AI-Enabled Research and Evaluation Specialist
AI-enhanced version of Research and Evaluation Specialist
AI-Enabled Research and Evaluation Specialist
Designs and carries out research and evaluation studies across their full cycle, from evaluation questions and methodology through data collection and analysis to evidence synthesis, and decides where AI fits in each stage. The role exists because organizations want to use AI to speed up research without losing methodological rigour, and someone has to own both the design and the quality of AI-supported results.
- Level
- Specialist 3–6 years
- Works with AI
- Integrates AI
- Career family
- Research, Impact Evaluation and Evidence
- Key tools
- Claude, ChatGPT, LLM APIs (OpenAI, Anthropic, Google) +7
Without AI
The role today: Research and Evaluation Specialist
The core responsibilities, as most job descriptions still list them.
- Develop evaluation and research questions, evaluation matrices and methodologies aligned with OECD-DAC criteria and the programme theory of change.
- Choose and justify study designs, sampling strategies and data collection methods for each assignment.
- Map each stage of the research workflow and decide where AI tools are used, what they produce and how outputs are checked.
- Write AI-use protocols covering approved tools, data protection rules, prompt records and human review steps.
- Design and pilot quantitative and qualitative instruments, including AI-assisted translation checks.
- Lead analysis of survey, interview and administrative data, combining AI-assisted coding with manual verification.
- Synthesise findings across data sources and prior studies into evaluation reports and learning products.
- Document every AI-supported step in the methods section so that reviewers can assess its reliability.
- Coach programme and M&E staff on using AI in research without weakening evidence quality.
What AI changes
The specialist moves from doing each research step by hand to designing a workflow in which AI handles transcription, first-pass coding, document review and drafting, with defined human checkpoints. Accountability for design choices, validity of findings and transparent reporting of AI use stays with the specialist.
With AI
The role with AI: AI-Enabled Research and Evaluation Specialist
What the job looks like once AI is part of the daily work.
- Design research workflows that specify which steps use AI, with validation samples and acceptance thresholds for each.
- Use AI to review programme documents during inception and draft the evaluability assessment for checking.
- Set up AI-assisted transcription and translation pipelines for KIIs and FGDs, with spot checks by fluent reviewers.
- Run AI-assisted first-pass coding against an agreed codebook and measure agreement with human coders before scaling up.
- Use AI to triangulate findings across sources in an evaluation matrix, then verify every link to evidence by hand.
- Keep a log of prompts, model versions and corrections so AI-supported analysis can be audited.
- Write methods-section text that discloses AI use, its limits and the checks applied.
A typical week
4 of these 5 tasks already involve AI.
- Drafting the evaluation matrix for a four-country resilience programme and mapping each question to data sources.
- AI-assisted task: Comparing AI-assisted codes with a colleague's manual codes on 40 transcripts and calculating Cohen's kappa.
- AI-assisted task: Updating the team's AI-use protocol after a partner requested that no transcripts leave their servers.
- AI-assisted task: Reviewing an analyst's AI-generated synthesis table and tracing five claims back to source documents.
- AI-assisted task: Agreeing with the commissioning manager how AI use will be described in the inception report.
What stays human
- Choosing the evaluation design and deciding what the evidence can and cannot support.
- Setting acceptable error rates for AI-assisted steps and stopping their use when they are not met.
- Judging whether AI use is appropriate for sensitive data, vulnerable respondents or partner restrictions.
- Interpreting findings with stakeholders and making evaluative judgements against the criteria.
Skills gap check · 2 minutes
Research and Evaluation Specialist jobs are being redesigned around AI. Are your skills ready?
Employers increasingly expect M&E professionals to work confidently with AI. Answer 8 quick questions to see where you stand against the “AI-Enabled Research and Evaluation Specialist” role, and what to learn before the gap costs you your next opportunity.
- Your AI readiness level
- Your skills gap for this role
- What to learn first
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AI in practice
Real examples of how people in this role use AI.
During inception, Claude reviews 60 programme documents and drafts a table of stated outcomes, assumptions and data availability for each evaluation question; the specialist checks every entry against the source page before the inception report is submitted.
Whisper transcribes 48 KIIs, an LLM applies a 25-code codebook, and two analysts hand-code 10 transcripts; coding is scaled up only after agreement reaches kappa 0.7 on every high-frequency code.
For each of 14 sub-questions, an AI assistant links survey tables, coded interview excerpts and document extracts to the evaluation matrix, and the specialist rates the strength of evidence for each judgement manually.
Career path
Where people usually come from, and where this role can lead.
Before
This role
Skills
Most important first within each group.
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M&E methodology
Ethics, data protection and governance
Professional
What to learn first
In priority order for someone moving into this role.
- Validating AI outputs — The specialist sets the validation rules that every AI-supported step in a study must pass.
- Data protection and privacy — Choosing whether interview data can go to a cloud AI service is now a routine design decision.
- Evaluating AI systems — Measuring AI coding and extraction accuracy against a human-coded sample is the core of a defensible workflow.
- Theory-based evaluation — Contribution analysis and process tracing keep AI-assisted synthesis tied to causal reasoning rather than word frequency.
- MAXQDA — Its AI features and inter-coder agreement functions make it a practical base for validated qualitative workflows.
Software and tools
Tool names open the official website in a new tab. “Roles” shows other careers that use the tool.
Data analysis
Data collection
Information systems
Qualitative analysis
Literature and evidence
AI development
- LLM APIs (OpenAI, Anthropic, Google) 36 roles
Standards and frameworks
Summary in the EvalCommunity framework: Integrate AI into research design, evaluation methodology, data collection, analysis and evidence synthesis.
The AI-enhanced title is a proposed career title, not a standardized occupational classification.
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