フランケンシュタイン,テーマ分析と生成型人工知能:質評価方法と定性研究に関する考察
Tanisha Jowsey1, Peta Stapleton2, Shawna Campbell2
1Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia.
PloS one
|September 5, 2025
まとめ
Microsoft Copilotのような生成型人工知能 (GenAI) ツールは,定性研究におけるテーマ分析にはまだ信頼できない. 現在のGenAIのバージョンは 引用文を作り出し 人間の分析とは違って 重要なデータを見逃しています
科学分野:
- 定性的な研究方法
- 研究における人工知能
- 健康科学 データ分析
背景:
- 生産性のある人工知能 (GenAI) の採用が拡大する中で,定性的なデータ分析の検証が求められています.
- テーマ分析のためのGenAIの信頼性と適性を評価することは研究者にとって極めて重要です.
研究 の 目的:
- テーマ分析のためのGenAIの使用の正確性と効率性を決定する.
- 質的研究におけるGenAIの出力の信頼性を評価する方法を提案する.
主な方法:
- テーマ別分析やデータセットを掲載した健康関連の研究に関するデータベースや雑誌を検索した.
- Microsoft Copilot (閉じたシステム GenAI) をプロンプトして,選択されたデータセットのテーマ分析を実行します.
- GenAIの出力を人間の分析と比較し,時間,精度,テーマの重複,引用の信頼性を測定した.
主要な成果:
- 人によって特定されたテーマとCopilotのテーマの間の重複は最小です.Copilotはしばしば引用 (58%) を作り出し,限られたデータセクションに焦点を当てました.
- 人間の分析では,より広範な表現とより高い引用精度 (79%) が示されました.
- コピロットは人間の研究者と違って テーマごとに参加者の分布を提供できませんでした
結論:
- コピロットのようなGenAIの現在のバージョンは,不正確さと信頼性のためにテーマ分析には推奨されません.
- この研究は,ヒト生成とGenAI生成の両方の定性データ分析と報告の有効性に関する重大な懸念を浮き彫りにしています.
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