🌍 AI in evaluation: Opportunity or oversimplification for evidence use?
A recent reflection by
@TwendeMnE challenges the evidence-informed decision-making (EIDM) community to think beyond the hype of AI and focus on what truly matters for evaluation and evidence use.
As AI tools become more integrated into our workflows, they are undeniably improving speed and efficiency, supporting everything from data synthesis to report drafting. But as highlighted by the author, faster does not automatically mean better.
⚖️ For
#AENmembers and the wider
#EIDMcommunity, a few critical insights stand out:
🔹 What’s changing:
AI is reshaping how quickly evaluation tasks can be completed.
🔹 What isn’t:
The fundamentals—rigor, critical thinking, and contextual understanding—remain non-negotiable.
🔹 What matters most:
• Credibility must not be sacrificed for convenience
• Context cannot be automated; local knowledge remains essential.
• Evidence must be translated into usable, decision-friendly formats.
• AI should strengthen, not replace human expertise.
🚀 The real question for our community is not whether to use AI, but how to ensure it strengthens evidence uptake and real-world impact.
💬 A reflection for our
#AENmembers:
How can we intentionally use AI to make evidence more accessible, relevant, and actionable across African contexts, while maintaining the integrity that underpins
#EIDM?
🔗 Read the full reflection:
twendembele.org/using-ai-in-….
#AI #Evaluation #EvidenceUse #EvidenceToAction #ResearchImpact #AfricaEvaluation #KnowledgeTranslation