Session 2: Building the Human-AI Research Pipeline

Building the pipeline once teaches more than reading about it ten times.

This guide offers a hands-on workshop for designing and executing a systematic review with AI tools. The approach is cognitive-first: instead of just learning to operate tools, we learn to think with them. We engage with AI as a mirror that makes our own research processes visible, helping us deconstruct complex tasks like "finding a gap" or "building a theory" into explicit, repeatable steps.

By the end of this session, you'll move beyond generic AI interaction to purposeful orchestration.

Learning outcomes

By the end of this session, you'll be able to:

• Construct a high-quality, curated literature set using discovery tools (Research Rabbit for the human eye, OpenAlex for the agent) and a management tool (Zotero)
• Deconstruct complex research tasks (e.g., "synthesizing a framework") into explicit, step-by-step cognitive operations
• Design and implement structured, multi-step prompts ("cognitive blueprints") that guide AI through sophisticated analytical work
• Critically evaluate AI-generated outputs, identifying common failure modes (like "botshit" and paradigm blindness)
• Architect a research workflow that strategically combines your domain expertise with AI capabilities

The complete research pipeline

     seed papers
          |
          v
  citation discovery
          |
          v
    expanded set
          |
          v
 +--------+--------+
 | Human curation  |
 +--------+--------+
          |
          v
  reference library
          |
          v
AI-assisted analysis
          |
 +--------+--------+
 | quality control |
 +--------+--------+
          |
  issues? +----> revise method
          |            |
          +<-----------+
          |
          v
 research synthesis

Human judgment provides quality gates throughout this pipeline: AI handles scale and pattern detection, while researchers provide curation and evaluation.

Core readings: the AI toolkit

Mindset & mental models

Techniques & best practices

Risks & responsibility

Supplementary readings

Quick reference guides

Foundational papers

Advanced context

Session structure

Our hands-on session will follow this structure:

1. Pipeline Overview - Understanding the complete workflow
2. Discovery & Curation - Research Rabbit, OpenAlex, and Zotero
3. AI Integration - Setting up Cherry Studio and MCP servers
4. Pipeline Practice - Working with a sample literature set

We use the scaling and scalability literature as a shared case study throughout these guides: research on how organizations grow, and on whether their structures, processes, business models, and capabilities can handle that growth. In this course shorthand:

Scaling: growth in size or scope.

Scalability: the capacity to grow without the organization breaking or losing coherence.

A seed library of 10 foundational papers is provided, which you can expand using the discovery methods we learn today.

MCP server setup (Live demo)

During the AI Integration phase, we'll add the first MCP servers:

MCP has had less attention lately than skills and plain command-line tools, and it is still worth the hour. It is the standard interface through which plugins in Claude Code, Codex, and Cherry Studio reach anything outside the machine, and it gives an organization a seam to put controls on: which servers an agent may reach, what each server is allowed to do on its behalf, and where that is logged. The protocol enforces none of that itself; the host's approvals, the server's own authorization, and the operating system's permissions do. An agent that does everything through a shell has no such seam.

For more MCP exploration, see the MCP Explorer Guide.

By the end, you'll have a complete, tested workflow for systematic reviews conducted through human-AI collaboration.

Pre-class setup for Session 2

Before our hands-on session, please complete the following setup to ensure you're ready to dive in. Arriving with the Student Vault worksheets and no AI tooling configured is a valid pre-class state: the vault's individual reading only mode stands on its own, and the tool steps below can follow. Bring the notes/benchmark/ files from Session 1 as well: your hand-made question, protocol and search log are what this session's pipeline gets checked against.

During class, we will:

• Set up knowledge bases together
• Practice Zotero MCP searches
• Create a first agent in Cherry Studio's Agent Workspace and compare its output with the chat view
• Export conversations to Obsidian
• Create literature note templates
• Practice conversation forking for different analyses

How Do You Know It's Working?

Related Resources

Navigation: Previous: Session 1 • Return to Case Study Overview • Next: Session 3