<![CDATA[Algorithm learning remains challenging in computer science education due to its abstract logic, steep conceptual difficulty, and lack of personalized support in traditional settings. This study presents AlgoLLM, a modular instructional system built on large language models (LLMs) to support students through natural language explanations, code-level guidance, and feedback-based refinement. The system includes four core components: Knowledge Explainer, Exercise Generator, Code Assistant and Debugger, and Feedback Evaluator. A four-week case study was conducted with 60 undergraduate students, comparing a control group using textbooks and an experimental group using AlgoLLM. Paired and independent t-tests showed that the experimental group achieved significantly higher learning gains in post-tests (mean increase of 18.3 percent, Cohen's d = 0.94). Code accuracy and task efficiency also improved. Pearson correlation revealed a moderate relationship between LLM interaction frequency and learning gain. Questionnaire feedback indicated high perceived usefulness, clarity, and satisfaction. These results suggest that LLM-based systems like AlgoLLM can enhance algorithm comprehension and offer scalable, personalized support in technical education]]>
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