Maintaining Academic Integrity in the Era of Large Language Models: A Guideline for Responsible Prompt Engineering

Abstract

This chapter provides comprehensive guidance for academic researchers on effectively integrating Large Language Models (LLMs) in research workflows. Beginning with technical foundations and capabilities, it examines LLMs' architecture, training mechanisms, and specific applications in academic tasks such as text summarization, literature review, data analysis, and code generation. The chapter then offers detailed criteria for model selection and presents advanced prompt engineering techniques, including specificity guidelines, constraint formatting, chain-of-thought prompting, and in-context learning approaches. Particular attention is given to ensuring academic integrity through robust reasoning frameworks, validation protocols, and ethical considerations regarding plagiarism and transparency. The chapter concludes with systematic approaches to critical evaluation, including quality assessment criteria, adapted peer review processes, and standardized documentation practices for LLM implementation in academic research.