Exploring digital skills cultivation through a blockchain-enabled ‘digital-intelligent workshop’

1 Introduction

The new generation of information technology is rapidly developing worldwide, leading to continuously higher demands on the abilities of university graduates. Driven by rapid technological change and shifting industrial demands, higher education worldwide is under increasing pressure to redesign curricula, strengthen practice-based learning, and develop interdisciplinary competencies that better align graduate capabilities with real-world professional contexts (Graham, 2018). Due to the relatively slow update of content and the large gap between experimental conditions and the actual industrial environment, the original course design and practice model in colleges and universities is difficult to provide students with a learning experience that adapts to the digital and intelligent era. The concept of the “Digital-Intelligent Workshop” originates from the field of the Internet of Things (IoT), and is introduced in this study into the educational context with contextual adaptation and extended application. This reality has led to the conception of the new type of practical platform called ‘Digital-Intelligence Workshop,’ which aims to create a learning and research space for students that is closer to real engineering scenarios and even provides a forward-looking simulated environment by combining intelligent devices, data processing, and virtual simulation. The purpose of this study is to explore a teaching reform path based on the ‘Digital-Intelligent Workshop’, bridging the gap between traditional laboratories and actual production scenarios, and helping students comprehensively improve their core theoretical literacy and practical skills in intelligent information technology and the digital field during their time at school. To address the critical issues of data security, privacy, and the authenticity of students’ learning outcomes in this process, blockchain technology is incorporated to provide a decentralized and tamper-evident mechanism. This ensures that every milestone in the students’ digital training is reliably recorded and verifiable by both educational institutions and industry partners.

1.1 Background and objectives

Currently, higher education in the cultivation of talents for the digital economy and the intelligent era still faces practical problems such as a disconnect between the curriculum system and technological development, insufficient authenticity in practical scenarios, an underdeveloped interdisciplinary collaborative education mechanism, and a need to improve the teaching quality assurance system. In recent decades, higher education worldwide has increasingly emphasized student-centered, outcomes-based education (OBE), with constructive alignment proposed by Biggs highlighting the alignment between learning outcomes, teaching activities, and assessment as a core mechanism for improving teaching quality and learning effectiveness (Huang et al., 2025). The field of higher education is actively practicing this teaching philosophy, focusing on building a quality assurance system for teaching that is compatible with it. In this transformation process, the application of intelligent information technology and the cultivation of digital skills have become key supports for improving the quality of education and teaching (Benešová and Tupa, 2017). In this context, how to improve students’ digital literacy and innovation ability through practice-driven and multidisciplinary integration has become a pressing issue for universities. With the gradual implementation of Industry 4.0 and intelligent manufacturing, the ‘integration of virtual and real’ production model has led to increasingly higher requirements for university graduates in big data processing, intelligent system development, and interdisciplinary collaboration (Ge et al., 2022). However, traditional training programs often emphasize the instillation of knowledge points, lack a practice process driven by real projects, and are unable to provide a high-intensity, cross-disciplinary collaborative environment, resulting in graduates’ intelligent information technology skills not matching social needs and being unable to effectively respond to complex and changing industry demands. In this situation, both the education sector and the industry call for the establishment of more immersive and iterative digital training platforms to create a growth environment for students that is close to the forefront of the industry. The ‘Digital-Intelligent Workshop’ is precisely a response to this demand. The core concept of the ‘Digital-Intelligent Workshop’ originates from the integrated application of technologies such as digital twins, the Internet of Things, and artificial intelligence in industries like manufacturing and services. The platform integrates physical equipment (e.g., industrial robots, intelligent sensors, and 5G communication networks) with virtual simulation systems into a unified data interaction environment, thereby establishing a comprehensive practice-oriented platform capable of multi-source data acquisition, analysis, and visualization. By closely integrating theory with practice and combining online and offline approaches in a diverse manner, the ‘Digital-Intelligent Workshop’ provides new possibilities for universities to cultivate talent in intelligent information technology and digital skills.

1.2 Problems encountered in the training of intelligent information technology talent

With the rapid development of artificial intelligence technology, the ‘Digital-Intelligent Workshop’ is an effective training model for applied talents in the field of information technology, but there are still some shortcomings in ensuring the quality of practical teaching.

1.2.1 The role of practice platforms and bases is not being fully utilized

In colleges and universities in underdeveloped areas, due to insufficient coordination among various entities, enterprises have low enthusiasm because of a lack of explicit benefits and invest little in the enrichment and innovation of training programs and content. Universities also have shortcomings in the integration of teaching and research, which limits the depth of internship content and the breadth of cooperation, making it difficult to create a good practical ecology for engineering. The management system and operational mechanism are not yet perfected, lacking systematic planning and detailed supervision. At the same time, freshmen in universities in underdeveloped areas generally have a weak foundation in computer knowledge. Surveys show that about two-thirds of freshmen know nothing about programming languages, and over 90% have no understanding of programming competitions, leading to problems such as difficulties in getting started in their studies, lack of interest, and weak hands-on skills. As a result, it is difficult for them to effectively integrate theory with practice and to enhance their core competitiveness (Zhang et al., 2023). Therefore, the combination of multiple factors has resulted in practice platforms and bases not being fully utilized, and the effectiveness of practical teaching still needs to be further improved.

1.2.2 Lack of verifiable evaluation and data governance mechanisms

In collaborative educational settings involving universities, government, and enterprises, evaluating student performance and sharing internship data often face trust issues. Traditional centralized databases are prone to subjective tampering and cannot provide verifiable micro-credentials for students’ digital skills. This lack of transparent data governance reduces enterprise confidence in the actual capabilities of graduates, hindering deeper industry-academia integration.

