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Scenario Engine: Reshaping Interactive Education Scenarios with Large Language Models

Scenario Engine is an open-source educational tool that allows teachers to create large language model (LLM)-based interactive learning scenarios without writing complex scripts. Students complete learning objectives by conversing with AI characters and receive intelligent assessments.

教育技术大语言模型交互式学习场景模拟AI教育角色扮演智能评估开源项目
Published 2026-04-15 20:35Recent activity 2026-04-15 20:49Estimated read 6 min
Scenario Engine: Reshaping Interactive Education Scenarios with Large Language Models
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Section 01

[Introduction] Scenario Engine: Reshaping Interactive Education Scenarios with Large Language Models

Scenario Engine is an open-source educational tool designed to enable teachers to create large language model (LLM)-based interactive learning scenarios without writing complex scripts. Students complete learning objectives by conversing with AI characters and receive intelligent assessments based on the dialogue content. This tool addresses the challenge of balancing scale and personalization in traditional online education, bringing a new paradigm to educational technology.

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Section 02

Background and Dilemmas of Educational Technology

Traditional online education faces the contradiction between scale and personalization: pre-recorded videos lack real-time feedback and targeted guidance, while one-on-one tutoring is effective but hard to popularize. The emergence of large language models provides new possibilities to solve this dilemma, as they can dynamically generate interactive content, balancing scale and personalization.

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Section 03

Core Design Concepts of Scenario Engine

  1. Goal-driven instead of script-driven: Teachers only need to define learning objectives, scenario backgrounds, and evaluation criteria; the LLM automatically generates dialogue flows, avoiding the high costs and path limitations of traditional decision trees.
  2. Immersive role-play: Supports creating AI characters with specific personalities and knowledge backgrounds, suitable for communication training scenarios like medical consultations and customer service.
  3. Intelligent evaluation mechanism: Analyzes student performance based on predefined objectives and generates in-depth feedback reports instead of simple right/wrong judgments.
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Section 04

Key Technical Implementation Points

  1. Prompt engineering and character setting: Define character information through a structured configuration interface, converting it into optimized prompts to ensure AI responses meet scenario requirements.
  2. Context management: Tracks dialogue history, student skills, and scenario status to maintain dialogue coherence.
  3. Multi-dimensional evaluation framework: Allows customizing evaluation dimensions (e.g., knowledge mastery, communication skills) and their weights to align with teaching objectives.
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Section 05

Application Scenario Examples

Scenario Engine applies to multiple fields:

  1. Medical education: Simulates patient consultations, evaluating students' consultation skills and diagnostic thinking.
  2. Business training: Simulates scenarios like sales and complaint handling, providing instant feedback.
  3. Language learning: Immersive dialogue scenarios for practicing target languages.
  4. Legal and ethical education: Case analysis and ethical discussions, evaluating legal thinking and ethical judgment.
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Section 06

Value for Educators

  1. Lower creation threshold: Visual interface and natural language configuration allow teachers without technical backgrounds to create complex scenarios.
  2. Improve update efficiency: No need to maintain large script libraries; quickly adjust scenario parameters and content.
  3. Support personalized learning: LLM dynamically generates content and adjusts direction based on student responses, enabling teaching tailored to individual needs.
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Section 07

Open-Source Community and Future Outlook

Scenario Engine is an open-source project that encourages the community to contribute scenario templates, algorithms, etc. Future plans include adding multi-modal support (images, audio), collaborative learning functions, and more refined learning analysis tools to continuously expand the boundaries of educational applications.