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大規模言語モデルAIと人間が生成した行動変容体重減少コーチングメッセージの比較
Zhuoran Huang1, Michael P Berry2, Christina Chwyl3
1Khoury College of Computer Sciences, Northeastern University, 440 Huntington Ave, Boston, MA 02115 USA.
Journal of technology in behavioral science
|January 22, 2026
まとめ
大規模言語モデル(LLM)AIチャットボットは、パーソナライズされた体重管理コーチングに有望である。修正後、AIメッセージは人間のコーチと同等の有用性を示し、人間が書いたものと間違われることが多かった。
科学分野:
- 行動科学
- 人工知能
- ヘルスコーチング
背景:
- 自動化された体重管理コーチングメッセージは、繰り返しが多く、人間のコーチングよりも効果が低い場合がある。
- 大規模言語モデル(LLM)AIチャットボットは、パーソナライズされた斬新なコーチングメッセージの可能性を提供する。
- LLMベースの体重管理コーチングの実行可能性と許容可能性は、依然として十分に研究されていない。
主な方法:
- BMIが27 kg/m²以上の成人87名が体重減少トライアルに参加した。
- 参加者は、人間が書いたコーチングメッセージとChatGPTが生成したコーチングメッセージの有用性を5点リッカート尺度で評価した。
- メッセージの修正を伴う2つのフェーズで、質的なフィードバックとAIメッセージの同定が収集された。
結論:
- LLM AIは、効果的な体重管理コーチングメッセージを作成するための予備的な実行可能性を示している。
- AI生成メッセージは、改良後、人間が書いたコンテンツに匹敵する有用性とパーソナライズされたものとして認識される可能性がある。
- AIコーチングの定型的でデータ中心的な側面に対処するためには、さらなる改善が必要である。
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