EduSim-LLM: Modular Educational Simulation

• EduSim-LLM is a modular educational simulation platform that integrates LLMs with robotic simulation environments for natural-language-driven control and collaboration.
• It employs a four-module pipeline combining natural-language interfaces, LLM-based instruction planning, real-time simulation control, and user-friendly visualization.
• The system supports zero-coding workflows, multi-agent orchestration, and domain-grounded simulations, validated by robust experimental benchmarks across robotics and virtual classrooms.

EduSim-LLM is a modular educational simulation platform that integrates LLMs with robotic simulation environments to enable natural-language-driven control and collaborative interaction for both human learners and autonomous agents. Originating with a focus on robotics and subsequently generalized across diverse educational domains, EduSim-LLM unifies structured prompt engineering, real-time simulation backends, and multi-agent interaction paradigms. This system supports zero-coding workflows, multi-robot orchestration, student and teacher modeling, and domain-grounded learning activity simulation, yielding a flexible infrastructure for research and practice in AI-powered education (Lu et al., 3 Jan 2026, Liu et al., 11 Sep 2025, Yang et al., 5 Aug 2025, Zhang et al., 2024, Yue et al., 2024, Xu et al., 2024, de-Fitero-Dominguez et al., 2024).

1. System Architecture and Core Workflow

EduSim-LLM adopts a four-module, end-to-end pipeline designed for novice accessibility and extensibility:

1. Natural-Language Interface: Accepts user input (text or speech), applying a prompt-engineering template to serialize instructions into structured queries.
2. LLM-Based Instruction Planner: Utilizes LLMs (e.g., Groq’s llama-3.3-70b-versatile, Ollama’s llama3.1:8b) via LangChain, generating executable Python code composed of calls to an established library of robot- or agent-control primitives.
3. Simulation Control Backend: Interprets and executes action primitives in a simulated environment through real-time APIs (e.g., CoppeliaSim remote API), supporting joint-position control, base-motion, manipulation, and sensory feedback loops.
4. User-Facing Frontend: Employs frameworks such as Gradio to visualize simulation state, execution traces, and live agent views, maintaining a fully code-free interface for end users.

These modules enable bidirectional data flow: parsed instructions from users are transformed into structured actions by the LLM and executed in the backend, with state updates and visual feedback streamed back to the frontend (Lu et al., 3 Jan 2026).

2. Language-Driven Control Models and Prompt Engineering

At its mathematical core, EduSim-LLM implements a mapping $f: \mathcal{L} \to \mathcal{A}$, where $\mathcal{L}$ is the space of natural-language instructions and $\mathcal{A}$ is the space of finite, ordered sequences of executable action primitives $a = [(p_1, \theta_1), ..., (p_n, \theta_n)]$, with $p_i$ as primitive types (e.g., moveToXY, closeGripper) and $\theta_i$ as parametrizations (Lu et al., 3 Jan 2026).

Prompt engineering is used to enhance instruction conformity and parsing fidelity. A canonical template instructs the LLM as follows:

“Finally, generate a Python list called actions, where each element is a call to RobotController.<method>(<parameters>) needed to accomplish the user’s task. Use only the provided primitives: [list of primitives]. Output nothing else.”

This structured wrapping increases instruction-parsing accuracy by over 15% relative to unconstrained prompting regimes in ablation studies (Lu et al., 3 Jan 2026). The paradigm generalizes to other domains, such as question-answering simulation (mapping QA histories to correctness or mastery-level probabilities) and role-driven agent behaviors in virtual classrooms (Liu et al., 11 Sep 2025, Zhang et al., 2024, Yue et al., 2024).

3. Multi-Agent and Human–Robot Interaction Modes

EduSim-LLM extends beyond single-agent or single-robot control, providing support for complex coordination, autonomous role-driven behavior, and interaction modeling:

• Direct Control Mode: Immediate, turn-by-turn parsing and dispatch of atomic user instructions (e.g., “Move forward 0.5 m”) with real-time execution feedback prior to the next command.
• Autonomous Control Mode: High-level directives are decomposed into multi-step action plans (e.g., “Retrieve the red block, bring it to the left bin, then return to start”), which are parsed, sequenced, and executed by robots or agents in a bundled fashion, with synchronized progress updates (Lu et al., 3 Jan 2026).

For collaborative scenarios, the system supports multi-robot orchestration by interleaving action sequences across heterogeneous platform types (e.g., three KUKA YouBots with differential drive, vision, and manipulation capabilities), enabling multi-agent task decomposition and coordination (Lu et al., 3 Jan 2026).

In pedagogical settings, agent roles (teacher, assistant, classmates) are instantiated by customizing LLMs via system prompts. Session controllers or meta-planners manage function inventories and turn-taking, implementing real-world classroom phenomena such as collaborative teaching, peer support, and discipline restoration (Zhang et al., 2024).

