MIT Educational Asset Pipeline

Problem

An MIT educational game and book project required a high volume of consistent visual assets. Traditional production pipelines could not deliver quality at the required pace and budget.

Context

Built at KAIA for MIT educational content. Assets needed to match a cohesive visual language across game and book formats while leveraging AI generation tools responsibly.

Constraints

• Consistent style across hundreds of assets
• Multiple output formats (game sprites, book illustrations)
• Quality bar set by MIT brand expectations
• Hybrid AI + manual refinement workflow

Goals

• Build a repeatable AI-assisted asset production pipeline
• Maintain visual consistency across asset types
• Enable rapid iteration on creative direction
• Document the pipeline for team handoff

Approach

Designed an AI Content Systems workflow: define style guide and asset taxonomy first, then build generation + refinement + QA pipeline.

Architecture

• ComfyUI workflows for SDXL and Flux generation
• Blender for 3D asset preparation and rendering
• Krita for manual refinement and touch-ups
• Style guide and prompt library as shared knowledge base
• QA checkpoints before assets enter production

Workflow

Style definition → prompt engineering → batch generation → manual refinement → QA review → asset delivery → iteration loop

Tools

SDXL, Flux, ComfyUI, Blender, Krita

Key decisions

• AI generates drafts; humans refine for quality and consistency
• Style guide as single source of truth, not ad-hoc prompts
• Pipeline documented so team can operate without single-person dependency

Results

• [TBD] Asset production time reduction
• [TBD] Assets delivered for game and book
• [TBD] Style consistency score (internal QA)
• Repeatable pipeline documented and operational

Lessons

AI content production is a systems problem, not a prompting problem. Style guides, QA gates, and hybrid human-AI workflows determine quality.

Framework

AI Content Systems — architect the full pipeline (style, generation, refinement, QA) before scaling output.

Next steps

Education and EdTech industry pages will reference this case study for AI-assisted content production engagements.