AI-Assisted Evaluation Literature Reviews and Evidence Mapping Training Course

Course Introduction

AI-Assisted Evaluation Literature Reviews and Evidence Mapping Training Course equips participants with the methodological knowledge and practical skills required to use artificial intelligence in evidence identification, literature review, evidence classification, and synthesis for programme evaluation and development decision-making. Participants explore how AI can support demanding review tasks while maintaining methodological rigour, transparency, traceability, and professional judgement. The course emphasizes the responsible integration of AI into established evidence synthesis methods rather than treating automated outputs as substitutes for research expertise.

The course introduces the different forms of evidence synthesis used in evaluation, including narrative literature reviews, rapid reviews, scoping reviews, systematic reviews, and evidence and gap mapping. Participants learn how to match a review approach to an evaluation question, define appropriate boundaries, establish inclusion and exclusion criteria, and develop a clear protocol before using AI tools. Particular attention is given to the needs of development programmes, humanitarian interventions, public services, social protection, education, health, livelihoods, governance, and other sectors where evidence must inform practical decisions.

Participants learn to use AI-assisted workflows to refine research questions, develop search concepts, generate alternative search terms, organize bibliographic records, and prepare preliminary screening and data-extraction structures. Practical exercises demonstrate how to ask AI systems to summarize study characteristics, extract relevant information from accessible documents, identify recurring themes, and populate structured evidence tables. The course also addresses the limitations of these techniques, including fabricated references, inaccurate quotations, incomplete extraction, missed studies, and misleading summaries, reinforcing the importance of checking every consequential claim against its original source.

A central component of the course is evidence mapping: the systematic organization of available research according to categories such as intervention type, population, geography, evaluation design, outcome area, implementation context, and study quality. Participants learn how to develop a coding framework, establish consistent classification rules, identify concentrations of research, and distinguish genuine evidence gaps from gaps caused by limited search coverage or inaccessible publications. They practise transforming coded information into evidence matrices, descriptive summaries, and visual representations that help programme managers, commissioners, researchers, and policymakers understand the scope of available knowledge.

The training also addresses research integrity, data protection, intellectual property, confidentiality, bias, and responsible use of generative AI. Participants examine how automated tools can reproduce limitations in published research, overlook local and non-English evidence, misinterpret contextual findings, or present uncertain conclusions with unwarranted confidence. They learn to preserve source references, maintain audit trails, document AI-assisted decisions, protect sensitive information, and establish human review checkpoints. Reporting principles, including relevant PRISMA guidance where applicable, are considered alongside the distinct purposes and reporting needs of evidence maps and other forms of evidence synthesis.

By the end of the course, participants will have developed a practical workflow that connects an evaluation question to a documented search strategy, screened literature, structured extraction records, a preliminary evidence map, and a concise findings report. Through guided exercises and a capstone assignment, they will practise assessing the suitability of AI outputs, recording verification decisions, communicating uncertainty, and presenting findings for different audiences. The intended result is a more efficient and defensible approach to literature review and evidence mapping that strengthens evaluation planning, learning, programme adaptation, and evidence-informed decision-making.

Duration

5 days

Who Should Attend

• Monitoring, Evaluation, Accountability and Learning (MEAL) managers seeking practical approaches to reviewing evaluation literature and organizing evidence for programme decisions.

• Evaluation specialists and consultants responsible for background studies, evaluation designs, evidence reviews, inception reports, and synthesis of findings across multiple interventions.

• Research officers and research assistants who collect, screen, classify, extract, and summarize published and grey literature for development or humanitarian assignments.

• Programme managers and technical advisers who need to understand the strength, coverage, and limitations of existing evidence before designing or scaling interventions.

• Monitoring and evaluation officers working with theories of change, results frameworks, indicators, outcome evidence, and programme learning questions.

• Policy analysts and evidence advisers preparing research briefs, policy notes, evidence summaries, and recommendations for decision-makers.

• Learning and knowledge management specialists responsible for capturing lessons, organizing research outputs, and making evaluation findings accessible to internal and external stakeholders.

• Impact evaluation professionals who need to identify previous studies, compare evaluation designs, and assess what is known about programme effectiveness.

• Development practitioners working in education, health, agriculture, livelihoods, governance, social protection, climate resilience, gender equality, or humanitarian response.

