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From Programming to Prompting

Developing Computational Thinking through Large Language Model-Based Generative Artificial Intelligence

Bibliographic Data

ID12815340
AuthorsHsiao-Ping Hsu (0000-0002-3943-2690, corresponding author)
Year2025
Volume69
Issue3
Pages485-506
Publication date2025-02-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTechTrends (JOURNAL)
Journal identifiersISSN: 8756-3894 • E-ISSN: 1559-7075
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s11528-025-01052-6
OpenAlexW4407661387
LanguageEN
Citations received5
References cited131

The advancement of large language model-based generative artificial intelligence (LLM-based GenAI) has sparked significant interest in its potential to address challenges in computational thinking (CT) education. CT, a critical problem-solving approach in the digital age, encompasses elements such as abstraction, iteration, and generalisation. However, its abstract nature often poses barriers to meaningful teaching and learning. This paper proposes a constructionist prompting framework that leverages LLM-based GenAI to foster CT development through natural language programming and prompt engineering. By engaging learners in crafting and refining prompts, the framework aligns CT elements with five prompting principles, enabling learners to apply and develop CT in contextual and organic ways. A three-phase workshop is proposed to integrate the framework into teacher education, equipping future teachers to support learners in developing CT through interactions with LLM-based GenAI. The paper concludes by exploring the framework’s theoretical, practical, and social implications, advocating for its implementation and validation

Cognitive science · Computational Thinking · Educational technology · Generative grammar · Generative model · Mathematics education · Multimedia · Computer Science · Genetics, Bioinformatics, and Biomedical Research · Psychology · Robotics and Automated Systems · Teaching and Learning Programming · Artificial Intelligence

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Unique citing works5
Citations per year5
Citation span2025 - 2026 (2)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 5

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