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AI 之未來：如何讓回饋更具吸引力、有效且可擴展？

arXiv - Computers and SocietyJennifer Meyer (University of Vienna, Vienna, Austria), Olaf K\"oller (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Thorben Jansen (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Johanna Fleckenstein (University of Hildesheim, Germany), Michael W. Asher (Carnegie Mellon University, USA), Sarah Bichler (Universit\"at Passau, Passau, Germany), Laura Brandl (LMU Munich, Germany), Jasmin Breitwieser (DIPF | Leibniz Institute for Research,Information in Education, Frankfurt, Germany), Kai S. Cortina (University of Michigan, USA), Mutlu Cukurova (University College London, United Kingdom), Martin Daumiller (University of Freiburg, Germany), Hannah Deininger (University of T\"ubingen, Germany), Frank Fischer (LMU, Germany), Dragan Ga\v{s}evi\'c (Monash University, Clayton, VIC, Australia), Jeanine Gr\"utter (LMU Munich, Germany), Anna Hilz (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Ioana Jivet (CATALPA, FernUniversit\"at in Hagen, Germany), Jelena Jovanovi\'c (University of Belgrade, Serbia), Rene F. Kizilcec (Cornell University, USA), Livia Kuklick (Humboldt Universit\"at zu Berlin, Berlin, Germany), Marlit Annalena Lindner (Leibniz Institute for Science,Mathematics Education,Europa-Universit\"at Flensburg, Germany), Anastasiya Lipnevich (NBME,the Graduate Center, City University of New York, USA), Ute Mertens (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Detmar Meurers (Leibniz Institut f\"ur Wissensmedien, T\"ubingen, Germany), Kou Murayama (University of T\"ubingen, Germany), Tanya Nazaretsky (EPFL, Switzerland), Knut Neumann (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Ernesto Panadero (Dublin City University,Deusto University), Maciej Pankiewicz (University of Pennsylvania, USA), Zachary A. Pardos (UC Berkeley, USA), Chris Piech (Stanford University, USA), Hannah P\"unjer (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Nikol Rummel (Ruhr-University-Bochum,CAIS, Bochum, Germany), Marlene Steinbach (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Olga Viberg (KTH Royal Institute of Technology, Sweden), Naomi Winstone (University of Surrey, UK)

本報告綜合50位跨領域學者觀點，探討生成式 AI 在即時自動回饋中的潛力、優勢與風險，並提出未來研究與實務方向。

AI 幫你先抓重點

AI 重點 1

AI 能即時產出高質量、可擴展的回饋，解決傳統回饋瓶頸。

滑鼠懸停看 AI 判斷理由
此點凸顯 AI 在提升回饋效率與覆蓋範圍上的核心優勢，對教育實務者設計自動化回饋系統具有直接啟發。
AI 重點 2

AI 生成回饋的倫理與偏見風險需被嚴格監督與透明化。

滑鼠懸停看 AI 判斷理由
因為風險不僅影響學習者信任，也可能加劇教育不平等，決策者必須先把握此議題才能安全部署。

核心研究發現

  1. 1

    生成式 AI 可即時產出量身訂製的回饋，突破傳統人工回饋的時間與人力限制，提升學習者參與度與學習成效。

  2. 2

    跨領域學者共識指出，AI 回饋若能結合學習目標與情境，能促進元認知發展與自主學習策略的運用。

  3. 3

    報告揭示主要爭議在於回饋質量、偏見風險與教師角色轉變，呼籲建立透明度與倫理監督機制。

  4. 4

    未解決挑戰包括如何確保 AI 回饋與課程設計同步、個別化程度與可解釋性，以及資料隱私與安全問題。

  5. 5

    未來研究方向聚焦於實證評估 AI 回饋對學習成效的長期影響、教師培訓模式與政策制定框架。

對教育工作者的啟發

實務工作者可先在小規模課程中試點 AI 回饋，並設計教師與學生共創的回饋流程，確保回饋內容符合學習目標且可解釋。建議建立回饋質量審核機制，監測偏見與資料隱私，並透過教師培訓提升其對 AI 工具的理解與調整能力。長期而言，需與學校資訊系統整合，形成可持續的回饋循環，並定期評估其對學習成效與學生動機的影響。

原始文獻資訊

英文標題：
The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?
作者：
Jennifer Meyer (University of Vienna, Vienna, Austria), Olaf K\"oller (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Thorben Jansen (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Johanna Fleckenstein (University of Hildesheim, Germany), Michael W. Asher (Carnegie Mellon University, USA), Sarah Bichler (Universit\"at Passau, Passau, Germany), Laura Brandl (LMU Munich, Germany), Jasmin Breitwieser (DIPF | Leibniz Institute for Research,Information in Education, Frankfurt, Germany), Kai S. Cortina (University of Michigan, USA), Mutlu Cukurova (University College London, United Kingdom), Martin Daumiller (University of Freiburg, Germany), Hannah Deininger (University of T\"ubingen, Germany), Frank Fischer (LMU, Germany), Dragan Ga\v{s}evi\'c (Monash University, Clayton, VIC, Australia), Jeanine Gr\"utter (LMU Munich, Germany), Anna Hilz (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Ioana Jivet (CATALPA, FernUniversit\"at in Hagen, Germany), Jelena Jovanovi\'c (University of Belgrade, Serbia), Rene F. Kizilcec (Cornell University, USA), Livia Kuklick (Humboldt Universit\"at zu Berlin, Berlin, Germany), Marlit Annalena Lindner (Leibniz Institute for Science,Mathematics Education,Europa-Universit\"at Flensburg, Germany), Anastasiya Lipnevich (NBME,the Graduate Center, City University of New York, USA), Ute Mertens (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Detmar Meurers (Leibniz Institut f\"ur Wissensmedien, T\"ubingen, Germany), Kou Murayama (University of T\"ubingen, Germany), Tanya Nazaretsky (EPFL, Switzerland), Knut Neumann (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Ernesto Panadero (Dublin City University,Deusto University), Maciej Pankiewicz (University of Pennsylvania, USA), Zachary A. Pardos (UC Berkeley, USA), Chris Piech (Stanford University, USA), Hannah P\"unjer (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Nikol Rummel (Ruhr-University-Bochum,CAIS, Bochum, Germany), Marlene Steinbach (Leibniz Institute for Science,Mathematics Education, Kiel, Germany), Olga Viberg (KTH Royal Institute of Technology, Sweden), Naomi Winstone (University of Surrey, UK)
來源：
arXiv - Computers and Society
AI 摘要模型：
openai/gpt-oss-20b
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