The article provides a comprehensive overview of how large language models (LLMs) are used and studied in educational settings. It covers a wide range of applications of LLMs to assist students, teachers, and personalized learning systems, highlighting current technologies, advantages, challenges, and future research directions.
Large language models like ChatGPT are transforming education by helping students learn better and by supporting teachers in their work. These AI models can answer questions, correct errors, generate quizzes, and personalize lessons. However, challenges such as potential biases, errors, and privacy require careful attention. The researchers emphasize the need to use these models responsibly and to develop future enhancements that will make AI more fair, reliable, efficient, and responsive to the needs of students around the world.
What did we learn?
Educational applications: LLMs support education through student assistance (e.g., question-solving assistance, error correction, and advice), teacher assistance (e.g., question generation, assignment grading, study material creation), and adaptive learning (e.g., student progress tracking, content personalization).
Effectiveness: Studies show that LLMs can perform well on standardized tests in a variety of subjects and help students by providing high-quality answers quickly. They also help teachers by automating routine tasks, allowing them to focus on more complex teaching.
Datasets and Benchmarks: The paper summarizes publicly available datasets used to evaluate LLMs for educational tasks such as question-solving, error correction, question generation, and grading, highlighting their scope and diversity.
Challenges and risks: Important concerns include bias in AI responses, fairness and inclusiveness, reliability and safety (such as hallucinations or errors), transparency and accountability, privacy and security, and potential overreliance on learners that could harm critical thinking and creativity.
Future directions: Promising areas for further research include the creation of pedagogically aligned models, multi-agent LLM systems for complex tasks, multilingual and multimodal supports, efficient edge computing, specialized models for subjects, and focused ethical and privacy frameworks to guide responsible use in education.
