Alexander Chang ¹ David Garcia ² , ¹ USA, ² University of California, USA

Large language models are increasingly integrated into educational and professional environments; however, effective interaction with these systems requires prompt engineering skills that most users have not formally developed. This paper presents an intelligent mobile application designed to teach prompt engineering through structured instruction, iterative practice, and AI-powered real-time feedback. The system integrates user authentication, a scaffolded educational framework, and live interaction with large language models through OpenAI. Key challenges addressed include feedback consistency, learner engagement, and operational cost management. Experimental evaluations examined the reliability of AI-generated feedback and the sustainability of API usage under simulated user loads. Results demonstrated strong alignment and correlation between AI evaluations and expert assessments, as well as significant efficiency gains through response caching. By combining meta-prompting, adaptive learning design, and mobile accessibility, the proposed application enables continuous skill development and offers a scalable solution for improving effective communication with large language models.

Prompt Engineering, Mobile Application, AI, Real-time Feedback