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This study investigates the intersection between generative AI, sustainable development and education by conducting a systematic review on the utilization of ChatGPT and other Large Language Models (LLMs) in relation to Sustainable Development Goals (SDGs), focusing on SDG 4 and SDG 12. The article explores how technology-enhanced learning and LLMs are challenging pedagogical traditions, practices, even ethical dilemmas, and calls for responsible governance. There were 59 peer-reviewed articles included between 2023–2025 which provided data synthesis. Three core clusters were discerned using the VOSviewer keyword co-occurrence map: (1) educational impact and learning outcomes, (2) stakeholder ethics and governance perspectives, and (3) integrity Challenges of AI-assisted instruction. The study also raises alarms about threats that could emerge from misinformation, academic cheating, algorithmic bias and unequal access. It also underscores the lesser-known environmental footprint of AI tools.

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Using ChatGPT for Sustainable Development Education: Potentials, Risks, and Governance Needs

  • 会议论文

  • 发表日期：

    2026年

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  • Payel Das,
  • Tejaswini Seelam,
  • Rajeswari Annam

摘要

This study investigates the intersection between generative AI, sustainable development and education by conducting a systematic review on the utilization of ChatGPT and other Large Language Models (LLMs) in relation to Sustainable Development Goals (SDGs), focusing on SDG 4 and SDG 12. The article explores how technology-enhanced learning and LLMs are challenging pedagogical traditions, practices, even ethical dilemmas, and calls for responsible governance. There were 59 peer-reviewed articles included between 2023–2025 which provided data synthesis. Three core clusters were discerned using the VOSviewer keyword co-occurrence map: (1) educational impact and learning outcomes, (2) stakeholder ethics and governance perspectives, and (3) integrity Challenges of AI-assisted instruction. The study also raises alarms about threats that could emerge from misinformation, academic cheating, algorithmic bias and unequal access. It also underscores the lesser-known environmental footprint of AI tools.