The findings and experiences presented throughout this book underline one central message: generative AI (GenAI) is neither a threat to academic integrity nor a solution to student learning challenges. Rather, it should be framed as a knowledge collaborator with non-substantial contributions that enhance learning when integrated responsibly into the design of teaching, learning activities, assessments, and evaluation frameworks. Importantly, these integrations must be accompanied by systematic monitoring and evaluation (M&E) to ensure that they deliver meaningful, measurable impacts, both in short and long terms. In this chapter, the authors bring these threads together, offering practical recommendations and future directions for educators, institutions, and policymakers who wish to harness GenAI responsibly and effectively.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Recommendations and Future Directions

  • Varun Gupta,
  • Chetna Gupta

摘要

The findings and experiences presented throughout this book underline one central message: generative AI (GenAI) is neither a threat to academic integrity nor a solution to student learning challenges. Rather, it should be framed as a knowledge collaborator with non-substantial contributions that enhance learning when integrated responsibly into the design of teaching, learning activities, assessments, and evaluation frameworks. Importantly, these integrations must be accompanied by systematic monitoring and evaluation (M&E) to ensure that they deliver meaningful, measurable impacts, both in short and long terms. In this chapter, the authors bring these threads together, offering practical recommendations and future directions for educators, institutions, and policymakers who wish to harness GenAI responsibly and effectively.