@inproceedings{haseeb-etal-2026-toward,
    title = "Toward Cross-Domain Automated Feedback: A Comparative Evaluation of Open-Source Models across Diverse Student Assessment Types",
    author = "Haseeb, Muhammad  and
      Hmue, Min Paing  and
      Imam Amjad, Ahmad  and
      Amjad, Maaz  and
      Sheng, Victor",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.64/",
    pages = "951--963",
    ISBN = "979-8-89176-409-5",
    abstract = "Constructive, personalized, and timely feedback is essential to student learning. However, providing such feedback in large classes remains a major challenge. Large language models (LLMs) offer alternatives to support instructors by generating relevant feedback that highlights both student strengths and areas for improvement. Nevertheless, most existing LLM-based feedback systems rely on proprietary APIs and are often tailored to specific tasks, which limits their accessibility, flexibility, and applicability in resource-constrained educational settings. In this study, we investigate the potential of two open-source LLMs (DeepSeek R1 and Qwen 3.5) to support automated feedback generation across three disciplines (e.g., programming assignments, essays, and mathematics problems). We evaluate two prompting strategies (unified and multi-agent) across these domains and use human judgment criteria to assess feedback quality. Through this analysis, we examine the potential of open-source models as practical, scalable alternatives to proprietary API-based systems for developing freely accessible feedback-generation tools. Our results show that a single open-source model can generate useful feedback across diverse domains, though with varying effectiveness. DeepSeek R1 performs better on reasoning-intensive tasks such as mathematics, while Qwen 3.5 works best for holistic tasks such as writing, but both models struggle with programming tasks. We find that model architecture matters more than prompting strategy, and reasoning-optimized models excel in structured domains, while general-purpose models perform better on holistic tasks. Finally, we conclude that a multi-agent approach does not consistently guarantee improved results over a single unified LLM approach."
}