arXiv:2601.04574v2 Announce Type: replace Abstract: Going beyond the prediction of numerical scores, recent research in automated essay scoring has increasingly emphasized the generation of high-quality feedback that provides justification and actionable guidance. To mitigate the high cost of expert annotation, prior work has commonly relied on LLM-generated feedback to train essay assessment models. However, such feedback is often incorporated without explicit quality validation, resulting in the propagation of noise in downstream applications. To address this limitation, we propose FeedEval,
The proliferation of LLMs creates a pressing need to validate and improve the quality of their generated outputs, especially in applications like educational feedback where accuracy and pedagogical alignment are crucial.
Improving the reliability of LLM-generated feedback can significantly reduce costs associated with expert annotation and accelerate the development of robust AI-driven educational tools.
The explicit validation of LLM-generated feedback introduces a critical quality control step, ensuring downstream applications are built on more accurate and pedagogically sound data, rather than propagating noise.
- · Educational technology providers
- · Students receiving AI-generated feedback
- · AI model developers aiming for higher quality outputs
- · Companies relying on unvalidated LLM feedback
- · Traditional, manual feedback providers
Higher quality and more reliable AI-generated educational feedback becomes widely integrated into learning platforms.
Reduced need for human annotators in specific feedback-generation tasks, leading to cost efficiencies for educational institutions.
Enhanced personalization and effectiveness of AI-driven education systems, potentially impacting learning outcomes on a broader scale.
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Read at arXiv cs.CL