関連する実験動画
2段階の構造方程式モデリングと説明可能な機械学習の枠組みで,大学生による生成AIの採用を理解する:内在的な動機と認識されたリスクのバランスをとる
Daixing Zeng1, Xiaoqin Xu1, Tianxing Zhu2
1Department of Statistics, Chengdu University of Information Technology, Chengdu, China.
Frontiers in psychology
|February 16, 2026
まとめ
大学生は大学生である.
科学分野:
- 教育における人工知能
- 高等教育 テクノロジーの採用
- 人とコンピュータの相互作用です.
背景:
- 大学生によるジェネラティブAI (GenAI) の採用は急速に増加しています.
- 採用の原動力や障壁を理解することは,高等教育におけるAIの責任ある統合に不可欠です.
- 伝統的な構造方程式モデリング (SEM) のような既存の方法は,すべての関連する要因を特定する上で制限があります.
研究 の 目的:
- GenAIの採用を分析するための新しい2段階のSEM-XMLフレームワークを導入し,検証する.
- 学生のGenAI使用に影響を与える,認識された利益とリスクの相互作用を調査する.
- 大学生の間でGenAIの採用を推進し,阻害する重要な要因を特定する.
主な方法:
- 中国南西部の大学生880人を対象に横断調査が行われました.
- 新しい2段階のSEM-XMLフレームワークが採用され,仮説テストのためのSEMとデータ主導の要因識別のためのXMLを統合しました.
- このフレームワークは,技術受容モデル,計画行動理論,認識されたリスク,知識態度実践モデルからのコンストクトをブレンドしたものです.
主要な成果:
- 認識された有用性と個人的な関心は,GenAIの採用と正に相関しています.
- 倫理,正確性,学術的な整合性に関する認識されたリスクは,GenAIの使用を大幅に抑制します.
- 内部的な態度と外部からの圧力の両方が相互作用し,学生のGenAIの採用を形作る.
結論:
- 開発されたSEM-XMLフレームワークは,GenAIの採用要因を効果的に分析します.
- 教育機関,政策立案者,開発者は,戦略的なAI統合のためにこれらの洞察を活用することができます.
- 認識されるリスクに対処することは,学術界における責任ある,有益なGenAIの使用を促進するために不可欠です.
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