Generation of Programming Exam Question and Answer Using ChatGPT Based on Prompt Engineering
Jongwook Si, Sungyoung Kim

TL;DR
This paper presents a method using prompt engineering with ChatGPT to automatically generate diverse, high-quality programming exam questions and answers, improving assessment efficiency and quality without additional model fine-tuning.
Contribution
It introduces a novel prompt engineering approach for automatic exam question generation, reducing manual effort and enhancing question diversity and quality.
Findings
Generated questions are comparable or superior to manual questions.
The method significantly reduces question preparation time.
Survey results support the effectiveness of the approach.
Abstract
In computer science, students are encouraged to learn various programming languages such as Python, C++, and Java, equipping them with a broad range of technical skills and problem-solving capabilities. Nevertheless, the design of objective examination questions to assess students' creativity, problem-solving abilities, and domain knowledge remains a significant challenge. This paper proposes a methodology to address these challenges by leveraging prompt engineering techniques with ChatGPT. Prompt engineering is an efficient technique that optimizes the performance of language models, enabling the automatic generation of high-quality exam questions with varying types and difficulty levels, all without requiring additional fine-tuning of the model. This study applies diverse patterns and templates to generate exam questions that incorporate both theoretical and practical components,…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Teaching and Learning Programming · Educational Assessment and Pedagogy