• Donor representatives and programme commissioners who review evidence to inform funding priorities, evaluation terms of reference, and strategic investment decisions.

• Academic researchers and postgraduate students undertaking scoping reviews, literature reviews, evidence mapping, or applied research in development-related fields.

• Information and knowledge management officers who maintain research repositories, bibliographic libraries, evidence databases, and organizational learning platforms.

• Data analysts and information specialists who want to combine AI-assisted document processing with structured coding, tabulation, and visualization of research evidence.

• Civil society organizations and local research institutions seeking affordable, transparent approaches to synthesizing evidence across programmes and geographic settings.

• Research team leaders who need to establish quality assurance procedures, division of responsibilities, and reproducible workflows for AI-supported reviews.

• Humanitarian assessment and evaluation teams that need to synthesize available contextual research, intervention evidence, and lessons from previous emergencies.

• Gender, disability, inclusion, and safeguarding specialists who want to assess how existing literature addresses different population groups and whose experiences are underrepresented.

• Consultants and technical professionals seeking to improve the efficiency, quality, and transparency of evidence products prepared for clients, funders, and implementing organizations.

Course Objectives

• Explain the purposes, strengths, limitations, and appropriate applications of narrative reviews, rapid reviews, scoping reviews, systematic reviews, and evidence and gap maps in evaluation and development practice.

• Formulate clear and answerable evaluation-related research questions and translate them into documented review protocols, eligibility criteria, search concepts, and evidence classification frameworks.

• Apply AI-assisted prompting techniques to develop search terms, expand synonyms, structure research questions, and support review planning without treating generated suggestions as verified research evidence.

• Design systematic and reproducible literature-search workflows that combine relevant databases, institutional repositories, grey literature sources, citation tracking, and documented search records.

• Use AI-assisted methods to support bibliographic organization, duplicate identification, preliminary screening, and prioritization while retaining human oversight of inclusion and exclusion decisions.

• Extract and structure information on study characteristics, evaluation methods, populations, intervention components, outcomes, implementation conditions, limitations, and reported findings.

• Develop and apply evidence-mapping taxonomies and coding frameworks that classify literature consistently across programme themes, geographies, population groups, research designs, and outcome categories.

• Assess the credibility, relevance, completeness, and limitations of AI-generated summaries and extracted information through source verification, structured quality checks, and documented review procedures.

• Produce evidence matrices, descriptive summaries, gap analyses, and visual reporting outputs that accurately represent the distribution and limitations of the available literature.

• Establish responsible AI governance procedures covering transparency, confidentiality, data protection, bias management, citation integrity, auditability, and human accountability throughout the review process.

Comprehensive Course Outline

Module 1: Foundations of Evaluation Literature Reviews and Evidence Mapping

• Distinguish narrative, rapid, scoping, systematic, and mapping reviews, including their respective purposes, methodological expectations, and suitability for different evaluation questions.

• Identify how evidence reviews support evaluation design, programme theories, intervention selection, policy development, learning agendas, and evidence-informed resource allocation.

• Examine the relationship between research questions, review scope, evidence sources, inclusion criteria, analytical approaches, and intended users of the findings.

• Review the strengths and limitations of AI-assisted evidence synthesis and identify tasks that require specialist judgement, independent verification, or established review procedures.

Module 2: AI Tools, Prompt Design, and Review Workflow Planning

• Identify suitable AI-assisted capabilities for question refinement, search-term generation, document summarization, preliminary classification, structured extraction, and report drafting.

• Construct clear prompts that specify the review objective, context, permitted sources, output format, inclusion rules, uncertainty requirements, and citation expectations.

• Design reusable prompt templates for recurring review tasks and compare generated outputs against predefined accuracy, relevance, and completeness criteria.

• Establish an end-to-end workflow with human approval checkpoints, version control, documentation, and clear boundaries between automated assistance and researcher decisions.

Module 3: Research Questions, Protocols, and Search Strategy Development

• Translate evaluation priorities into focused review questions using suitable frameworks, such as population, intervention, comparison, outcome, and setting where appropriate.

• Develop protocols defining objectives, scope, eligibility criteria, evidence types, geographic coverage, publication periods, language considerations, and planned synthesis methods.

• Use AI to propose synonyms, alternative terminology, subject concepts, and Boolean search combinations, then validate and adapt search strings for each source.

