This chapter explores how emerging AI technologies can shape knowledge dissemination strategies aligned with the SDGs. Building on the previous chapter’s foundation of ICT-based extension, we examine how SAWBO integrates these technologies to improve content delivery, optimize gender inclusion in agricultural trainings, and increase global reach. A case study from Bangladesh illustrates how machine learning can analyze contextual variables to improve both participation and gender equity in extension events. We also highlight SAWBO’s use of AI-powered tools in video distribution via social media and app-based platforms, as well as the challenges posed by automatic language translation for scientific accuracy. While acknowledging the risks of AI and the complexities of digital divides, we argue that these tools—if deployed ethically and empirically—can significantly advance the right to knowledge. We conclude with a call for future research into scalable, accurate, and equitable approaches for AI-driven knowledge systems that serve underserved populations globally.

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How ICT-Based Big Data, Machine Learning, and AI Support the Achievement of the Sustainable Development Goals: The Case of SAWBO

  • Julia Bello-Bravo,
  • Anne Namatsi Lutomia,
  • John William Medendorp,
  • Barry Robert Pittendrigh

摘要

This chapter explores how emerging AI technologies can shape knowledge dissemination strategies aligned with the SDGs. Building on the previous chapter’s foundation of ICT-based extension, we examine how SAWBO integrates these technologies to improve content delivery, optimize gender inclusion in agricultural trainings, and increase global reach. A case study from Bangladesh illustrates how machine learning can analyze contextual variables to improve both participation and gender equity in extension events. We also highlight SAWBO’s use of AI-powered tools in video distribution via social media and app-based platforms, as well as the challenges posed by automatic language translation for scientific accuracy. While acknowledging the risks of AI and the complexities of digital divides, we argue that these tools—if deployed ethically and empirically—can significantly advance the right to knowledge. We conclude with a call for future research into scalable, accurate, and equitable approaches for AI-driven knowledge systems that serve underserved populations globally.