Improving students’ programming performance: an integrated mind mapping and generative AI chatbot learning approach
Abstract With the development of the times, programming education has become increasingly important for individual development. However, for programming beginners such as primary and secondary school students, learning programming is not a simple task and requires additional learning support. Genera...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-04-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-04846-4 |
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| Summary: | Abstract With the development of the times, programming education has become increasingly important for individual development. However, for programming beginners such as primary and secondary school students, learning programming is not a simple task and requires additional learning support. Generative AI (GenAI) chatbots are effective teaching aids that can reduce the learning difficulty of programming by providing real-time guidance and personalized learning support based on students’ abilities. Therefore, it has been a trend to apply GenAI chatbots in teaching. However, technology always has two sides. Over-reliance on these chatbots may weaken students’ ability to think independently and affect their learning effectiveness. Therefore, how to rationally utilize GenAI chatbots in the classroom and improve their application effectiveness has become an important issue for both researchers and frontline teachers. Based on this, the present study proposed a learning method that integrates mind mapping with GenAI chatbots. To assess the effectiveness of this learning method and to investigate whether there are differences in the impact of various types of mind mapping-supported GenAI chatbots on students’ programming academic performance, computational thinking, and programming self-efficacy, the research team conducted a quasi-experimental study. The participants were 111 seventh-grade students at a junior high school in southeastern China. Experimental Group 1 (36 students) used a learning approach that integrated progressive mind maps with a Generative AI chatbot, Experimental Group 2 (36 students) used a learning approach that integrated self-constructed mind maps with a GenAI chatbot, and the control group (39 students) used a traditional AI chatbot-based learning approach. The results showed that participants in both experimental groups had significantly better programming learning performance as well as computational thinking than the control group, and that the learning method integrating progressive mind mapping with GenAI chatbots was more effective. |
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| ISSN: | 2662-9992 |