A Zero-Shot Prompting Approach for Automated Feedback Generation on ENEM Essays - Anchiêta, Rafael T.; Luz, Anthony I. M.; Lopes, Shara L. C.; Moura, Raimundo S. (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15377, 2025)
Automated Essay Scoring (AES) has made significant progress in evaluating written texts, but the generation of constructive feedback for essays, particularly in Portuguese, remains underexplored. This paper proposes a zero-shot prompting approach to automatically generate feedback for ENEM essays, an essential component of formative assessments.We evaluate several Large Language Models (LLMs) – Gemini 2.0 Flash, Sabiá 3, Llama 3-8B, and Qwen 3-8B – to provide feedback on five competencies defined by the ENEM scoring rubric. Using the AES-ENEM dataset, we prompt the models to generate feedback for each competency, comparing their semantic similarity with human feedback using the BERTScore metric. Additionally, a linguist with expertise in ENEM essays evaluates the feedback for its constructiveness and informativeness. Our results demonstrate that while the models perform similarly in BERTScore evaluations, the Qwen model produces the most informative and constructive feedback. This work contributes to the development of automated systems that not only grade essays but also assist in improving student writing skills, potentially reducing teacher workload in large-scale assessments.