Prompt Engineering for Automatic Short Answer Grading in Brazilian Portuguese - Mello, Rafael Ferreira; Rodrigues, Luiz; Cabral, Luciano; Pereira, Filipe Dwan; Pereira Júnior, Cleon; Gasevic, Dragan; Ramalho, Geber (Anais do Simpósio Brasileiro de Informática na Educação (SBIE); 2024: Anais do XXXV Simpósio Brasileiro de Informática na Educação, 2024)
Automatic Short Answer Grading (ASAG) is a prominent area of Artificial Intelligence in Education (AIED). Despite much research, developing ASAG systems is challenging, even when focused on a single subject, mostly due to the variability in length and content of students' answers. While recent research has explored Large Language Models (LLMs) to enhance the efficiency of ASAG, the LLM performance is highly dependent on the prompt design. In that context, prompt engineering plays a crucial role. However, to the best of our knowledge, no research has systematically investigated prompt engineering in ASAG. Thus, this study compares over 128 prompt combinations for a Portuguese dataset based on GPT-3.5-Turbo and GPT-4-Turbo. Our findings indicate the crucial role of specific prompt components in improving GPT results and shows that GPT-4 consistently outperformed GPT-3.5 in this domain. These insights guide prompt design for ASAG in the context of Brazilian Portuguese. Therefore, we recommend students, educators, and developers leverage these findings to optimize prompt design and benefit from the advancements offered by state-of-the-art LLMs whenever possible.