MazeMate: An LLM-Powered Chatbot to Support Computational Thinking in Gamified Programming Learning

TL;DR

MazeMate is an innovative LLM-powered chatbot integrated into a gamified programming environment, designed to enhance computational thinking skills through adaptive scaffolding during maze solving and design activities.

Contribution

This paper introduces MazeMate, the first LLM-based chatbot embedded in a 3D gamified programming environment to support computational thinking in classroom settings.

Findings

Students found MazeMate moderately helpful, especially for maze solving.

Thematic analysis confirmed support for CT processes like decomposition and abstraction.

Limitations include mismatched suggestions and fabricated solutions.

Abstract

Computational Thinking (CT) is a foundational problem-solving skill, and gamified programming environments are a widely adopted approach to cultivating it. While large language models (LLMs) provide on-demand programming support, current applications rarely foster CT development. We present MazeMate, an LLM-powered chatbot embedded in a 3D Maze programming game, designed to deliver adaptive, context-sensitive scaffolds aligned with CT processes in maze solving and maze design. We report on the first classroom implementation with 247 undergraduates. Students rated MazeMate as moderately helpful, with higher perceived usefulness for maze solving than for maze design. Thematic analysis confirmed support for CT processes such as decomposition, abstraction, and algorithmic thinking, while also revealing limitations in supporting maze design, including mismatched suggestions and fabricated algorithmic solutions. These findings demonstrate the potential of LLM-based scaffolding to support CT and underscore directions for design refinement to enhance MazeMate usability in authentic classrooms.