Bridging Pedagogy and Play: Introducing a Language Mapping Interface for Human-AI Co-Creation in Educational Game Design

2026-03-03

AI Summary

This work addresses the challenge educators face in effectively mapping instructional objectives to game mechanics, a process often hindered by high design barriers and opaque AI recommendations in existing tools. To overcome these limitations, the authors propose a web-based co-design tool grounded in controlled natural language, which enables teachers to collaboratively construct structured mappings between pedagogical intent and gameplay mechanics through an explicit, editable linguistic interface. The system employs a four-component interactive architecture that preserves human agency via a human-in-the-loop design while enhancing alignment between educational goals and game dynamics. Empirical evaluation demonstrates that the tool significantly lowers the entry barrier for non-expert developers, improves their comprehension of and control over AI-assisted design processes, and effectively facilitates the coherent integration of learning outcomes with game mechanics.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Educational games can foster critical thinking, problem-solving, and motivation, yet instructors often find it difficult to design games that reliably achieve specific learning outcomes. Existing authoring environments reduce the need for programming expertise, but they do not eliminate the underlying challenges of educational game design, and they can leave non-expert designers reliant on opaque suggestions from AI systems. We designed a controlled natural language framework-based web tool that positions language as the primary interface for LLM-assisted educational game design. In the tool, users and an LLM assistant collaboratively develop a structured language that maps pedagogy to gameplay through four linked components. We argue that, by making pedagogical intent explicit and editable in the interface, the tool has the potential to lower design barriers for non-expert designers, preserves human agency in critical decisions, and enables alignment and reflections between pedagogy and gameplay during and after co-creation.

Problem

• educational game design
• learning outcomes
• non-expert designers
• AI co-creation
• pedagogy

Innovation

• controlled natural language
• human-AI co-creation
• pedagogy-to-gameplay mapping
• educational game design
• LLM-assisted design