# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 6 Conclusions  
In conclusion, this research demonstrates the significant potential of LLMs for automated evaluation in programming courses. No single method is superior in all respects; rather, the optimal approach is a hybrid one that combines the efficiency and scalability of LLMs with the irreplaceable supervision and pedagogical judgment of the human instructor, thereby overcoming the limitations of both purely manual assessment and automated assessment based solely on test cases. The high correlation between LLM-generated scores and human instructor grades, particularly for models such as o3-mini, DeepSeek, and GPT-4o, suggests that these models can effectively replicate human assessment. However, the variability observed across different LLMs and the nuanced discrepancies between AI and human instructors underscore the necessity of human oversight to ensure fairness and accuracy. The developed Python-based interface offers an accessible implementation for diverse educational institutions, though issues of dependence on commercial AI providers and the long-term sustainability of access to these technologies require careful consideration. Looking forward, the optimal approach likely involves LLMs augmenting human expertise, with instructors retaining final authority. Future work should focus on refining rubrics, creating adaptive feedback, and thoroughly investigating the impact of AI assessment on learning outcomes and student perceptions. Conceptualization, [PERSON]; Methodology, [PERSON], [PERSON] and [PERSON]; Software, [PERSON]; Validation, [PERSON], [PERSON], [PERSON] and [PERSON]; Formal analysis, [PERSON], [PERSON], [PERSON] and [PERSON]; Investigation, [PERSON], [PERSON], [PERSON] and [PERSON]; Resources, [PERSON]; Data curation, [PERSON] and [PERSON]; Writing--original draft, [PERSON], [PERSON], [PERSON] and [PERSON]; Writing--review & editing, [PERSON], [PERSON], [PERSON] and [PERSON]; Visualization, [PERSON]; Supervision, [PERSON]; Project administration, [PERSON]; Funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The raw data supporting the conclusions of this article will be made available by the authors on request. The source code for the system is available in the following public repository on GitHub: [[https://github.com/UFV-INGINF/JorGPT](https://github.com/UFV-INGINF/JorGPT)]([https://github.com/UFV-INGINF/JorGPT](https://github.com/UFV-INGINF/JorGPT)) (accessed on 15 June 2025). The authors would like to thank Universidad Francisco de Vitoria for their support. The authors declare no conflicts of interest. This appendix presents the evaluation rubric used in the experiments. ```
<RUBRIC>  Dimension: Logic: Evaluates whether the code logic effectively solves the given problem. Important: in this category, you must not consider syntax errors--they should not lower the score. ```
0: No logic implemented. 1: Attempts logic, but with no coherence. 2: Basic logic, but with serious errors. 3: Structured logic, though incomplete or with critical errors. 4: Logic with major issues that affect functionality. 5: Functional logic, but with moderate errors. 6: Adequate logic with minor errors. 7: Correct logic, only small adjustments needed. 8: Robust logic, minimal errors that do not affect functionality. 9: Clear and precise logic, no functional errors. 10: Impeccable and efficient  
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10: Perfectly optimized code, no redundancy, maximum performance. Dimension: Readability and Style: Ease of understanding the code, including clarity and structure. As the exam is written in a plain text editor, do not penalize the lack of tabulation. ```
0: Illegible and disorganized code. 1: Very difficult to understand, practically unreadable. 2: Hard to follow due to unclear structure. 3: Basic organization, still hard to read. 4: Minimal readability requires extra effort to understand. 5: Generally readable code with some confusing parts. 6: Clear code, but visual structure could improve.