AI Tools for Researchers Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Research Officers, Policy Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-AI-038

Before you join: Participants should have practical experience conducting, commissioning, reviewing or using research.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Researchers are under pressure to find relevant evidence faster, manage growing volumes of literature, analyse mixed data sources and produce defensible written outputs. AI tools can reduce time spent on search formulation, screening, extraction, coding, drafting and administrative tasks. They can also introduce fabricated citations, weak source traceability, confidentiality breaches and biased outputs when used without clear controls. Professional researchers need repeatable methods for deciding where AI is suitable, how to verify outputs and how to document its use.

This course develops practical capability in AI-assisted research workflows. Participants learn to frame research questions using PICO, SPIDER and FINER; construct transparent database searches; use AI-supported evidence discovery; screen studies against predefined criteria; extract structured data; assess study quality; and analyse quantitative and qualitative findings. The course also addresses AI-supported academic writing, citation verification, prompt design, reproducibility, data protection, research integrity and AI-use disclosure.

Instruction combines short technical briefings with research cases, guided tool demonstrations, group review of evidence records, screening and extraction simulations, and application planning. Cases can be adapted for policy research, health research, market research, development programmes, banking, engineering, public services or corporate research functions. Participants leave with a documented AI-assisted research workflow, a prompt library, a verification checklist, a provenance log and a practical plan for applying approved tools to their own research work.

The course is suited to professionals who already conduct, commission, review or manage research and need sound methods for using AI without weakening evidential standards.

What you will be able to do

  • Design an AI-assisted research workflow with defined verification and approval points
  • Formulate research questions using PICO, SPIDER and FINER structures
  • Construct reproducible database searches using Boolean logic and documented search strings
  • Evaluate AI-generated evidence summaries against primary sources and persistent identifiers
  • Build structured data-extraction tables using controlled vocabularies and JSON schemas
  • Assess research quality using critical-appraisal methods and bias assessment criteria
  • Produce traceable AI-assisted research writing with verified citations and disclosures
  • Create an AI governance plan covering data classification, provenance and human review

Benefits of attending

For you

  • Reduce time spent drafting search strings, screening records and preparing research summaries
  • Identify fabricated citations, unsupported claims and source-traceability gaps before publication
  • Build a documented prompt library for recurring research tasks
  • Apply recognised appraisal and reporting methods when using AI outputs
  • Present AI-assisted research methods credibly to reviewers, managers and clients

For your organisation

  • Shorten research-cycle time without removing expert review and accountability
  • Improve consistency in literature searches, screening records and evidence extraction
  • Reduce exposure to fabricated references, unsupported findings and poor-quality synthesis
  • Protect confidential research material through clearer data-classification and tool-use rules
  • Create repeatable AI research procedures that teams can review and improve

Course modules

10 modules over 5 days

Participants can map research tasks to suitable AI tools, controls and verification points.

  • Research-task decomposition: discovery, synthesis, extraction, analysis, drafting and administration
  • Generative, retrieval, predictive and coding tools mapped to research stages
  • Prompt components: role, context, constraints, output schema and evaluation criteria
  • Model limitations: hallucination, staleness, bias, privacy and provenance gaps
  • Human-in-the-loop checkpoints for claims, calculations, citations and decisions
  • Tool selection matrix using task risk, data sensitivity and verification burden

Participants can create transparent AI-supported search strategies for defined research questions.

  • PICO, SPIDER and FINER structures for answerable research questions
  • Search strings built with Boolean operators, truncation, phrases and field tags
  • Database search translation between Scopus, Web of Science and PubMed
  • Elicit-assisted evidence discovery checked against source databases and publisher records
  • Snowballing through backward citations, forward citations and related-record networks
  • Search log fields supporting repeatable retrieval and transparent reporting
  • AI-generated query expansions screened for concept drift and false synonyms

Participants can document consistent study selection decisions with AI used only as a triage aid.

  • PRISMA 2020 flow diagram requirements for documented study selection
  • Inclusion and exclusion criteria operationalised before abstract screening begins
  • Deduplication rules for DOI, title, author and publication-year variants
  • Title-abstract screening procedures calibrated with borderline-record adjudication
  • AI relevance scores treated as triage signals, not inclusion decisions
  • Inter-rater agreement measured with Cohen's kappa during screening calibration

Participants can extract and verify research data using structured schemas and source-linked records.

  • Data-extraction schema defining variables, units, coding rules and source locations
  • Structured outputs constrained with JSON schemas and controlled vocabularies
  • PDF tables and figures checked against original page images
  • Claim-evidence matrices linking findings to quotations, tables and persistent identifiers
  • Double extraction for critical variables and disputed study characteristics
  • Prompted summaries compared with full-text methods, results and limitations
  • Missing-data codes distinguished from absent reporting and inapplicable fields

Participants can assess study quality and challenge AI-generated judgments using established appraisal criteria.

  • Critical appraisal matched to design: CASP, ROB 2 and ROBINS-I
  • Evidence hierarchies distinguished from certainty ratings and study applicability
  • Bias mechanisms identified in sampling, measurement, confounding and reporting
  • GRADE certainty judgments documented for bodies of intervention evidence
  • Publication bias signals assessed through protocols, registries and result patterns
  • AI-generated quality assessments challenged with primary-study evidence
  • Evidence tables separating findings, uncertainty, limitations and transfer conditions

Participants can use AI to support analysis while checking methods, calculations and interpretations.

