Course Introduction
The AI-Assisted Systematic Reviews and Evidence Synthesis Training Course equips participants with the knowledge and practical skills required to integrate artificial intelligence into systematic review workflows while preserving methodological rigour, transparency, and reproducibility. As research outputs expand across disciplines, evidence synthesis professionals increasingly need efficient approaches to searching, screening, extracting, appraising, and synthesizing large volumes of literature. This course explores how AI-enabled tools can support these activities without replacing critical human judgement or established systematic review standards.
Systematic reviews and evidence syntheses inform public policy, clinical practice, development programming, education, environmental management, and institutional decision-making. However, traditional review processes can be resource-intensive, particularly when researchers must manage thousands of records, diverse publication formats, complex eligibility criteria, and rapidly evolving evidence. Participants will examine how AI-assisted methods can reduce repetitive workloads, support structured information management, and improve workflow efficiency while maintaining clear documentation of decisions, limitations, and methodological assumptions.
The course introduces practical applications of natural language processing, machine learning, large language models, semantic search, and AI-assisted research discovery. Participants will learn how these technologies can help formulate research questions, refine search strategies, identify potentially relevant studies, prioritize screening decisions, and organize extracted information. Particular attention will be given to the distinction between AI-generated suggestions and verified evidence, helping participants recognize that automated outputs require appropriate validation before they can inform review conclusions.
A central component of the training focuses on methodological integrity, bias prevention, and responsible AI use throughout the evidence synthesis lifecycle. Participants will explore risks such as fabricated references, incorrect study classifications, omitted evidence, inaccurate extraction, model bias, and unsupported summaries. The course demonstrates practical approaches to human-in-the-loop review, verification against original publications, transparent recording of AI involvement, and independent quality checks. These safeguards help ensure that efficiency gains do not undermine the credibility, completeness, or reproducibility of systematic reviews.
Participants will also develop skills in evidence extraction, critical appraisal, qualitative synthesis, quantitative evidence preparation, and structured reporting using AI-assisted workflows. Through practical exercises and realistic research scenarios, they will learn to compare conventional and AI-supported methods, evaluate the reliability of automated outputs, and select suitable tools according to research objectives, data sensitivity, available resources, and methodological requirements. The emphasis is on building workflows that are auditable, adaptable, and aligned with recognized evidence synthesis practices.
By the end of the AI-Assisted Systematic Reviews and Evidence Synthesis Training Course, participants will be better prepared to plan and conduct responsible, efficient, and transparent evidence reviews in their professional settings. They will understand where AI can provide meaningful assistance, where expert intervention remains essential, and how to document limitations and validation procedures. The resulting capabilities can support stronger research products, more timely evidence-informed decisions, improved collaboration among review teams, and greater confidence in the findings communicated to policymakers, practitioners, researchers, and other stakeholders.
Course Duration
5 days
Who Should Attend
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Researchers and academics conducting systematic reviews, scoping reviews, rapid reviews, and other forms of evidence synthesis.
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Monitoring, evaluation, accountability, and learning professionals responsible for reviewing programme evidence and intervention effectiveness.
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Public health specialists and clinical researchers synthesizing evidence for healthcare policy, guidelines, and practice.
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Evidence synthesis specialists working in universities, research institutes, government agencies, and independent research organizations.
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Librarians and information specialists supporting scientific literature searches and evidence discovery.
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Policy analysts and public sector professionals preparing evidence reviews to inform policy development and implementation.
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Development practitioners and NGO researchers evaluating interventions, programme outcomes, and lessons learned.
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Research assistants and postgraduate students developing advanced literature review and evidence management skills.
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Knowledge management professionals organizing research outputs and translating evidence into usable insights.
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Epidemiologists and biostatisticians supporting evidence assessment and preparation for quantitative synthesis.
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Environmental and climate researchers reviewing scientific findings to support sustainability and adaptation decisions.
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Pharmaceutical, clinical research, and health technology assessment professionals evaluating published evidence.
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Research consultants managing literature reviews and evidence synthesis assignments for clients.
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Editors, technical advisors, and research managers responsible for reviewing the quality and transparency of evidence products.
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AI and data science professionals developing or evaluating tools for research discovery, literature screening, and evidence synthesis.
Course Objectives
By the end of this course, participants will be able to:
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Explain the principles, stages, and methodological requirements of systematic reviews and evidence synthesis, identifying where AI can provide useful assistance without compromising established review standards.
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Formulate clear, answerable research questions and translate them into structured eligibility criteria, search concepts, and review protocols suitable for AI-supported research workflows.
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Develop comprehensive literature search strategies that combine conventional bibliographic databases, scholarly discovery tools, semantic search, and AI-assisted retrieval while assessing coverage limitations.
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Apply AI-assisted screening methods to title, abstract, and full-text records, using explicit inclusion and exclusion criteria, human verification, and documented conflict-resolution procedures.
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Use large language models and specialized research tools to support structured data extraction while checking extracted findings against original publications and recording uncertainty.
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Assess the credibility, relevance, methodological quality, and risk of bias of included studies using appropriate appraisal frameworks and carefully validated AI-supported assistance.
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Organize extracted evidence for narrative, thematic, qualitative, and quantitative synthesis while distinguishing verified study findings from machine-generated interpretations or summaries.
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Evaluate AI tools for accuracy, reproducibility, transparency, data protection, cost, and suitability, selecting approaches that match the scope and sensitivity of each review project.
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Implement quality assurance procedures that address fabricated citations, missing studies, extraction errors, hallucinations, automation bias, and inconsistent outputs across different models or tools.
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Produce transparent and reproducible evidence synthesis reports that document search methods, screening decisions, AI involvement, validation steps, limitations, and implications for research or policy.
