LARGE LANGUAGE MODELS AS INTELLIGENT ACADEMIC ASSISTANTS: DESIGN, OPPORTUNITIES, AND CHALLENGES

This comprehensive study investigates the structural integration of Large Language Models (LLMs) as intelligent academic assistants within contemporary higher education frameworks. We establish a system design model that optimizes Retrieval-Augmented Generation (RAG) pipelines paired with reinforcement learning reasoning architectures to facilitate secure, domain-specific intellectual support for researchers and students. Based on empirical pilot telemetry tracking 120 active participants, the study critically analyzes the primary technical opportunities, including automated context summarization, literature synthesis, and personalized adaptive tutoring. Furthermore, critical boundaries such as academic integrity regulations, hallucinations, and copyright ownership constraints are mapped out. The quantitative validation indicates that deploying moderated LLM infrastructures enhances individual research efficiency by 85% while minimizing student cognitive strain.