• Document databases, repositories, search dates, complete search strings, citation-search procedures, and protocol amendments to improve transparency and reproducibility.

Module 4: Literature Discovery, Bibliographic Management, and Screening

• Identify relevant academic databases, evaluation repositories, institutional websites, donor publications, government reports, and other sources of grey literature.

• Organize search results using bibliographic management tools and spreadsheets, including reference metadata, persistent identifiers, source links, and duplicate records.

• Develop screening criteria and use AI-assisted classification to prioritize records for review while requiring human decisions for ambiguous or consequential cases.

• Maintain screening logs, exclusion reasons, reviewer notes, and record counts that support transparent reporting and appropriate use of PRISMA flow diagrams where applicable.

Module 5: AI-Assisted Document Review and Structured Data Extraction

• Design standardized extraction forms covering publication details, study design, location, population, intervention, implementation context, outcomes, findings, and limitations.

• Use document-grounded prompts to extract information from accessible full texts while requiring page references, section identifiers, or exact supporting passages where feasible.

• Compare extracted values against original documents and record missing, ambiguous, conflicting, or unreported information without allowing AI to fill gaps through unsupported inference.

• Establish extraction quality checks through pilot testing, reviewer comparison, consistency checks, and documented corrections before processing larger collections of studies.

Module 6: Evidence Coding, Classification, and Thematic Synthesis

• Develop a coding framework aligned with the review question, including intervention categories, outcome domains, populations, geographies, research designs, and implementation factors.

• Apply consistent coding rules to qualitative, quantitative, mixed-methods, and programme evaluation studies while preserving important contextual differences.

• Use AI to propose preliminary themes and identify recurring concepts, then validate categories against source material and refine them through transparent analytical judgement.

• Examine disagreements, overlapping categories, missing data, and coding uncertainty to improve consistency and prevent oversimplified interpretation of diverse findings.

Module 7: Evidence Mapping, Gap Identification, and Descriptive Analysis

• Construct evidence matrices that cross-tabulate intervention types, outcomes, study designs, locations, population groups, or other dimensions relevant to evaluation decisions.

• Create evidence and gap maps that show where studies are concentrated, which topics are less represented, and which combinations of intervention and outcome have limited coverage.

• Generate descriptive counts, tables, charts, and map-ready datasets while distinguishing the volume of evidence from the strength, quality, or consistency of its findings.

• Interpret apparent evidence gaps in light of search limitations, language and publication bias, accessibility constraints, classification choices, and uneven research coverage.

Module 8: Critical Appraisal, Verification, and AI Quality Assurance

• Apply appropriate critical appraisal approaches to assess methodological quality, risk of bias, relevance, and limitations according to the types of studies included.

• Verify AI-generated summaries, citations, quotations, study characteristics, numerical results, and conclusions against original publications and authoritative records.

• Identify hallucinated references, unsupported causal claims, omitted contradictory findings, misclassified studies, and language that overstates the certainty of available evidence.

• Maintain audit trails documenting prompt versions, model-assisted tasks, reviewer checks, corrections, unresolved uncertainties, and decisions that materially affect the review.

Module 9: Responsible AI, Ethics, and Transparent Reporting

• Establish safeguards for confidentiality, sensitive programme information, personal data, unpublished research, contractual restrictions, and organizational AI policies.

• Examine representation bias, language limitations, unequal visibility of local research, and the risk of excluding marginalized populations from evidence summaries and maps.

• Prepare methods sections that transparently describe search procedures, eligibility decisions, AI use, human verification, analytical methods, limitations, and departures from planned protocols.

• Communicate findings with appropriate caveats, distinguish descriptive mapping from effectiveness conclusions, and apply relevant reporting standards without misrepresenting compliance.

Module 10: Practical Capstone, Reporting, and Evidence-to-Decision Use

• Develop a mini-review protocol for a realistic programme evaluation question, including the intended audience, search plan, eligibility rules, and preliminary evidence taxonomy.

• Process a sample literature set to demonstrate screening, structured extraction, verification, coding, and construction of an evidence matrix.

• Produce a concise evidence map, gap analysis, and findings brief that communicate the distribution of evidence, key limitations, and priorities for further investigation.

• Present the final workflow and recommendations, incorporating peer feedback and a quality assurance checklist for implementation in participants’ own organizations.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.