ReVISit Games Suite

Years Active: 2025-current

In just three weeks during 2025, I developed the ReVISit game suite: three fully-functional research games designed to replicate and extend player experience (PX) studies online. The goal is to test whether complex PX studies could be effectively conducted on the web with reVISit.

• Role: Lead Developer
• Duration: 3 weeks (2025)
• Tech Stack: Unity (WebGL), JavaScript, Web Integration

ReVISit-Games

reVISit-Games is a technical and methodological framework that makes it easier to conduct scalable, asynchronous, and crowdsourced player experience (PX) studies. It builds on the open-source ReVISit research study framework, but adds:

• Embedded WebGL game content (so people play real games in their browser)
• Event logging (tracking what players do in detail)
• Reusable questionnaire templates (for surveys)
• Flexible study sequencing (combining surveys, games, attention checks, etc.)
• Dynamic runtime parameterization (games can change based on URL, no extra coding needed)

Try the study platform with games used for case studies: ReVISit-Games.

Dungeon Digger Web: Mixed-Initiative Procedural Content Generation (PCG) Game

This game explores how players interact with a level design tool powered by mixed-initiative procedural content generation. Players take turns with an AI assistant to co-create game levels, with the system logging both human and AI contributions.

The research goal is to assess the differences in engagement, enjoyment, and other aspects of player experience (PX) between AI-generated and human-authored game content.

This game replication is built based on: Evan C. Sheffield and Michael D. Shah. 2018. Dungeon Digger: Apprenticeship Learning for Procedural Dungeon Building Agents. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts (CHI PLAY ‘18 Extended Abstracts). Association for Computing Machinery, New York, NY, USA, 603–610. https://doi.org/10.1145/3270316.3271539

Since the original authors made their scripts available as an open-source repository on GitHub (DungeonDigger/tile-level-generator), our replication focused primarily on adapting the study for the web. Specifically, we modified the implementation to support Unity WebGL builds and added the ability to configure which level to show using URL parameters to allow a single build to display different levels without requiring extra input from users.

In the original in-lab study, a survey was manually administered after each level, and participants could not continue until the survey was completed. Replicating this process online presented challenges, so we improved the workflow: the game now sends a signal to the parent web app upon successful level completion. This triggers the end-of-level survey, and only after the survey is completed can participants proceed to the next randomly selected level. The order of levels is randomized and managed by the reVISit.

City Wanderer: 3D Navigation with Procedurally Generated City Map

This game explores how players navigate a procedurally generated 3D city, using different types of navigation aids such as mini-maps, directional markers, and trails. The system tracks player movements and decision-making as they attempt to reach specific goals within the city.

The research goal is to examine how these navigation systems impact players’ ability to form a mental map of the environment, support spatial understanding, and influence overall player experience (PX).

This game replication is built based on: Colby Johanson, Carl Gutwin, and Regan L. Mandryk. 2017. The Effects of Navigation Assistance on Spatial Learning and Performance in a 3D Game. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY ‘17). Association for Computing Machinery, New York, NY, USA, 341–353. https://doi.org/10.1145/3116595.3116602

Since the original game files were unavailable, we rebuilt the experiment in Unity using the Fantastic City Generator and deployed it via WebGL. Using procedural cities not only makes navigation more realistic, but also ensures each player encounters a unique, unfamiliar map. This eliminates the familiarity bias present in the original study when using maps from existing games.

Juicy Frogger: The Impact of Juicy Feedback on Player Engagement

This game explores how adding “juicy” feedback impacts player engagement and enjoyment in a classic Frogger clone game. “Juicy” feedback is the playful, exaggerated, and satisfying responses to player actions. Players guide a character across a series of obstacles, with and without “juice” through visual effects, screen shakes, and rewarding animations as the player moves, jumps, and makes progress.

The research goal is to investigate how juicy feedback influence player engagement, motivation, and overall player experience.

This game replication is built based on: Kieran Hicks, Kathrin Gerling, Patrick Dickinson, and Vero Vanden Abeele. 2019. Juicy Game Design: Understanding the Impact of Visual Embellishments on Player Experience. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY ‘19). Association for Computing Machinery, New York, NY, USA, 185–197. https://doi.org/10.1145/3311350.3347171

To support player agency and autonomy, Juicy Frogger replication includes a difficulty selection, letting players tailor the challenge to their skill level. This choice is designed to boost perceived competence by ensuring players feel capable and successful. Adding this option also allows us to investigate how player-chosen difficulty interacts with juicy feedback to influence intrinsic motivation and overall player experience.