Date
Publisher
arXiv
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.
Study at a glance
- Question: How can a controlled language mapping interface support instructors in co-creating educational games with an AI assistant?
- Setting and data: Not stated in the paper
- Method: System development and prototype design
Why it matters
Instructors often struggle to design educational games that align with learning outcomes, while opaque AI tools risk reducing human control and causing pedagogical misalignment.
Key findings
- A four-part controlled sentence template maps instructional pedagogical elements (adverbs, verbs, nouns, and adjectives) directly to corresponding gameplay mechanics, rules, content, and contextual aesthetics.
- A web tool operationalizes this framework through a three-phase workflow consisting of requirement extraction, language translation, and language development.
- The system generates inspectable, component-wise rationales and pseudocode rather than opaque game code, preserving instructor agency and traceability across design decisions.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
