AI-Enhanced Sense-Making for Complexity-Aware Evaluation
Webinar | Online
-
Organized by:
ESLA Loops
- In partnership with: Raquel Rubio Rodriguez
About the Event
As development challenges become more systemic, politically sensitive, and uncertain, evaluators face a growing paradox: more data is available than ever before, yet making credible, timely sense of it is increasingly difficult. This webinar introduces a complexity-aware evidence sense-making approach that combines structured human reflection with AI-enabled pattern recognition. Presenters will share a real example of testing and implementing this approach, its strengths and challenges. By the end of the session, participants will gain practical insights into when and how AI can add real value to evaluation in complex global challenges, and how to integrate AI-enhanced sense-making approaches while preserving methodological integrity, stakeholder voice, and trust in evidence.
Meeting ID: 818 7956 5834
Passcode: 481206
Meeting ID: 818 7956 5834
Passcode: 481206
Speakers
| 名称 | 标题 | Biography |
|---|---|---|
| Robbie Gregorowski | Speaker | With nearly two decades of global experience in monitoring, evaluation, and learning (MEL), I have supported international organizations, foundations, and policy actors across sectors including climate resilience, low-carbon transition, and youth empowerment. My work focuses on strengthening how stakeholders interpret evidence in complex and uncertain contexts, ensuring that learning processes are as valued as evaluation outputs. |
| Raquel Rubio | Speaker | I work at the intersection of strategy, evidence, and complexity — helping organizations make confident decisions when data is messy, stakeholder perspectives diverge, and implementation realities challenge plans. Over the past 15+ years working globally in monitoring, evaluation, and learning (MEL) in international development and social impact, I have supported donors, NGOs, and private sector partners to strengthen how evidence informs action. |