Architecture of AI-Assisted Interactive Simulators for Teaching Prompt Engineering: Design Methodology and Implementation Experience

The rapid development of artificial intelligence technologies and their widespread implementation across various spheres of human activity highlight the urgent need for mass education in AI tool usage, particularly in mastering prompt engineering techniques. Traditional educational approaches cannot keep pace with technological change, creating a gap between labor market demands and competencies of educational programs’ graduates.

Purpose. To present the architecture and design methodology of an interactive simulator system with automated assessment based on large language models for teaching various aspects of working with AI tools; to describe the practical implementation experience in development courses.

Methodology and research methods. The study is based on instructional design methodology and gamification theory in education. The system architecture is implemented using client-side web technologies (HTML5, JavaScript) and integration with the API of large language models (OpenRouter) for automated assessment of open-ended tasks. Approbation was carried out in the context of advanced training courses under the program “Introduction to neural networks: practical development of AI platforms”.

Results and scientific novelty. An interactive simulator system architecture was developed, including six specialized modules: prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought, etc.), AI ethics, image and video generation, presentation creation, and AI data analytics. Scientific novelty lies in applying large language models for automated assessment of creative assignments with detailed feedback generation without instructor involvement. The system integrates gamification mechanisms (achievement system, progressive content unlocking) to enhance learner motivation.

Practical significance. The presented system can be integrated into teacher professional development courses and higher education programs in various subject areas. The modular architecture provides the ability to adapt simulators to different subject areas and educational contexts. Areas for further development of the system have been identified, including integration with LMS through SCORM packages and the development of its own plugin for educational analytics.

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