Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

• URL: http://arxiv.org/abs/2601.22199v2
• Date: Wed, 04 Feb 2026 03:41:26 GMT
• Title: Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines
• Abstract summary: We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education.<n>We analyzed 95 studies from 12,485 records across four databases (2014-2025)<n>Three patterns emerged: 1) approach-context-pedagogy coupling; 2) emphasis on introductory programming and modular kits; and 3) short study horizons.
• Score: 44.4001664732566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

Related papers

• Beyond Accuracy: Towards a Robust Evaluation Methodology for AI Systems for Language Education [0.0]
  The rapid adoption of large language models in AI-powered language education has created an urgent need for evaluations that assess pedagogical effectiveness.<n>We introduce L2-Bench, a novel evaluation benchmark grounded in a validated "language learning experience designer" construct.<n>Our methodology integrates pedagogical theory, sociotechnical AI evaluation methods, and operationalizes a hierarchical taxonomy to structure an expert-curated dataset.
  arXiv  Detail & Related papers  (2026-03-20T16:13:03Z)
• What Matters for Simulation to Online Reinforcement Learning on Real Robots [51.77095085120584]
  We investigate what specific design choices enable successful online reinforcement learning on physical robots.<n>We systematically ablate algorithmic, systems, and experimental decisions that are typically left implicit in prior work.<n>We find that some widely used defaults can be harmful, while a set of robust, readily adopted design choices within standard RL practice yield stable learning across tasks and hardware.
  arXiv  Detail & Related papers  (2026-02-23T10:34:15Z)
• Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation [63.302074484672424]
  We propose a pedagogically-inspired framework for knowledge distillation.<n>Our approach identifies knowledge deficiencies in student models, organizes knowledge delivery through progressive curricula, and adapts representations to match cognitive capacity of student models.<n>Our framework particularly excels in complex reasoning tasks, showing 19.2% improvement on MATH and 22.3% on HumanEval compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-12T17:00:36Z)
• From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education [0.6372261626436676]
  Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning.<n>This scoping review synthesizes 32 studies purposively sampled from 259 records to map personalization mechanisms and effectiveness signals.<n>We identify five application domains: intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review, and analyze how design choices shape learning outcomes.
  arXiv  Detail & Related papers  (2025-12-23T19:20:34Z)
• Integrating Large Language Models in Software Engineering Education: A Pilot Study through GitHub Repositories Mining [1.043230260556633]
  Large Language Models (LLMs) are increasingly adopted in software engineering (SE) education.<n>This research aims to develop a validated framework for integrating LLMs into SE education.
  arXiv  Detail & Related papers  (2025-09-05T07:46:27Z)
• Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code [76.80306464249217]
  We propose TeaR, which aims at teaching LLMs to reason better.<n>TeaR leverages careful data curation and reinforcement learning to guide models in discovering optimal reasoning paths through code-related tasks.<n>We conduct extensive experiments using two base models and three long-CoT distillation models, with model sizes ranging from 1.5 billion to 32 billion parameters, and across 17 benchmarks spanning Math, Knowledge, Code, and Logical Reasoning.
  arXiv  Detail & Related papers  (2025-07-10T07:34:05Z)
• Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop [120.2806035123366]
  RoboTwin Dual-Arm Collaboration Challenge was held at the 2nd MEIS Workshop, CVPR 2025.<n>Competitors totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios.<n>Report outlines the competition setup, task design, evaluation methodology, key findings and future direction.
  arXiv  Detail & Related papers  (2025-06-29T17:56:41Z)
• A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
  We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
  arXiv  Detail & Related papers  (2024-11-08T17:20:47Z)
• Instruction Tuning with Human Curriculum [15.025867460765559]
  We introduce Curriculum Instruction Tuning, (2) explore the potential advantages of employing diverse curriculum strategies, and (3) delineate a synthetic instruction-response generation framework. Our generation pipeline is systematically structured to emulate the sequential and orderly characteristic of human learning. We describe a methodology for generating instruction-response datasets that extensively span the various stages of human education.
  arXiv  Detail & Related papers  (2023-10-14T07:16:08Z)
• Understanding the role of single-board computers in engineering and computer science education: A systematic literature review [0.0]
  Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels. This systematic literature review explores how the SBCs are employed in engineering and computer science.
  arXiv  Detail & Related papers  (2022-03-30T18:34:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.