Comprehensive Course Outline
Module 1: Foundations of Systematic Reviews and AI-Assisted Evidence Synthesis
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Principles and distinguishing features of systematic reviews, scoping reviews, rapid reviews, realist reviews, and other evidence synthesis approaches.
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The systematic review lifecycle, from question formulation and protocol development through searching, screening, appraisal, synthesis, and reporting.
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Opportunities and limitations of artificial intelligence across evidence identification, classification, extraction, interpretation, and knowledge translation.
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Core principles of methodological transparency, reproducibility, human oversight, and evidence traceability in AI-supported research environments.
Module 2: Research Questions, Protocols, and Review Planning
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Formulating structured research questions using frameworks such as PICO, PICOS, SPIDER, and other approaches suited to different research disciplines.
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Translating research objectives into explicit eligibility criteria covering populations, interventions, comparators, outcomes, settings, designs, and publication characteristics.
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Using AI tools to refine review questions, identify conceptual gaps, draft protocol components, and generate alternative formulations for expert assessment.
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Establishing protocols, decision logs, team responsibilities, and predefined procedures for handling methodological changes during the review process.
Module 3: AI-Assisted Literature Searching and Research Discovery
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Designing database search strategies using controlled vocabularies, Boolean operators, truncation, synonyms, and discipline-specific indexing conventions.
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Applying semantic search, natural language queries, citation mapping, and AI-assisted discovery tools to identify potentially relevant studies beyond keyword matching.
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Combining scholarly databases, institutional repositories, grey literature, reference lists, and citation networks to improve the breadth of evidence identification.
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Evaluating search sensitivity, precision, database coverage, retrieval bias, and reproducibility while documenting queries, dates, sources, and search limitations.
Module 4: Reference Management, Deduplication, and Screening
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Importing, organizing, deduplicating, and tracking bibliographic records using reference managers and systematic review management platforms.
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Applying machine learning classifiers and AI-assisted prioritization to rank records according to their potential relevance to predefined review criteria.
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Developing consistent screening instructions and using pilot exercises to improve reviewer agreement and reduce inconsistent inclusion decisions.
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Combining automation with human verification, documenting exclusion reasons, resolving reviewer disagreements, and monitoring potentially missed eligible studies.
Module 5: Full-Text Retrieval and AI-Assisted Data Extraction
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Identifying, retrieving, and organizing full-text publications while recording unavailable reports, supplementary materials, and related study publications.
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Designing standardized extraction forms for study characteristics, methods, sample sizes, interventions, outcomes, effect estimates, and contextual information.
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Using large language models and document-processing tools to locate relevant passages, populate extraction fields, and identify information requiring further investigation.
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Verifying extracted values against source documents, recording page or section references, resolving discrepancies, and managing missing or ambiguously reported information.
Module 6: Critical Appraisal, Risk of Bias, and Evidence Quality
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Selecting appropriate critical appraisal and risk-of-bias frameworks according to study design, research question, and the intended use of review findings.
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Using AI-assisted workflows to organize methodological details and flag potential concerns without treating automated judgments as definitive quality assessments.
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Assessing selection bias, confounding, measurement limitations, selective reporting, missing data, and other threats to the validity of included studies.
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Documenting appraisal judgments, supporting evidence, reviewer disagreements, and uncertainty while maintaining expert responsibility for final quality assessments.
Module 7: Evidence Synthesis, Interpretation, and Quantitative Preparation
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Structuring extracted findings into evidence tables, thematic categories, study comparisons, and narrative summaries aligned with the review questions.
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Using AI-assisted coding to organize qualitative findings and identify candidate themes while validating interpretations against the original study context.
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Preparing quantitative evidence for meta-analysis by checking effect measures, sample sizes, standard errors, confidence intervals, and compatibility across studies.
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Interpreting heterogeneity, inconsistency, evidence gaps, and contradictory findings while preventing automated summaries from overstating causal relationships or certainty.
Module 8: Prompt Engineering and Large Language Models for Research Workflows
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Developing precise prompts that define the research task, eligibility criteria, output structure, source restrictions, and verification requirements for review activities.
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Using structured templates and constrained outputs to support repeatable screening assistance, data extraction, evidence comparison, and preliminary summarization.
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Testing model responses against known examples and source documents to identify hallucinations, omissions, inconsistent classifications, and sensitivity to prompt changes.
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Establishing reusable prompt libraries, version control, validation records, and escalation procedures for uncertain or high-impact research decisions.
Module 9: Responsible AI, Ethics, Privacy, and Research Integrity
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Identifying ethical and methodological risks associated with automation bias, algorithmic bias, opaque model behavior, and uneven representation in available literature.
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Protecting confidential manuscripts, unpublished findings, personal information, and licensed research content when selecting AI tools and processing documents.
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Developing disclosure practices for AI-assisted methods, including tool names, model versions where available, tasks performed, human checks, and known limitations.
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Establishing governance procedures for accountability, reproducibility, audit trails, intellectual property, research integrity, and responsible use of generated content.
Module 10: Reporting, Reproducibility, and Applied Review Project
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Preparing systematic review reports that clearly document research questions, protocols, information sources, search strategies, selection methods, and synthesis procedures.
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Integrating transparent screening records, flow diagrams, evidence tables, appraisal results, and appropriate reporting guidance into the final review output.
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Conducting an end-to-end practical exercise that applies AI assistance to a selected topic while retaining human oversight and traceable methodological decisions.
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Developing an implementation plan that defines workflow responsibilities, tool selection criteria, quality indicators, staff training needs, and continuous improvement priorities.
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.