4. Domain-Specific Simulation and Student Modeling

Through integration with robot simulators (CoppeliaSim) and domain-grounded agent role schemata, EduSim-LLM accommodates a range of educational domains with specialized technical underpinnings:

• Robotics: Real-time simulation control, multi-stage deceleration, and trajectory tracking for manipulator arms, grippers, and differential-drive bases. Supports object transport, vision streaming, and collision management.
• Question-Answering Simulation: Uses a knowledge- and reasoning-distilled architecture (such as LDSim), combining graph-based embeddings, attention over question-concept graphs, and soft mastery-level predictions distilled from teacher LLMs. The simulator achieves high simulation fidelity (ACC=0.8179, AUC=0.8668 on Junyi dataset) with low resource overhead (~170 MB GPU, 0.73 s per 30-response sequence) (Liu et al., 11 Sep 2025).
• Virtual Classrooms: Agent role definition, turn-structured dialogue management, and meta-planner–driven stage advancement for tasks such as mathematical modeling, collaborative problem solving, and teacher training (Zhang et al., 2024, Yue et al., 2024, Xu et al., 2024, de-Fitero-Dominguez et al., 2024). Simulation outputs can be analyzed via educational-theory-derived metrics, e.g., Flanders Interaction Analysis System (FIAS) and Community of Inquiry (CoI).

5. Evaluation Metrics, Experimental Results, and Benchmarks

EduSim-LLM’s performance and effectiveness have been empirically evaluated across several educational and simulation benchmarks:

• Instruction-Parsing Success Rate: For robot control, simple tasks (1–2 steps) yielded 100% success, composite (3–4 steps) 94.4%, and complex (5–6 steps) 88.9%, demonstrating robust translation of natural language to action (Lu et al., 3 Jan 2026).
• Efficiency: In robotics, natural-language input reduced user interaction time by over 17 seconds on complex tasks relative to manual GUI control.
• Simulation Fidelity: The LDSim QA simulator outperforms LLM-free and prompt-only baselines in accuracy and AUC across four standard datasets; ablation studies confirm the necessity of both knowledge and reasoning distillation (Liu et al., 11 Sep 2025).
• Classroom Simulation: In SimClass, LLM-powered multi-agent classrooms match traditional FIAS-derived teacher/student utterance ratios, and the inclusion of peer agents increases quiz-derived learning gains and social/cognitive CoI presence ratings (Zhang et al., 2024).
• Agent Communication: Echo-mode reciprocal exchanges in AgentSME maximize both accuracy and diversity of reasoning in multi-agent settings, with high-capacity models showing marked gains over solo or unidirectional modes (Yang et al., 5 Aug 2025).

6. Limitations and Prospective Directions

EduSim-LLM exposes several limitations requiring further research:

• Ambiguity in Natural Language: Unclear user instructions can result in missed or erroneous action primitives, especially in open-loop execution without sensor-driven re-planning (Lu et al., 3 Jan 2026).
• Open-Loop Execution: Current pipeline does not support closed-loop adaptation (dynamic re-querying of the LLM based on real-time feedback); future work aims to integrate sensor-triggered LLM re-planning (Lu et al., 3 Jan 2026).
• Sim-to-Real Generalization: No real-hardware transfer evaluation; real-world calibration, actuation, and environmental variability may degrade simulation-trained policies.
• Role Drift and Schema Cascades: For agent-based classrooms, LLMs may shift out of scripted roles or propagate initial schema misparsing through the dialogue, necessitating improved state tracking and error correction (Yue et al., 2024).
• Resource Constraints: Multi-turn, multi-agent LLM simulations incur nontrivial latency and token costs; optimizations and distilled model variants can mitigate these at scale (Liu et al., 11 Sep 2025).

Targeted proposals include fine-tuning for failure modes, sim-hardware transfer via ROS/Gazebo, richer role and memory modeling, and human-in-the-loop reflective correction pipelines (Lu et al., 3 Jan 2026, Xu et al., 2024, Yue et al., 2024).

7. Generalization and Future Applications

The EduSim-LLM paradigm is generalizable across educational and training domains requiring natural-language interaction, simulation-based reasoning, or multi-agent collaboration:

• Robotics and Automation: Language-driven synthetic environments facilitate scalable, accessible robotics education and research.
• Personalized Learning and Knowledge Tracing: Simulators provide safe, data-rich evaluation grounds for deploying and validating recommender systems, student modeling algorithms, and agent-based coaching (Liu et al., 11 Sep 2025, Xu et al., 2024).
• Classroom, Group, and Pedagogical Simulation: Role-customized agent frameworks (SimClass, MathVC, AgentSME) enable the study and improvement of real-world classroom dynamics, including communication diversity and peer effects (Zhang et al., 2024, Yue et al., 2024, Yang et al., 5 Aug 2025).
• Teacher Training and Feedback: Automated analysis of open-ended teacher-candidate responses, especially with LLMs resilient to new instructional behaviors, is now feasible at fine granularity and low operational cost (de-Fitero-Dominguez et al., 2024).

In summary, EduSim-LLM unites advances in LLMs, prompt engineering, simulation backends, and agent-based reasoning within a practical, extensible framework for zero-coding, research-grade educational simulation (Lu et al., 3 Jan 2026).