  • Analysis plans specify estimands, populations, outcomes, covariates and decision rules
  • Descriptive statistics verified through code, calculations and source-data reconciliation
  • Thematic analysis uses codebooks, memoing, negative cases and audit trails
  • LLM-assisted qualitative coding checked against excerpts and analyst interpretations
  • Regression outputs interpreted with assumptions, uncertainty intervals and diagnostics
  • Prompted code reviewed for package versions, logic errors and reproducibility
  • Triangulation compares quantitative results, qualitative evidence and domain knowledge

Participants can produce AI-assisted research drafts that retain evidential traceability and author accountability.

  • IMRaD sections drafted from evidence tables rather than unsupported prose
  • Argument maps connect research questions, claims, evidence and limitations
  • AI-assisted outlines retain authorial judgment, disciplinary conventions and contribution
  • Citation verification checks author, year, title, DOI and supporting claim
  • Paraphrasing safeguards prevent patchwriting, fabricated quotations and citation laundering
  • Plain-language summaries separate evidence, interpretation, implications and recommendations
  • Journal and funder instructions converted into submission checklists

Participants can apply data protection, integrity and disclosure controls to AI-supported research.

  • Data classification determines permissible AI tools, retention and access controls
  • Personal, confidential and unpublished data removed from external prompts
  • GDPR principles applied to lawful basis, minimisation and data-subject rights
  • Research integrity policies address authorship, disclosure, plagiarism and accountability
  • Bias testing compares outputs across demographic groups, languages and contexts
  • Copyright and licence checks govern uploads, reuse and model-generated content
  • AI-use statements record tools, versions, prompts, dates and human review

Participants can document AI-supported research so that outputs can be reviewed, repeated and audited.

  • FAIR principles applied to metadata, identifiers, file formats and repositories
  • Research data management plans define storage, sharing, preservation and responsibilities
  • Version control records changes to prompts, datasets, code and manuscripts
  • Reproducibility packages include README files, dependencies, seeds and execution steps
  • Prompt libraries document purpose, inputs, expected outputs and validation steps
  • Provenance logs connect AI outputs to sources, transformations and approvals
  • Repository deposit checks licences, anonymisation, metadata and persistent identifiers

Participants can prepare a controlled plan for introducing AI tools into their research work.

  • Research workflow maps identify automation candidates, controls and handoffs
  • Use-case prioritisation scores value, feasibility, risk and data readiness
  • Standard operating procedures define prompts, review roles and escalation triggers
  • Quality-assurance sampling tests AI outputs against predefined acceptance criteria
  • Pilot metrics track time, error rates, coverage, reproducibility and user confidence
  • Personal application plan schedules tool trials, governance checks and peer review
  • Manager briefing frames resource needs, benefits, constraints and success measures

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. AI-assisted research workflow map
  2. Documented literature search protocol
  3. Evidence extraction and verification template
  4. AI output quality-assurance checklist
  5. Research prompt library template
  6. Responsible AI research implementation plan

Who should attend

  • Research Officers — need faster evidence gathering while retaining defensible methods
  • Policy Analysts — need traceable AI-supported evidence for recommendations and briefings
  • Monitoring and Evaluation Specialists — need consistent extraction and synthesis of programme evidence
  • Market Research Managers — need controls for AI-assisted desk research and reporting
  • University Researchers — need responsible methods for literature review, analysis and publication
  • Research Directors — need governance standards for teams adopting AI research tools

Skills and tools covered

AI research workflowsLiterature search designEvidence verificationData extractionResearch integrityReproducible reporting

Tools and standards

ChatGPT, Elicit, Scite, PRISMA 2020

How the course runs

Training methodology

The course uses short instructor briefings to establish methods, followed by tool demonstrations, research cases, group analysis and structured simulations. Participants examine search records, source documents, AI-generated summaries, extraction tables and draft research outputs to identify quality issues. Facilitated discussions compare acceptable and unacceptable uses of AI for research tasks. Application planning helps each participant select suitable use cases, define human review points and prepare a controlled introduction plan for their own research setting.

Requirements and prerequisites

Participants should have practical experience conducting, commissioning, reviewing or using research. They should understand basic research concepts such as research questions, sources, sampling, data collection and citations. Familiarity with academic databases, spreadsheets or reference managers is helpful but not essential. Participants should be comfortable reading research papers and assessing whether a source is relevant to a question. Programming, statistical modelling, machine learning development and prior use of generative AI are not required. The course focuses on research methods, tool use, verification and governance rather than building AI models.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should already understand basic research tasks such as defining questions, finding sources and using citations. You do not need programming, data science or previous generative AI experience.

Bring a laptop and, if permitted by your employer, an example research question or non-confidential research material. Do not bring confidential datasets, unpublished papers or personal data for use with public AI services.

It is designed for professionals who conduct, commission, assess or manage research. This includes policy, academic, market, technical, programme and corporate research roles.

This course concentrates on research methods rather than general productivity uses. It addresses literature searching, study selection, extraction, appraisal, evidence verification, reproducibility and publication-quality documentation.

You will identify suitable research tasks for AI support and define the checks required before results are used. The final application plan can be used to discuss approved tool trials, review roles and governance requirements with your manager.

The five days contain ten teaching modules covering the research workflow from question formulation through implementation planning. Each training day runs from 08:30 to 16:30 and includes approximately 6.5 contact hours, with time for instruction, cases, group work and reflection.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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