EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners

Synthesis: Lu and Zhang (2026) present EduSim-LLM, an educational platform that integrates large language models with robot Simulation to make robotic control accessible to beginners. Recognizing that the integration of natural language understanding into robotic control is a key challenge in human-robot interaction, the platform constructs a language-driven control model that translates natural-language instructions into executable robot behavior sequences in CoppeliaSim. The authors design two human-robot interaction models — direct control and autonomous control — and conduct systematic evaluations of their educational and practical Accessibility.

Key Findings

• The rapid development of LLMs has enhanced natural language understanding and human-computer interaction, creating new opportunities in robotics, but integrating natural language understanding into robotic control remains a challenge for intuitive, accessible robot control.
• EduSim-LLM integrates LLMs with robot simulation (CoppeliaSim) and constructs a language-driven control model that translates natural-language instructions into executable robot behavior sequences.
• Two human-robot interaction models are designed: direct control and autonomous control.
• The platform aims to make robotic control and programming accessible to beginners, addressing educational and practical accessibility of complex robotic systems.

Study Design & Method

This is a platform development and evaluation study. The researchers designed and implemented EduSim-LLM, an educational platform combining LLMs with the CoppeliaSim robot simulator, with a language-driven control model that converts natural-language instructions into executable robot behavior sequences. They developed two human-robot interaction models (direct control and autonomous control) and conducted systematic evaluations to assess the platform's usability and effectiveness for beginner learners of robotic control and programming.

What this means for practice

• Software developers. Build a language-driven control layer that translates natural-language instructions into executable robot behavior sequences in a simulator such as CoppeliaSim, so beginners can command a robot without writing low-level code.
• Software developers. Expose two interaction modes — direct step-by-step control and autonomous control — and let learners switch between them, because each suits different tasks and skill levels.
• Software developers. Grade the system against a benchmark spanning instruction complexities before classroom use: across 108 cases on three robot types, success fell from 100% on simple tasks to 94.4% composite and 88.9% complex, so complexity is the failure surface to test.
• Instructors. Use conversational control as the entry point to robotics and Computational Thinking, and reserve low-level programming for once learners hold the concepts, so novices are not blocked by syntax.

Limitations

• The evaluation focuses on platform accessibility and usability for beginners rather than comprehensive learning-outcome measurement.
• The simulation environment (CoppeliaSim) and the specific LLM integration may not generalize to physical robots or other platforms.
• The two interaction models (direct vs. autonomous control) may trade off differently across learner levels and tasks.

Citation

Lu, S., & Zhang, L. (2026). EduSim-LLM: An educational platform integrating large language models and robotic simulation for beginners.