Date
Publisher
arXiv
In recent years, large language models (LLMs) and generative AI have
revolutionized natural language processing (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.
Study at a glance
- Question: How can generative AI be used to automatically generate questions and evaluate student answers?
- Setting and data: Not stated in the abstract
- Method: Not stated in the abstract
Why it matters
Human assessments are costly and time-consuming. While large language models offer new capabilities in education, question generation and automated assessment face quality variations and areas needing improvement to effectively streamline educational processes.
Key findings
- Large language models can accurately evaluate student responses, provide constructive feedback, and identify misconceptions, though some assessment areas still need improvement.
- Prompting techniques enable language models to generate high-quality open-ended and multiple-choice questions across multiple languages, though question quality varies across different methods.
- When appropriately guided, large language models have the potential to streamline educational processes by replacing costly, time-consuming human assessments.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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