EvalforEarth’s recent discussions present a consistent message: Atificial intelligence can strengthen evaluation practice, but only when evaluators remain firmly responsible for judgment, validation, ethics, and contextual interpretation. Across the community’s webinars, technical guidance, and discussion materials, AI is treated not as a substitute for evaluators, but as a set of tools that can improve efficiency, synthesis, and communication when used with discipline and transparency. My take on www.weitzenegger.de
Why this debate matters
The current EvalforEarth conversation places trust at the center of AI-enabled evaluation. The June 2026 webinars on trustworthy evidence argues that credibility must be built into evaluation design from the outset, rather than added later as a corrective step. This is especially important in policy and development settings where weak evidence can distort resource allocation, accountability, and learning.
A parallel strand of discussion focuses on practical adaptation. EvalforEarth’s 2025 technical note frames AI as relevant across the evaluation lifecycle, particularly in design, analysis, and communication, while insisting on accountable, transparent, and inclusive use. The wider community discussion on AI in evaluation likewise highlights that large-scale data processing and pattern recognition can support evaluators, but that human expertise remains essential for interpretation and validity checks.
Main messages from the recorded discussions
The recorded EvalForward session on applying AI in evaluation emphasizes both opportunity and restraint. The discussion notes that AI can help process existing data, support analytical tasks, and speed up parts of the workflow, but it also warns that AI systems rely on existing data and algorithms, may generate erroneous results, and require users to exercise caution and discretion. One core takeaway is that responsible and ethical AI use depends on understanding limitations and complementing automation with human oversight.
This message has become more concrete in ch’s more recent reflections. A 2026 community blog synthesizing interagency practice exchange discussions identifies six lessons, including that AI can help evaluators “do more with less” by shifting effort from mechanics to meaning, that the strongest use cases are those that extend evaluators’ reach rather than replace them, and that human insight becomes a defining feature of quality in an AI-enabled environment. The same synthesis also underlines major risks, including structural bias, exclusion of local voices, privacy concerns, weak or unverifiable evidence, and widening inequality in access and capacity
Practical uses of AI in evaluation
Across the material reviewed, the most convincing applications are pragmatic rather than speculative. EvalforEarth discussions repeatedly point to desk reviews, evidence synthesis, coding support, analysis of large document sets, translation, communication products, and report drafting as areas where AI can add value. These are tasks where speed and scale matter, but where final accountability must still rest with evaluators.
The technical note on AI in evaluations reinforces this practical orientation. It highlights AI’s potential to enhance evaluation design, analysis, and communication in human-centered ways, while encouraging experimentation that remains purposeful, critical, and well documented. A particularly important lesson from the EvalforEarth ecosystem is that AI adds the most value when it is embedded in question-driven workflows that keep evaluators in charge.
Ethics, trust, and evaluator responsibility
The strongest common thread across recent discussions is that AI raises methodological and ethical questions that cannot be outsourced to software vendors or technical specialists. EvalforEarth’s June 2026 webinar explicitly points to transparency, bias, data protection, and the possible erosion of professional judgment as central concerns. It also stresses that unequal access to technology and external control over digital systems may create additional challenges, especially in contexts where local ownership of evidence is already fragile.
The community’s 2026 synthesis goes a step further by calling for rights-based governance, inclusive design, local relevance, and long-term safeguards. It argues that AI-supported evaluation should be co-designed with communities, grounded in locally relevant data, and supported by sustained investment in capacity, so that AI strengthens rather than overrides local knowledge. This point is especially relevant for development evaluation, where credibility depends not only on technical rigor but also on fairness, participation, and contextual legitimacy.
Implications for evaluation practice
For evaluators, the emerging lesson is not simply to learn new tools, but to redefine quality in an AI-supported environment. Good evaluation is presented as timely, strategically focused, evidence-informed, influential, and deeply human. In practical terms, this means validating AI-generated outputs, documenting prompts and coding rules, disclosing decision protocols, and preserving space for professional judgment and dialogue with stakeholders.
For commissioners and institutions, recent EvalforEarth discussions suggest a governance agenda as much as a technical one. AI can improve evidence mapping, identify gaps, and support more efficient evaluation processes, but institutions need clear norms on disclosure, verification, procurement, ethics, and consultant responsibilities. Without these safeguards, efficiency gains may come at the expense of trust.
Note for weitzenegger.de readers
If you are curious about practical use cases, lessons from peers, and how to keep evaluations both credible and future-ready, have a look at the article and join the conversation:
👉 Post on Weitzenegger.de: https://www.weitzenegger.de/content/
👉 Further reflections on my DevEval blog: https://deveval.wordpress.com/
As a member of EvalforEarth, I also encourage you to connect directly with the community:
🔗 EvalforEarth website: https://www.evalforearth.org
🔗 EvalforEarth blog: https://www.evalforearth.org/blog
🔗 EvalforEarth on YouTube: https://youtube.com/@evalforearth2025?si=HFyCJoQj1Oo5sJ8G
🔗 EvalforEarth on LinkedIn: https://www.linkedin.com/company/evalforearth/
🔗 EvalforEarth on BlueSky: https://bsky.app/profile/evalforearth.bsky.social/
Join the mailing list: evalforearth.dgroups.io/g/evalforearth
I would be very interested to hear your experiences: How are you already using – or deliberately not using – AI in your evaluation work?
Transparency note: The author was not involved in the study discussed here. This article reflects solely the author’s views and cannot be attributed to any organization or faith. Artificial intelligence was used to assist in the creation of the text and images. Errare humanum manet. Anyone who spots the deliberate factual error can have it verified by sending us a message and may expect a reward.