Unified LLM Intelligent Learning Framework: An End-to-End Solution from Personalized Learning to Interview Simulation

Based on the MERN tech stack and Google Gemini large language model, this framework integrates three core functions: intelligent learning material generation, adaptive assessment, and interview simulation. It builds an end-to-end intelligent education platform through resume-driven question generation and context-aware learning paths, addressing the "one-size-fits-all" issue of traditional education platforms and the high cost of interview preparation.

In digital education, learners face dual challenges of information overload and personalized needs. Traditional platforms struggle to adapt to different knowledge backgrounds, learning paces, and career goals; interview preparation relies on manual tutoring, which is costly and hard to scale. Breakthroughs in LLM provide new ideas, but existing applications are mostly single-point tools lacking end-to-end integration solutions—this framework fills this gap.

The system is built using the MERN tech stack (MongoDB, Express.js, React, Node.js) with a cloud-native design that uses Cloudinary for file storage and Google Gemini as the AI engine. Core modules include: 1. Intelligent learning material generation (resume-driven learning, multi-format support, context-aware generation); 2. Adaptive assessment (intelligent question bank generation, LLM-as-Grader scoring, diagnostic feedback); 3. Interview simulator (role-specific scenarios, resume-driven questioning, real-time interactive feedback).

To address LLM hallucination issues, the framework uses multi-layer protection: 1. Retrieval-Augmented Generation (RAG) to obtain context from trusted knowledge bases; 2. Multi-round verification to ensure logical consistency and factual accuracy of content; 3. A manual review interface for administrators to oversee content in critical scenarios.

Experiments show that the group using this system improved knowledge mastery efficiency by about 40%, and user satisfaction scores were 35% higher; in interview simulation tests, job seekers' real interview performance was better than the control group, and they built psychological confidence when facing interviewers. Users praised the resume-driven personalized experience, which can accurately identify skill gaps and generate targeted content.

Current limitations: Insufficient content depth in highly specialized fields (e.g., cutting-edge quantum computing), requiring integration of domain expert knowledge bases; interview simulation only supports text interaction. Future plans: Introduce speech synthesis/recognition for oral practice; integrate multi-modal capabilities (images, charts, videos); explore multi-agent collaborative simulation scenarios.

This framework amplifies educational effectiveness and accessibility through human-machine collaboration, covering the complete journey from knowledge acquisition to ability verification. It demonstrates the potential of LLM for edtech practitioners, corporate training leaders, and lifelong learners, looking forward to a more intelligent, personalized, and inclusive educational future.