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The study explores leveraging Large Language Models (LLMs) and prompt engineering to analyze feedback from vocational skill training programs, utilizing the TVET Monitoring Application (TMAP) Suite to enhance data utilization in social work and women’s empowerment initiatives. By applying techniques like contextual and instructional prompting, researchers demonstrated LLMs’ potential to provide real-time monitoring and generate actionable insights for program coordinators. The approach could enable social intervention implementers with limited technical skills to streamline feedback analysis, analyzing training session aspects such as teaching quality, infrastructure, and participation. Despite some limitations in response consistency, the research highlights the promise of bridging technological advancements with social work practices, offering a novel method for improving intervention effectiveness.

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Empowering Social Interventions with Prompt Engineering: Insights from Vocational Training Feedback

  • 会议论文

  • 发表日期：

    2026年

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  • Aswathi Padmavilochanan,
  • Lekha Shekhar,
  • Srividya Sheshadri

摘要

The study explores leveraging Large Language Models (LLMs) and prompt engineering to analyze feedback from vocational skill training programs, utilizing the TVET Monitoring Application (TMAP) Suite to enhance data utilization in social work and women’s empowerment initiatives. By applying techniques like contextual and instructional prompting, researchers demonstrated LLMs’ potential to provide real-time monitoring and generate actionable insights for program coordinators. The approach could enable social intervention implementers with limited technical skills to streamline feedback analysis, analyzing training session aspects such as teaching quality, infrastructure, and participation. Despite some limitations in response consistency, the research highlights the promise of bridging technological advancements with social work practices, offering a novel method for improving intervention effectiveness.