, Guanyao Xu3
1School of Foreign Languages, Southwest Jiaotong University, No. 999, Xi'an Road, Chengdu, 611756, China.
Acta psychologica
|February 18, 2026
まとめ
学習者は学習者に
科学分野:
- 教育技術教育技術について
- 教育における人工知能
- 多言語言語学習 言語学習の多言語学習
背景:
- 言語教育における人工知能 (AI) の統合が拡大している.
- 学習者の受け入れ,多言語学習の楽しさ (MLE),および自己効率性 (SE) は,AIの教育効果の鍵です.
- 既存のフレームワークは,多様な多言語の文脈でAIの受け入れを完全に捉えることができないかもしれません.
研究 の 目的:
- 多言語言語学習のためのAI受け入れ (AIAC) フレームワークを開発し,検証する.
- AIACとSEの関係におけるMLEの仲介的役割を調査する.
- 多言語教育における効果的なAI統合戦略の実証的証拠を提供すること.
主な方法:
- 探索的要素分析 (EFA) と確認的要素分析 (CFA) を用いたAIACスケールの開発と検証.
- 構造方程式モデリング (SEM) を用いて,AIAC,MLE,SEの関連性を分析する.
- 中国の11の大学で524人の多言語の大学生から集めたデータです.
主要な成果:
- 4つの次元 (知覚された有用性,知覚された使いやすさ,使用行動的意図,実際の使用) を含む14項目のAIACフレームワークが検証されました.
- AIACは,学習者のSEとの直接的・間接的な関連性を実証した.
- MLEはAIACとSEの関係を部分的に仲介した.
結論:
- 検証されたAIACフレームワークは,多言語環境におけるAIの受け入れ率を評価するための文脈に敏感なツールです.
- 人工知能をサポートするタスクや文化間の活動に組み込むことは,指導とともに,受け入れ,楽しみ,SEを高めることができます.
- 研究結果は,カリキュラムの設計と教員養成のための実用的な洞察を提供し,言語教育における持続可能なAI採用を支援します.
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