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Open Access | Just Accepted

PEMUTA: Pedagogically-Enriched Multi-Granular Undergraduate Thesis Assessment

Jialu Zhang1,*, Qingyang Sun1,*, Qianyi Wang1, Risa Higashita4, Hongwu Qin4, Xiaoqing Zhang1( ), Jianfeng Ren2, Jiang Liu1,2,3,4( )

1 Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Guangdong, 518055, China

2 School of Computer Science, University of Nottingham Ningbo China, Zhejiang, 315100, China

3 School of Ophthalmology and Optometry, Wenzhou Medical University, Zhejiang 325035, China

4 Department of Electronic and Information Engineering, Changchun University, 130022, China

* These authors contributed equally to this work.

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Abstract

Undergraduate thesis (UGTE) serves as a critical indicator of a student’s cumulative academic development throughout university education. Although large language models (LLMs) have advanced educational intelligence, existing approaches typically focus on holistic assessment with only a single evaluation score, overlooking nuanced variations across multifaceted criteria. This limits their ability to reflect structural criteria, pedagogical objectives, and multi-dimensional academic competencies. Meanwhile, pedagogical theories have long guided manual UGTE evaluation through multi-dimensional assessment of cognitive development, disciplinary thinking, and academic performance, yet such guidance remains underexplored in automated assessment. Motivated by the research gap, we pioneer the Pedagogically-Enriched Multi-granular Undergraduate Thesis Assessment (PEMUTA) framework, which effectively activates domain-specific knowledge in pretrained LLMs for UGTE assessment in engineering disciplines. Grounded in Vygotsky’s theory and Bloom’s taxonomy, PEMUTA evaluates UGTEs across six fine-grained dimensions: Structure, Logic, Originality, Writing, Proficiency, and Rigor (SLOWPR), alongside an overall holistic assessment. A hierarchical two-stage pipeline is designed to first conduct fine-grained evaluation, then synthesize a holistic judgment, enabling more accurate elicitation of domain-relevant assessment knowledge in pretrained LLMs. Few-shot and role-based prompting are further incorporated to improve alignment with expert evaluation practices without parameter tuning. A benchmark consisting of authentic UGTEs collected from multiple engineering disciplines with expert SLOWPR-aligned annotations is curated to support multi-granular UGTE evaluation. Extensive experiments demonstrate that PEMUTA achieves strong agreement with expert assessments under diverse engineering UGTE evaluation scenarios, showing its substantial potential for fine-grained, pedagogically-grounded UGTE assessment within engineering education.

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Cite this article:
Zhang J, Sun Q, Wang Q, et al. PEMUTA: Pedagogically-Enriched Multi-Granular Undergraduate Thesis Assessment. CAAI Artificial Intelligence Research, 2026, https://doi.org/10.26599/AIR.2026.9150013

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Received: 19 March 2026
Revised: 28 August 2026
Accepted: 20 September 2026
Available online: 21 September 2026

© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)