Intelligent tutoring systems are increasingly used in schools, providing teachers with valuable analytics on student learning. However, many teachers lack the time to review these reports in detail due to heavy workloads, and some face challenges with data literacy. This project investigates the use of large language models (LLMs) to generate brief, actionable summaries of assignment reports, making key insights more accessible. We evaluated different solutions to tabular data summarization, including direct text conversion, sentence serialization, and rule-based aggregation approaches. Our findings suggest that sentence serialization is currently the most viable approach, offering informative summaries with moderate token usage. Future work will focus on refining these methods, exploring teachers' perceived utility of a summary, and assessing the impact on teachers' engagement. Code and data are available at: https://osf.io/yzts6/files/osfstorage?view_only=d5bd7f557d8843da8 fa3a6b52e3822fe. [This paper was published in: "Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S '25), July 21-23, 2025, Palermo, Italy," ACM, 2025, pp. 356-360.]