Enhancing Academic Feedback through GenAI Prompt Engineering

Abstract

This study investigates the efficacy of various prompt engineering frameworks for providing feedback on students’ academic assignments using generative artificial intelligence (GenAI) tools, such as ChatGPT and Gemini. The focus is on developing effective prompts for formative assessment feedback. Faculty instructors often face substantial challenges in managing the heavy workload of grading a large volume of assignments within limited timeframes. To mitigate these challenges, educators are increasingly turning to GenAI tools to streamline the feedback process.The methodology involved a systematic literature review to list the 11 prompt engineering frameworks and further interact with ChatGPT and Gemini to develop and compare feedback from these frameworks. Structured prompts were crafted, refined, and tested across various academic scenarios to assess their effectiveness, ensuring the findings are relevant and applicable in real-world educational contexts.The results show that tailored prompt engineering can significantly enhance the efficiency and personalization of assignment feedback, potentially transforming how academics evaluate student work. By integrating these advanced GenAI tools, educators can better manage their workload while maintaining the quality of feedback provided to students. This study contributes to the emerging field of AI in education by offering practical insights into the application of GenAI tools and developing prompt engineering frameworks that enhance academic feedback mechanisms.

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