LIDA LEADING / Innovation Lab

A place for useful “what ifs”.

Prototype early. Ask better questions. Keep the things that help people.

Curiosity, with a method.

AI, immersive learning and playful interaction are starting points. Each experiment needs a problem, a testable idea and a reason to continue.

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LEARN
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INNOVATE

01 / CURIOSITY02 / CONNECTION03 / CHANGE

THE LEARNING KNOT

AI / Functioning prototype

PROBLEM: An Assistant Principal asked for a more consistent approach to curriculum planning, without losing sight of individual learner needs.
EXPERIMENT & TECHNOLOGY: An AI-supported Scheme of Learning connecting course information, planning and adaptive support.
OUTCOME: A functioning prototype emerged after approximately a week of experimentation, debugging and iteration. This is prototype evidence; an organisation-wide rollout has not been confirmed.
WHAT I LEARNED / DESIGN RATIONALE: Grounded sources and clear output structures matter more than a clever prompt alone. Adaptive suggestions still need a knowledgeable person to check relevance and accuracy.
WHAT I’D TEST NEXT: Compare generated plans against agreed quality criteria.

VR / Pilot & ongoing exploration

PROBLEM: Determine when immersion offers something useful beyond a conventional learning activity.
EXPERIMENT & TECHNOLOGY: Evaluating VR for genuine learning value through immersive learning pilots.
OUTCOME: The pilot was described as successful in my professional account. No numerical improvement in empathy, retention or performance is claimed.
WHAT I LEARNED / DESIGN RATIONALE: Accessibility, facilitation and debriefing are as important as the immersive scenario.
WHAT I’D TEST NEXT: Evaluate an immersive task alongside a non-VR alternative.

Gamification / Playable portfolio experiment

PROBLEM: Demonstrate learning design through an experience visitors can actually play.
EXPERIMENT & TECHNOLOGY: A browser game connecting brand recognition with resource decisions, feedback and progression.
OUTCOME: A playable single-player prototype with timed and untimed modes, an 80-question bank and a final vault encounter.
WHAT I LEARNED / DESIGN RATIONALE: Choices need to matter to the game. Knowledge retrieval earns the resources that make those choices possible.
WHAT I’D TEST NEXT: Observe how players balance learning, time pressure and resource choices.

Automation / Exploration

PROBLEM: Explore whether AI can support assessment tasks while retaining quality and accountability.
EXPERIMENT & TECHNOLOGY: Investigating AI-assisted assessment and marking workflows with professional oversight.
OUTCOME: Exploration informed thinking about possible workflows. No automated grading deployment, accuracy score or institution-wide impact is claimed.
WHAT I LEARNED / DESIGN RATIONALE: Efficiency should never come at the expense of fairness, explainability or learner trust.
WHAT I’D TEST NEXT: Evaluate approved anonymised samples before considering any pilot.

INSIDE A PROTOTYPE

CONCEPTUAL WORKFLOW · SELECT A STAGE

Course information

Curriculum specifications establish the source material.

Process reconstruction, not a screenshot of the prototype.