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        <datestamp>2025-01-19T10:43:15Z</datestamp>
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          <dc:title>ChatGPTを活用した金融数学問題の解法と教材生成</dc:title>
          <dc:title xml:lang="en">Utilizing ChatGPT for Solving Financial Mathematics Problems and Generating Educational Materials</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>白田, 由香利</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName>桑名, 杏奈</jpcoar:creatorName>
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          <jpcoar:subject subjectScheme="Other">データ工学</jpcoar:subject>
          <datacite:description descriptionType="Other">本研究では，ChatGPT を利用して経済数学の問題解決と教材の生成を試みた．初めに，ChatGPT の統計的手法だけを用いると，演繹推論に基づく数学問題の解法で誤りが生じることが確認された．そのため，Wolfram Cloud などの記号処理 AI を組み込むことで，金融数学の具体的な問題，例えば債券プライシング，変動金利の住宅ローン返済などの解法を実現した．さらに，ChatGPT を利用して，学生向けの有益な教材の生成も行った．具体的には，演繹推論の過程を可視化するグラフなどを作成した．この研究の核心は，正確な演繹推論を ChatGPT に実行させ，目的に応じた教材を効果的に生成するためのプロンプト提示方法の開発にある．結果として，ChatGPT を活用した新しい経済数学の教授法を提案する．</datacite:description>
          <datacite:description descriptionType="Other">In this study, we attempted to solve economic mathematics problems and generate educational materials using ChatGPT. Initially, it was observed that solely using ChatGPT's statistical methods resulted in errors when deducing reasoning for mathematical problems. Consequently, by integrating symbolic computation AIs, such as Wolfram Cloud, we successfully addressed various financial mathematics problems including bond pricing, variable interest housing loan repayments, and foreign currency deposit calculations. Furthermore, we utilized ChatGPT to create valuable resources for students, such as graphs visualizing the deductive reasoning process. The essence of this research lies in the development of prompt presentation methods to enable ChatGPT to perform accurate deductive reasoning and generate purposeful educational materials accordingly. As a result, we propose a new teaching methodology for economic mathematics leveraging ChatGPT.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2023-12-19</datacite:date>
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          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/231566</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">2188-871X</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AN10112482</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>研究報告データベースシステム（DBS）</jpcoar:sourceTitle>
          <jpcoar:volume>2023-DBS-178</jpcoar:volume>
          <jpcoar:issue>5</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>6</jpcoar:pageEnd>
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