Phone-to-EDU: A Smart Contract-Based Framework for Comprehensive Management of GAI-Assisted Programming Courses

Smartphones are widely used Internet of Things (IoT) devices in higher education, but relying on them alone does not sufficiently aid teachers in managing courses or improving student learning. For teachers, managing course progress through predefined rules and safeguarding student privacy at key stages is challenging. Likewise, students lack targeted learning assistance to enhance their capabilities. Therefore, a comprehensive scheme is essential to address these challenges effectively. In this paper, we propose a novel framework based on smart contracts and integrated with generative artificial intelligence (GAI) assistance. This framework manages programming courses and enhances student learning, with smartphones serving as access points. It leverages smart contracts to dynamically manage the entire course lifecycle, with contracts built upon access control policies to ensure that only authorized roles can access course resources via smartphone. The proposed framework is based on a consortium network. GAI model-ChatGPT-4o provides code generation and code explanation assistance in programming courses. We select the optimal prompt templates and store them on the blockchain, allowing GAI to provide more precise services using them. We implement and evaluate the proposed framework using the Hyperledger Fabric blockchain, demonstrating its effectiveness and scalability in real-world scenarios. Additionally, we evaluate the code-related content generated by the prompt templates using three criteria: pass rate, time spent, and number of votes. This evaluation confirms that the templates selected and recorded on the blockchain are optimal. The framework can be easily adapted to other scenarios such as course management, record management, and research collaboration.

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Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-06-16
DOI
https://doi.org/10.1109/jiot.2025.3580160
Citations
1
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
1.49

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article

Phone-to-EDU: A Smart Contract-Based Framework for Comprehensive Management of GAI-Assisted Programming Courses

Zimin Lin, Lei Guo, Wenfei Ge, Yuan Gao et al.
1 citations
IEEE Internet of Things Journal
Distributed and Parallel Computing Systems
1.49
article

Phone-to-EDU: A Smart Contract-Based Framework for Comprehensive Management of GAI-Assisted Programming Courses

Zimin Lin, Lei Guo, Wenfei Ge, Yuan Gao, Tao Liu, Beibei Li, Hao Song
article en
1 citations

Abstract

Smartphones are widely used Internet of Things (IoT) devices in higher education, but relying on them alone does not sufficiently aid teachers in managing courses or improving student learning. For teachers, managing course progress through predefined rules and safeguarding student privacy at key stages is challenging. Likewise, students lack targeted learning assistance to enhance their capabilities. Therefore, a comprehensive scheme is essential to address these challenges effectively. In this paper, we propose a novel framework based on smart contracts and integrated with generative artificial intelligence (GAI) assistance. This framework manages programming courses and enhances student learning, with smartphones serving as access points. It leverages smart contracts to dynamically manage the entire course lifecycle, with contracts built upon access control policies to ensure that only authorized roles can access course resources via smartphone. The proposed framework is based on a consortium network. GAI model-ChatGPT-4o provides code generation and code explanation assistance in programming courses. We select the optimal prompt templates and store them on the blockchain, allowing GAI to provide more precise services using them. We implement and evaluate the proposed framework using the Hyperledger Fabric blockchain, demonstrating its effectiveness and scalability in real-world scenarios. Additionally, we evaluate the code-related content generated by the prompt templates using three criteria: pass rate, time spent, and number of votes. This evaluation confirms that the templates selected and recorded on the blockchain are optimal. The framework can be easily adapted to other scenarios such as course management, record management, and research collaboration.

IEEE Internet of Things JournalVol. 12(17)
University of Electronic Science and Technology of China (CN), China University of Political Science and Law (CN), Sichuan University (CN), Jilian Technology Group (China) (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 16%
Distributed and Parallel Computing Systems
1.49
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