{"id":6756,"date":"2026-05-13T12:11:10","date_gmt":"2026-05-13T04:11:10","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6756"},"modified":"2026-05-13T15:55:03","modified_gmt":"2026-05-13T07:55:03","slug":"jase-202609-32-032","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-032","title":{"rendered":"Research on the Construction and Application of an AIGenerated Content-Based Intelligent Educational Evaluation System"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Ye Su1<\/sup>, Zhenzhong Huang2<\/sup><\/i><\/a> , and Yuewang Cao3<\/sup><\/p>\n\n\n\n

1<\/sup>Guangxi Normal University, School of DesignGuilin, Guangxi Province 541004, China<\/p>\n\n\n\n

2<\/sup>Guangxi Science & Technology Normal University, Future Teachers College, Laibin, Guangxi province 546199, China<\/p>\n\n\n\n

3<\/sup>Nanning University, School of Marxism, Nanning, Guangxi province 530299, China<\/p>\n<\/div>\n\n\n\n

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Received: January 31, 2026
Accepted: March 9, 2026
Publication Date: May 13, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Conceptual Workflow of the Implemented AIGC Evaluation Process for Text-Based Assignments  <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.032<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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With the rapid advancement of Large Language Models (LLMs) and AI-Generated Content (AIGC) technologies, educational assessment is entering a new phase of intelligent upgrading. LLM-based teaching evaluation faces rubric misalignment, scale drift, and low interpretability. This study proposes an AIGC framework using automated testing, LLM analysis, rubric alignment, and Low-Rank Adaptation (LoRA) fine-tuning for multidimensional assessment of text, code, and learning behavior. Experimental results demonstrate that rubric-structured prompting combined with LoRA fine-tuning effectively mitigates scoring scale drift by calibrating model outputs to instructor-defined grading distributions, improving Pearson correlation from
0.72 to 0.89 while significantly reducing systematic bias. The dual-channel evaluation strategy (functional testing + AIGC static analysis) enhances interpretability by separating execution correctness from structural quality, achieving 0.88 overall agreement with instructor grading. Furthermore, the proposed human-machine collaborative mechanism dynamically balances automated efficiency and expert validation, reducing grading time by approximately 75% while preserving grading reliability. Collectively, these components establish a unified, interpretable, and scale-stable intelligent educational evaluation system.<\/p>\n\n\n\n

Keywords: AIGC; Large Language Models; Intelligent Educational Assessment; LoRA FineTuning; Automated Code Grading; Rubric Alignment<\/em><\/p>\n\n\n\n

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