Related papers: SpellForger: Prompting Custom Spell Properties In-Game using BERT supervised-trained model

SpellForger: Prompting Custom Spell Properties In-Game using BERT supervised-trained model

URL: http://arxiv.org/abs/2511.16018v1
Date: Thu, 20 Nov 2025 03:37:16 GMT
Title: SpellForger: Prompting Custom Spell Properties In-Game using BERT supervised-trained model
Authors: Emanuel C. Silva, Emily S. M. Salum, Gabriel M. Arantes, Matheus P. Pereira, Vinicius F. Oliveira, Alessandro L. Bicho,
Abstract summary: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts.<n>The game is developed in the Unity Game Engine, and the AI backend is in Python.
Score: 35.18016233072556
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Introduction: The application of Artificial Intelligence in games has evolved significantly, allowing for dynamic content generation. However, its use as a core gameplay co-creation tool remains underexplored. Objective: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts, aiming to provide a unique experience of personalization and creativity. Methodology: The system uses a supervisedtrained BERT model to interpret player prompts. This model maps textual descriptions to one of many spell prefabs and balances their parameters (damage, cost, effects) to ensure competitive integrity. The game is developed in the Unity Game Engine, and the AI backend is in Python. Expected Results: We expect to deliver a functional prototype that demonstrates the generation of spells in real time, applied to an engaging gameplay loop, where player creativity is central to the experience, validating the use of AI as a direct gameplay mechanic.

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