The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models

Abstract: In recent years, LLMs and generative AI have revolutionized NLP, offering unprecedented capabilities in education. This chapter explores the transformative potential of LLMs in automated question generation and answer assessment. It begins by examining the mechanisms behind LLMs, emphasizing their ability to comprehend and generate human-like text. The chapter then discusses methodologies for creating diverse, contextually relevant questions, enhancing learning through tailored, adaptive strategies. Key prompting techniques, such as zero-shot and chain-of-thought prompting, are evaluated for their effectiveness in generating high-quality questions, including open-ended and multiple-choice formats in various languages. Advanced NLP methods like fine-tuning and prompt-tuning are explored for their role in generating task-specific questions, despite associated costs. The chapter also covers the human evaluation of generated questions, highlighting quality variations across different methods and areas for improvement. Furthermore, it delves into automated answer assessment, demonstrating how LLMs can accurately evaluate responses, provide constructive feedback, and identify nuanced understanding or misconceptions. Examples illustrate both successful assessments and areas needing improvement. The discussion underscores the potential of LLMs to replace costly, time-consuming human assessments when appropriately guided, showcasing their advanced understanding and reasoning capabilities in streamlining educational processes.