Case Study · Higher Education & Research

From ‘Generation Tool’ to Colleague

RMIT School of Psychology | AI Training | March 2026

At a Glance

Client	RMIT University — School of Psychology (31 academics)
Engagement	8 weeks — 5 February to 26 March 2026
Format	8 × 4-hour hands-on workshops (28 contact hours; ~180 person-hours delivered)
Investment	$12,800
Core adopters	3–5 researchers, 5+ sessions each
Legacy	200+ pages of reusable research briefs, handouts, decks & transcripts

“I used to use AI to just generate stuff or search for stuff. But now I see it as almost like another colleague where you can bounce ideas off and evaluate and error-check. That’s been the biggest enlightenment for me — I never thought before these workshops of using it that way.”

— Russell Conduit, Associate Dean (Psychology), attended 7 of 8 workshops

The Engagement

The Challenge

No domain-specific training — university AI sessions covered prompting basics, not psychology research methods
No verification culture — researchers either trusted AI blindly or avoided it entirely
No methodology integration — AI used as a search engine, not embedded in research workflows
31 academics with scattered, inconsistent AI relationships across the school

What We Built

8 workshops covering the full arc from mindset shift to reproducible pipelines
11 research briefs synthesising 50,000+ words of peer-reviewed evidence
8 slide decks, 8 exercise handouts, 7 clean transcripts and prompt template packs
A unified ResearchOps Loop: Ingest → Structure → Transform → Verify → Ship

The Impact

8 months to 20 minutes — data-extraction code that took 8 months now runs in 20 min
500-paper screen with zero errors across 200 spot-checked papers
Teaching multiplier — core adopters now training their own students
Conservative Year 1 ROI of 195%+ against the RMAI fee

195%+

Conservative Year 1 Recovery

Against the RMAI engagement fee

200+

Pages of Reusable Materials

Research briefs, handouts, transcripts

28

Hours of Hands-On Training

~180 total person-hours delivered

What Made This Different

Domain-specific, not generic

Every exercise used real psychology research tasks — real transcripts, real ARC criteria, real PICO frameworks. Researchers learned to integrate AI into methodologies they had already mastered.

Methodology-first, not tool-first

We started with “here’s what your research needs,” not “here’s what the tool can do.” Epistemological limits came first; practical application followed.

Honest about failures

We demonstrated 20% citation error rates live. We showed emotion sanitisation in qualitative data. We triggered automation bias on purpose — building trust through transparency.

Materials as institutional legacy

200+ pages of briefs, handouts, decks and transcripts available to all 31 academics — including those who didn’t attend. The long tail extends years beyond the program.

The Bigger Picture

Most AI training teaches people which buttons to press. The shift that matters — the one Russell named — is moving from treating AI as a generation tool to treating it as a colleague you can reason with, challenge, and error-check.

That shift doesn’t come from a tool demo. It comes from working through your own real tasks, with someone who knows both the technology and the limits of trusting it. For a research school, the payoff compounds: the people we trained are now training their students, and the materials outlast the engagement.

Taking new clients

Training a research team or faculty?

If your people are stuck using AI as a search engine — or avoiding it because they don't trust it — we run domain-specific, methodology-first training that changes how they work. One conversation is all it takes to scope it.

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Case study published with client approval. Full documentation and evidence available on request.