# A Course Intelligence Platform for Higher Education: Lessons from AI-Assisted Course Evaluation

arXiv: https://arxiv.org/abs/2411.02455 · HTML: https://mlsanity.com/paper/2411.02455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)

## Built on by (most cited first, 3 of 3)

- [Investigating Bias: A Multilingual Pipeline for Generating, Solving, and Evaluating Math Problems with LLMs](https://mlsanity.com/paper/2509.17701/) (2025)
- [Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K--12 Science Instructional Materials](https://mlsanity.com/paper/2602.13243/) (2026)
- [SciEval: A Benchmark for Automatic Evaluation of K-12 Science Instructional Materials](https://mlsanity.com/paper/2604.25472/) (2026)

## Abstract (arXiv)

The rapid adoption of generative AI has created new opportunities for teaching, learning, and quality assurance. Existing applications, however, remain largely student-facing, with comparatively limited attention to institution-level needs. This paper presents a course intelligence platform deployed across more than 100 universities and serving over 10,000 instructors in China. By linking competency requirements, knowledge structures, teaching activities, and assessment evidence, it establishes a shared foundation for knowledge organization, instructional design, learning assessment, and quality evaluation. The course evaluation module is examined as a representative institution-facing application of the platform, which integrates national evaluation standards, structured educational evidence, customized prompting strategies, and domain-adapted LLMs to generate quantitative scores and qualitative feedback. A case study involving 100 authentic university courses is conducted to evaluate its alignment with expert judgments and the interpretability of its outputs. Statistical analyses show substantial agreement between AI-generated assessments and expert ratings, while qualitative results highlight the credibility of the feedback. The findings further suggest that AI-assisted evaluation requires not only capable models but also structured domain knowledge and transparent criteria. In this context, human ratings should be treated as an informative reference rather than an error-free gold standard, and the objective is to achieve consistent, interpretable, and defensible judgments instead of merely replicating expert scores.