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

• URL: http://arxiv.org/abs/2603.03644v1
• Date: Wed, 04 Mar 2026 02:08:32 GMT
• Title: Bridging Pedagogy and Play: Introducing a Language Mapping Interface for Human-AI Co-Creation in Educational Game Design
• Authors: Daijin Yang, Erica Kleinman, Casper Harteveld,
• Abstract summary: Existing authoring environments do not eliminate the underlying challenges of educational game design.<n>We designed a controlled natural language framework-based web tool that positions language as the primary interface for LLM-assisted educational game design.<n>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.
• Score: 15.064660952504129
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Playsemble: Learning Low-Level Programming Through Interactive Games [6.174988590679205]
  Playsemble is a gamified learning system that transforms assembly instructions into interactive, game-like tasks.<n>Our findings suggest that Playsemble promotes active experimentation, sustained engagement, and deeper conceptual understanding.
  arXiv  Detail & Related papers  (2026-02-09T06:31:44Z)
• Open TutorAI: An Open-source Platform for Personalized and Immersive Learning with Generative AI [1.440818306216858]
  This paper presents Open TutorAI, an open-source educational platform based on LLMs and generative technologies.<n>The system integrates natural language processing with customizable 3D avatars to enable multimodal learner interaction.<n>It includes tools for organizing content, providing embedded feedback, and offering dedicated interfaces for learners, educators, and parents.
  arXiv  Detail & Related papers  (2026-02-06T20:24:33Z)
• Multi-Actor Generative Artificial Intelligence as a Game Engine [49.360775442760314]
  Generative AI can be used in multi-actor environments with purposes ranging from social science modeling to interactive narrative and AI evaluation.<n>We argue here that a good approach is to take inspiration from tabletop role-playing games (TTRPGs), where a Game Master (GM) is responsible for the environment and generates all parts of the story not directly determined by the voluntary actions of player characters.
  arXiv  Detail & Related papers  (2025-07-10T22:31:09Z)
• From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning [82.50157695987558]
  Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy.<n>We propose an online reinforcement learning (RL)-based alignment framework that can quickly adapt LLMs into effective tutors.
  arXiv  Detail & Related papers  (2025-05-21T15:00:07Z)
• A Problem-Based Learning Approach to Teaching Design in CS1 [0.9786690381850356]
  Design skills are increasingly recognized as a core competency for software professionals. New designers require a structured process to keep them from being overwhelmed by possibilities. We present our experience teaching a team design project course to 200 first-year-university students.
  arXiv  Detail & Related papers  (2024-10-15T23:36:08Z)
• Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces [0.31873871499564926]
  We introduce generative AI capabilities to assist educators in creating tutor interfaces. Our approach leverages Large Language Models (LLMs) and prompt engineering to generate tutor layout and contents. A small-scale comparison shows the potential of our approach to enhance the efficiency of tutor interface design.
  arXiv  Detail & Related papers  (2024-05-23T15:46:10Z)
• Instruction-Driven Game Engines on Large Language Models [59.280666591243154]
  The IDGE project aims to democratize game development by enabling a large language model to follow free-form game rules. We train the IDGE in a curriculum manner that progressively increases the model's exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, a universally cherished card game.
  arXiv  Detail & Related papers  (2024-03-30T08:02:16Z)
• Thinking beyond chatbots' threat to education: Visualizations to elucidate the writing and coding process [0.0]
  The landscape of educational practices for teaching and learning languages has been predominantly centered around outcome-driven approaches. The recent accessibility of large language models has thoroughly disrupted these approaches. This work presents a new set of visualization tools to summarize the inherent and taught capabilities of a learner's writing or programming process.
  arXiv  Detail & Related papers  (2023-04-25T22:11:29Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• Exploration Based Language Learning for Text-Based Games [72.30525050367216]
  This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents.
  arXiv  Detail & Related papers  (2020-01-24T03:03:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.