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      <image:title>Writing - Qualitative Researchers Are Not OK: adapting to the use of AI by research respondents</image:title>
      <image:caption>This post first appeared on the MERL Tech Initiative blog, which I recommend following if you like interesting, intelligent writing at the intersection of digital development, AI, and MERL. Back in 2024, a mere year ago (and yet light years away in AI terms), Becky Zelikson, a member of the AI Ethics and Governance Working Group (EGWG) at the NLP-CoP, flagged yet another micro-phenomenon linked to the advent of GenAI: the detection of AI-generated responses in survey data. During a subsequent live chat on Slack, members exchanged questions and theories: How widespread was this practice in the context of research in LMICs? Is this the end of remote research? Should we see this as an indicator of ‘fraudulent’ behaviour, or as a marker of cultural differences when it comes to the legitimacy of opinions vs ‘right’ answers? Or was this emerging phenomenon a positive leveller, in that it enabled respondents to overcome language barriers?</image:caption>
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      <image:title>Writing - What can we learn from emerging evidence of GenAI use in Social and Behaviour Change chatbots? - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Writing - What can we learn from emerging evidence of GenAI use in Social and Behaviour Change chatbots?</image:title>
      <image:caption>This post first appeared on the MERL Tech Initiative blog, which I recommend following if you like interesting, intelligent writing at the intersection of digital development, AI, and MERL. Back in March 2024, MERL Tech Initiative, supported by iMedia Associated and the Gates Foundation, set out to define a research agenda on the use of Generative AI within Social and Behaviour Change (SBC) programming in development work. Over 50 organisations were represented at our workshop during the ICT4D Conference in Accra, and it rapidly became clear that it was way too soon to develop a nuanced research agenda when many practitioners were still grappling with foundational questions like “what even is GenAI?” and “how could it be used in SBC”. At this time, there weren’t enough anecdotes, let alone case studies or data, to start drawing any meaningful conclusions as to how this new technology was influencing our field – with the exception of frontrunners such as Viamo and Data Science Nigeria.</image:caption>
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