AI支援適応型テストにおける認知的診断フィードバックとその成績との関連性:学部外科学教育におけるパイロットスタディ
Nuno Silva Gonçalves1,2, Carlos Collares1,3,4,5,6, José Miguel Pêgo1,2,4,7
1Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal.
Frontiers in behavioral neuroscience
|January 22, 2026
まとめ
人工知能(AI)支援コンピュータ適応型テスト(CAT)は、外科教育において高いテストスコアとの相関を示し、有望であることが示されている。このAI主導の評価モデルでは、記憶スキルが生徒の成績を大幅に予測した。
科学分野:
- 医療教育技術
- ヘルスケアにおける人工知能
- 認知的評価
背景:
- 効果的な認知的フィードバックは外科研修に不可欠ですが、リソースと形式の制約に直面しています。
- 人工知能(AI)は、外科教育における自動フィードバック、診断、および項目生成のための革新的なソリューションを提供します。
主な方法:
- 150の多肢選択問題のAI生成項目バンクを開発しました。
- 合計進捗テストの前に、記憶、分析、意思決定領域にわたる形成的AI支援CATを実施しました。
- 116人の参加外科学生の成績相関、グループ比較、および回帰を分析しました。
結論:
- 認知的診断モデリング(CDM)を備えたAI支援CATは、学部外科学教育において実行可能な形成的評価ツールです。
- このAI主導のアプローチは、合計成績の向上に関連しており、パーソナライズされた診断フィードバックを提供します。
- フィードバックメカニズムと意思決定評価のさらなる洗練は、外科研修におけるAIの教育的有用性を高める可能性があります。
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