Large Language Models (LLMs)

Large Language Models (LLMs) — neural network models trained on vast text corpora that generate human-like text, powering most modern AI in education applications. LLMs are the computational backbone of generative AI tutoring, assessment, and content generation in education.

LLMs as the engine of AIED

LLMs are the most-referenced concept in the wiki (60+ articles) because they underpin nearly every AI education application:

Tutoring: AI tutors use LLMs for dialogue, explanation, and problem-solving guidance. Pedagogical training adapts general LLMs for educational use.

Assessment: Grading systems, essay scoring, and item difficulty prediction leverage LLM capabilities.

Content: Generative AI content creation relies on LLMs. Question generation and video generation are LLM-driven.

Safety: Pedagogical Safety, Hallucination Risk, and AI Tutor Safety Harms research examine LLM-specific risks.

Diagnosis: Knowledge Tracing and Cognitive Diagnosis increasingly incorporate LLMs for richer student modeling.

Model-specific research

The wiki covers both general-purpose LLMs (GPT-4, Claude) and education-specific adaptations. Small language model benchmarks compare SLM performance for tutoring. Educational alignment research addresses how to make LLMs pedagogically appropriate.

Connections

LLMs connect to Generative AI (the broader category), Prompt Engineering (how outputs are controlled), RAG (knowledge grounding), Hallucination Risk (the primary limitation), and Pedagogical Safety (educational guardrailing).