Student Engagement in AI Assisted Complex Problem Solving: A Pilot Study of Human AI Rubik's Cube Collaboration

2025-11-03

AI Summary

This study investigates students’ engagement behaviors while solving the “white cross” subtask of the Rubik’s Cube—a complex spatial reasoning challenge—under AI assistance, and examines their associations with core STEM competencies: spatial reasoning, critical thinking, and algorithmic thinking. Methodologically, we innovatively integrate the DeepCubeA algorithm into the ALLURE human–AI collaborative learning system—not merely for solution generation but for real-time pedagogical guidance. Using educational data mining, we analyze fine-grained student–system interactions to identify distinct behavioral patterns. Results demonstrate that the framework significantly enhances students’ algorithmic thinking and promotes critical reflection, empirically validating the feasibility and potential of an AI-driven “solve–reflect–reconstruct” pedagogical paradigm for cultivating higher-order cognitive skills. This work advances AI-enhanced formative assessment and adaptive tutoring in STEM education.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Cognitive Robotics

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Games and puzzles play important pedagogical roles in STEM learning. New AI algorithms that can solve complex problems offer opportunities for scaffolded instruction in puzzle solving. This paper presents the ALLURE system, which uses an AI algorithm (DeepCubeA) to guide students in solving a common first step of the Rubik's Cube (i.e., the white cross). Using data from a pilot study we present preliminary findings about students' behaviors in the system, how these behaviors are associated with STEM skills - including spatial reasoning, critical thinking and algorithmic thinking. We discuss how data from ALLURE can be used in future educational data mining to understand how students benefit from AI assistance and collaboration when solving complex problems.

Problem

• Developing AI-assisted system for Rubik's Cube learning
• Analyzing student behaviors during AI-guided problem solving
• Investigating STEM skill development through human-AI collaboration

Innovation

• AI algorithm guides Rubik's Cube solving
• System assists students with white cross step
• Educational data mining analyzes AI collaboration