# Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study

arXiv: https://arxiv.org/abs/2005.07478 · HTML: https://mlsanity.com/paper/2005.07478/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 4 of 4)

- [Procedural Generation of 3D Maps with Snappable Meshes](https://mlsanity.com/paper/2108.00056/) (2021)
- [The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games](https://mlsanity.com/paper/2311.04710/) (2023)
- [From Metrics to Meaning: Time to Rethink Evaluation in Human-AI Collaborative Design](https://mlsanity.com/paper/2402.07911/) (2024)
- [Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach](https://mlsanity.com/paper/2503.02703/) (2025)

## Abstract (arXiv)

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimisation was designed for this study which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process.