Interactive data structure and algorithm visualization based on AI agents

doi:10.3969/j.issn.1000-5641.2025.05.004

Abstract

Abstract:

Data structures and algorithms (DSA), as a core course in computer science education, play a key role in cultivating programming skills and algorithmic thinking of students. Visualization can significantly enhance teaching effectiveness and deepen student understanding in DSA education. However, existing DSA visualization tools often rely on manually written visualization codes that lead to limitations such as limited coverage, high maintenance costs, and lack of interactivity; hence, the needs of dynamic demonstrations and personalized teaching are difficult to meet. With the outstanding performance of large language models (LLMs) in code generation, automated DSA visualization has become a promising possibility. Therefore, this study proposed an interactive visualization code generation method based on the reasoning and acting (ReAct) AI agent framework, aiming to address the low automation and insufficient interactivity of traditional visualization tools. By leveraging the code generation capabilities of LLMs and integrating with the data structure visualization (DSV) platform interface, the proposed method transformed Python-based DSA code into interactive, executable, and dynamically visualized code, thereby enhancing teaching clarity and learning experience. To systematically evaluate the effectiveness of the method, we constructed a dataset of 150 pairs of DSA code and corresponding DSV visualization code and compared three approaches—direct prompting, chain-of-thought prompting, and the ReAct AI agent approach—across several mainstream LLMs. The experimental results showed that the proposed ReAct AI agent-based method significantly outperformed the other approaches in terms of the compilation rate, execution rate, and usability rate, with the best performance observed in the DeepSeek-R1 model. This demonstrated notable improvements in the accuracy and interactivity of generated visualization code. This research confirms the feasibility and advantages of integrating LLMs with agent frameworks in DSA visualization teaching, offering a novel path toward building efficient, personalized, and automated tools for computer programming education.

Key words: data structure and algorithm visualization, large language model, AI agent, code generation

CLC Number:

TP391

Ruiyang PANG, Xuesong LU. Interactive data structure and algorithm visualization based on AI agents[J]. J* E* C* N* U* N* S*, 2025, 2025(5): 32-42.

Figures/Tables 9

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References 26

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模型	方法	CR@1/%	CR@3/%	ER@1/%	ER@3/%	UR@1/%	UR@3/%	NLD/%
Qwen2.5-Coder-7B-Instruct	Direct	57.45	78.67	37.33	62.07	9.56	31.98	76.39
	CoT	60.98	85.41	45.56	65.60	11.73	34.07	75.31
	ReAct	71.81	91.93	47.24	74.21	12.08	35.62	75.12
Qwen2.5-Coder-14B-Instruct	Direct	68.43	83.48	41.17	64.18	14.31	33.29	75.34
	CoT	69.56	89.97	47.78	71.30	15.19	34.67	74.50
	ReAct	83.42	93.54	49.63	79.81	17.87	35.93	73.81
Qwen2.5-Coder-32B-Instruct	Direct	78.68	95.43	46.47	78.67	25.24	51.31	73.59
	CoT	81.29	96.01	49.12	80.14	26.67	52.49	72.88
	ReAct	87.53	97.17	57.83	85.48	30.29	53.63	72.62
DeepSeek-Coder-6.7B-Instruct	Direct	43.56	70.88	21.59	32.63	4.05	10.52	91.44
	CoT	48.11	78.69	24.30	39.41	5.33	13.26	89.85
	ReAct	49.47	81.32	28.13	41.09	5.98	15.72	88.15
DeepSeek-Coder-33B-Instruct	Direct	46.74	82.67	23.77	40.18	4.67	12.07	88.04
	CoT	49.27	84.33	28.12	46.00	5.56	16.71	86.92
	ReAct	50.56	87.26	30.57	48.42	6.12	17.32	84.81
DeepCoder-14B-Preview	Direct	48.97	85.34	26.33	46.32	8.11	14.38	88.60
	CoT	51.56	88.27	28.67	49.65	10.65	17.46	86.21
	ReAct	53.32	89.02	29.81	51.18	11.17	19.21	85.02
DeepSeek-R1	Direct	83.43	96.77	63.11	86.53	45.98	72.61	71.93
	CoT	87.89	97.12	65.37	91.34	48.31	78.96	71.86
	ReAct	91.52	98.28	68.53	93.14	50.46	82.75	69.84

模型	方法	简单			中等			困难
模型	方法	CR@3/%	ER@3/%	UR@3/%	CR@3/%	ER@3/%	UR@3/%	CR@3/%	ER@3/%	UR@3/%
Qwen2.5-Coder-32B -Instruct	Direct	96.57	90.17	77.78	95.18	90.17	47.73	93.48	40.47	1.96
	CoT	97.26	93.65	79.37	95.84	93.28	53.28	94.01	45.81	4.27
	ReAct	98.41	94.92	87.48	96.23	94.09	62.72	94.97	50.40	5.65
DeepSeek-Coder-33B -Instruct	Direct	87.31	80.14	43.81	73.75	37.27	4.55	60.11	3.54	0
	CoT	89.82	86.25	48.57	75.27	43.19	6.81	64.18	3.87	0
	ReAct	91.53	88.73	50.24	79.52	47.67	7.72	66.74	4.02	2.33
DeepCoder-14B -Preview	Direct	85.43	79.60	42.27	76.82	51.82	5.92	65.63	14.17	0
	CoT	87.62	84.96	47.46	78.63	59.10	7.61	72.09	20.23	1.04
	ReAct	92.15	86.81	51.62	80.26	62.43	11.36	75.13	23.26	2.57
DeepSeek-R1	Direct	99.34	90.04	84.16	89.81	86.36	68.19	88.15	49.27	10.47
	CoT	100	93.18	89.44	92.14	87.52	70.24	91.45	52.09	13.22
	ReAct	100	95.47	91.65	94.71	90.41	75.13	93.02	59.57	16.51

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