Extraction and Use of Highly Explainable and Transferable Indicators for AI in Education

• Project/Area Number: 23K25698
• Project/Area Number (Other): 23H01001 (2023)
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Multi-year Fund (2024); Single-year Grants (2023)
• Section: 一般
• Review Section: Basic Section 09070:Educational technology-related
• Research Institution: Kyoto University
• Principal Investigator: Flanagan Brendan 京都大学, 国際高等教育院, 特定准教授 (00807612)
• Co-Investigator(Kenkyū-buntansha): Majumdar Rwito 熊本大学, 半導体・デジタル研究教育機構, 准教授 (30823348) ; 戴 憶菱 京都大学, 学術情報メディアセンター, 特定研究員 (90912254) ; 高見 享佑 国立教育政策研究所, 教育データサイエンスセンター, 主任研究官 (70912252)
• Project Period (FY): 2023-04-01 – 2026-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥18,850,000 (Direct Cost: ¥14,500,000、Indirect Cost: ¥4,350,000); Fiscal Year 2025: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000); Fiscal Year 2024: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000); Fiscal Year 2023: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
• Keywords: Educational AI / Explainability / Transferability

Outline of Research at the Start

AI models are increasingly being used in education. To explain AI model decisions and support their transfer to new or low resource classes, we aim to identify and extract highly explainable and transferable indicators and investigate their application in machine learning in education.