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This paper extends the Lifelong Sustainable Inquiry-based Community Learning (LSiCL) framework, based on Eduinformatics, to address challenges and opportunities presented by the rise of Large Language Models (LLMs) in education. LLMs like ChatGPT are fundamentally transforming education, with human-LLM and inter-LLM learning relationships opening new frontiers. This research explores whether the traditional human-to-human LSiCL can be meaningfully extended to encompass human-LLM and LLM-LLM interactions while retaining its core principle of collaborative inquiry. Our analysis demonstrates that LSiCL principles are applicable across all these configurations, paving the way for sustainable and scalable Human-LLM Learning ecosystems. This framework is crucial for improving resource efficiency, promoting educational equity, and enhancing global educational sustainability.

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Extended Lifelong Sustainable Inquiry-Based Community Learning (LSiCL) for Human-LLM Learning Based on Eduinformatics

  • 会议论文

  • 发表日期：

    2026年

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  • Kunihiko Takamatsu,
  • Sayaka Matsumoto,
  • Katsuhiko Murakami,
  • Hidehiko Kamei,
  • Kazuya Tsuruta,
  • Naoya Honda,
  • Yumi Ishige,
  • Shimpei Matsumoto,
  • Hiroya Kawasaki,
  • Ikuhiro Noda,
  • Kenya Bannaka,
  • Tetsuhiro Gozu,
  • Tomoyuki Sakai,
  • Ryosuke Kozaki,
  • Aoi Kishida,
  • Hibiki Ito,
  • Koichi Akashi,
  • Sayaka Hama,
  • Gerald Prescott,
  • Masahiro Uchida,
  • Akira Nakamura,
  • Yasuhiro Kozaki,
  • Tamotsu Mori,
  • Shintaro Tajiri,
  • Naruhiko Shiratori,
  • Shotaro Imai,
  • Kenichiro Mitsunari,
  • Yasuo Nakata,
  • Sachio Hirokawa,
  • Masao Mori

摘要

This paper extends the Lifelong Sustainable Inquiry-based Community Learning (LSiCL) framework, based on Eduinformatics, to address challenges and opportunities presented by the rise of Large Language Models (LLMs) in education. LLMs like ChatGPT are fundamentally transforming education, with human-LLM and inter-LLM learning relationships opening new frontiers. This research explores whether the traditional human-to-human LSiCL can be meaningfully extended to encompass human-LLM and LLM-LLM interactions while retaining its core principle of collaborative inquiry. Our analysis demonstrates that LSiCL principles are applicable across all these configurations, paving the way for sustainable and scalable Human-LLM Learning ecosystems. This framework is crucial for improving resource efficiency, promoting educational equity, and enhancing global educational sustainability.