Liverpool John Moores University · 2026

Helping students build AI confidence through real-world challenges.

GoodShip* designed and delivered AI Activator with Liverpool John Moores University in Liverpool. Students worked on real challenges set by real organisations, using AI as a practical tool rather than a subject to be studied. 87% reported increased confidence and 80% said the programme changed how they thought about their career.

OngoingConsultancyDigital & TechCommunityEmpowermentAI Activator

At a glance

Client: Liverpool John Moores University
Challenge: Students needed practical AI and employability experience, not more tool demonstrations.
GoodShip* role: Programme design and delivery
GoodShip* services: Consultancy · Digital & Tech · Community · Empowerment
Capabilities: AI · Training · Events · Prototyping
Activation: AI Activator
Collaboration: Liverpool John Moores University
Sector: Education & Skills · Technology & Innovation
Location: Liverpool
Project period: 2026
Status: Ongoing
Relationship: University partner
Impact themes: Skills · Confidence · Employability

The challenge

Liverpool John Moores University wanted students to gain practical experience with AI while developing employability and confidence.

The audience was students across a range of disciplines, most of whom had used AI tools informally but had never applied them to a real problem with a real client on the other side of it. Conventional training was not enough. Tool walkthroughs teach features; they do not build judgement, and they do not give a student anything credible to talk about in an interview.

At the same time, employers across the city region were unsure how to talk about AI capability in early-career roles. The gap was not knowledge of the tools. It was confidence, critical thinking and evidence of applied experience.

The question we were trying to answer

How could students gain meaningful AI experience without needing to become AI specialists?

The approach

What we did

Discover. We spent time with university staff and students to understand what students already did with AI, where they lost confidence, and what employers actually wanted to see.

Question. We challenged the assumption that more instruction was the answer. The pilot deliberately reduced teaching time and increased experimentation time.

Make. We designed AI Activator as a challenge-led programme: real briefs from real organisations, cross-discipline teams, mentoring from practitioners, and structured reflection built into every stage.

Activate. The programme ran with a live cohort in Liverpool, with challenge partners engaging directly with students rather than sitting behind a brief document.

Measure. Every participant completed a structured end-of-programme evaluation covering confidence, career perspective, and experience of the programme itself.

Who was involved

Built together

Liverpool John Moores University
Client and programme partner
Challenge partners
Set the real-world briefs students worked on
GoodShip*
Programme design, facilitation and evaluation

What we made

What was created

A repeatable programme model: challenge briefs, team structures, mentoring pattern, facilitation materials, reflection framework and evaluation instrument — plus the delivery itself.

The impact

What changed

Students left with applied experience of using AI on a real problem, and with the language to describe it. The clearest change was confidence: not confidence in a particular tool, but confidence in their own judgement about where AI helps and where it does not.

Several participants reported changing how they thought about their career direction. Challenge partners gained fresh perspectives on questions they were already sitting with, and the university gained a delivery model it could repeat.

Impact metrics

87%

reported increased confidence

AI Activator pilot cohort. Self-reported, post-programme survey.

80%

changed career perspective

AI Activator pilot cohort. Self-reported, post-programme survey.

4.93 / 5

participant rating

AI Activator pilot cohort participant feedback.

Evidence

How we know

Evidence source: end-of-programme participant evaluation Cohort: AI Activator pilot Method: self-reported, post-programme survey Date: pilot cohort

Confidence, career perspective and programme rating figures come from that evaluation. Employer-side observations are qualitative, drawn from challenge partner feedback.

Evidence source
End-of-programme participant evaluation
AI Activator pilot cohort · self-reported, post-programme
Evidence source
Challenge partner feedback
Qualitative feedback from participating organisations

The human bit

One participant arrived describing themselves as "not a tech person" and left presenting their team's response to a live organisational challenge. The change was not technical fluency. It was the willingness to have an opinion about it.

Why this mattered

The project matters because AI education is often focused on tools rather than confidence, critical thinking and experience.

Tools change every few months. The ability to ask a good question, judge an output and know when not to use AI lasts longer. If AI capability becomes a condition of entry to good work, then who gets confident with it — and who does not — becomes a question about access and opportunity, not just skills.

Next chapter

What happened next

AI Activator became an ongoing GoodShip* Activation, delivered with further cohorts and challenge partners, and is now commissioned by universities, employers and programme partners.

Learning

What we learned

Participants did not need more tool demonstrations. They needed more time experimenting, and more permission to get it wrong in front of each other.

We also learned that the challenge partner relationship carried more weight than we expected. Students behaved differently when the person who set the brief was in the room.

Services, capabilities and impact