Case Study: Systematic Reviews with AI

Systematic reviews teach orchestration because every step is inspectable.

Case study overview

This page outlines a course that teaches the Research Memex approach through a common academic task: the systematic literature review. This case study demonstrates one way to integrate AI into the research workflow, from literature discovery through to final synthesis, while keeping every step inspectable.

We're sharing this approach to show how researchers can develop the complete pipeline, learning when and how to work with AI as a cognitive partner. Your implementation might look different based on your field and research questions.

Learning schedule & key topics

This case study is structured around four sessions, each building on the last:

Getting started with the case study

To get the most out of this case study, we suggest following the Quick Start Checklist first. Each session also has its own detailed guide with associated readings and exercises. Adapt the pace and focus to match your own learning goals.

Learning assessment

The learning process is assessed through two main components:

Learning through practice (50%)

• Session 2 Exercise: Master prompt development and cognitive scaffolding using a sample literature set.
• Session 3 Exercise: Develop critical evaluation skills by documenting AI failure modes and the limitations of automated synthesis.
• In-class work: Build presentation and peer feedback abilities.

Capstone learning project (50%)

Participants choose a final project that best serves their research goals:

• Option A: Validation skills - critically compare AI vs. human synthesis approaches
• Option B: Workflow design - develop reproducible, AI-enhanced research pipelines
• Option C: Quality control - build expertise in identifying and preventing common AI failure modes

Each option develops different competencies for AI-enhanced research. Choose what matters most for your work.

Tools & budget

• Essential tools: The workflows in this case study use Research Rabbit (free), Zotero (free), and Cherry Studio (an open-source tool for multi-model AI interaction). See the API Keys Setup Guide for more.
• API budget: For course participants, a budget is typically provided for API access. Independent learners can use free tiers from providers like Google AI Studio.

Support

• Technical: See the setup guides for each tool.
• Content: See the PRISMA 2020 Guidelines and the Cochrane Handbook for systematic-review methodology.

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Navigation

Next steps:

• Session 2: AI-Powered Practice
• Session 3: Human vs AI Synthesis
• Session 4: Agentic Workflows

Resources:

• Quick Start Checklist

@misc{docs-case-studies-systematic-reviews-systematic-review-syllabus-2026,
  author = {Xule Lin},
  title = {Case Study: Systematic Reviews with AI},
  year = {2026},
  howpublished = {\url{https://research-memex.org/docs/case-studies/systematic-reviews/systematic-review-syllabus}},
  note = {ORCID: 0000-0001-7885-4194}
}