1.2.3 Lack of systematic policy and management support

Some colleges in underdeveloped regions have not yet incorporated intelligent information technology and digital skills development into their long-term development or professional construction plans, and various departments within the schools often lack unified direction and sufficient coordination when promoting related projects. At the same time, the dispersion of educational resources and the diversity of teaching languages brought about by remote areas and multi-ethnic cultural environments have also intensified the need for top-level design and systematic guidance documents (Spady, 1994). Due to the lack of specific standards and operational guidelines regarding training objectives, implementation paths, and resource investment, departments find it difficult to achieve efficient coordination and scientific planning in teaching practice, resulting in a limited integration of digital skills training with regional economic and industrial upgrading.

1.2.4 Lack of faculty with practical internship experience

Many teachers enter the education field directly after graduation. They generally have high academic qualifications and possess rich theoretical knowledge, but they lack work experience and fail to achieve the expected teaching effectiveness. Relying solely on training instructors to enhance practical skills is not feasible; it is necessary to introduce professional practical training teachers and collaborate with industry experts to jointly build a faculty team for internships and practical training.

1.3 Contributions

This article focuses on the practical needs of transforming the talent cultivation model in universities under the background of the digital economy and the intelligent era. Using the ‘Digital-Intelligent Workshop’ as a practical platform, it systematically explores the path of practical teaching reform based on the student-centered and outcome-oriented concepts. By constructing a ‘virtual-real integration’ digital training environment and integrating multidisciplinary knowledge systems with real industry projects, this paper achieves the collaborative optimization of teaching content, teaching methods, and evaluation mechanisms, and conducts multidimensional data analysis and empirical verification of the reform effectiveness.

Third, a blockchain-enabled data governance layer was proposed and implemented to ensure secure storage, traceability, and trusted sharing of teaching and practice data. The empirical results verified the significant effects of this model in enhancing students’ digital thinking and engineering practice ability.

2 Related work

In recent years, with the continuous advancement of the construction of ‘New Engineering’ and the acceleration of global educational digital transformation, domestic and foreign universities have conducted extensive explorations in areas such as the cultivation of talents in intelligent information technology, the reform of engineering practice teaching, and the construction of virtual simulation platforms. These efforts are mainly focused on three dimensions: the reconstruction of teaching concepts, the construction of digital training environments, and the deepening of the ‘Digital-Intelligent Workshop’ and industry-education integration mechanisms.

2.1 Reconstruction of teaching concepts and models

In engineering education, the traditional ‘knowledge-infusion’ teaching can no longer meet the complex and ever-changing industry demands. The concept of outcome-based education (OBE), widely adopted internationally, provides a core framework for addressing this issue. Spady was one of the early proponents of the OBE concept, emphasizing that the education system should be designed backwards and implemented forwards with a focus on the learning outcomes that students are ultimately able to achieve (Edström and Kolmos, 2014). On this basis, project-based learning (PBL) and the CDIO model have been widely integrated into the engineering education system. Edström and Kolmos point out that these two models have significant complementary advantages in cultivating students’ abilities to solve complex engineering problems, teamwork, and self-directed learning (Zhong, 2017). Many scholars, in the context of the ‘New Engineering’ initiative, have further explored the localized practice of deep integration of OBE and PBL. Zhong Denghua emphasized that universities need to proactively respond to the new round of technological revolution and accelerate the digital transformation of traditional engineering disciplines (Grieves, 2014). This study is precisely based on the OBE concept and real project-driven approach, systematically reconstructing the teaching process.。

2.2 Digital teaching environment and the practice of ‘integration of virtual and real'

The digital transformation of education provides technical support for the practice teaching of ‘virtual-real integration’. Traditional university laboratories are often limited by space and equipment costs, making it difficult to replicate real industrial intelligent scenarios on a 1:1 scale. For this reason, ‘Digital Twin’ and virtual simulation technology are gradually being introduced into the field of education. Grieves proposed in his digital twin model theory that by constructing an accurate mapping of physical entities in virtual space, real-time interaction and optimization of full lifecycle data can be achieved (Tao et al., 2018). Tao and others pointed out, when discussing the current applications of digital twins in related fields, that this technology not only plays a core role in smart manufacturing but also provides engineering education with a high-fidelity, immersive interactive environment (Abele et al., 2017). In recent years, some universities have built virtual simulation experiment platforms based on the Internet of Things and cloud computing, effectively improving students’ engineering operation skills. However, existing training platforms are mostly limited to individual courses and lack horizontal integration of data across multiple disciplines.

2.3 The evolution of the ‘digital-intelligent workshop’ and the school-enterprise collaborative education mechanism

To bridge the gap between traditional laboratories and real industries, the education sector and industry have begun exploring the introduction of real production environments into teaching. Against the backdrop of Industry 4.0 in Europe, the concept of the ‘Learning Factory’ has emerged. Abele and others systematically elaborated on the value of ‘learning factories’ in future-oriented manufacturing engineering education, pointing out that by reproducing real value chains and production environments, they enable students to conduct trial-and-error and innovation in highly realistic engineering scenarios (Mourtzis et al., 2018). With the integration of artificial intelligence, big data, and Internet of Things technologies, the ‘learning factory’ is gradually evolving into a ‘Digital-Intelligent Workshop’. Research by Mourtzis and others shows that the ‘Digital-Intelligent Workshop’ platform, which integrates digital twins, cloud collaboration, and intelligent algorithms, can significantly reduce the skills gap between engineering education and actual industrial needs, and provide students with an immersive data-driven analytical experience (Li et al., 2022). In the Chinese education sector, the ‘Industry-Academia Cooperation and Collaborative Education’ project is also actively promoting enterprises to introduce real business pain points and datasets into the construction of the ‘Digital-Intelligent Workshop'.

2.4 Application of blockchain in education and digital training

The integration of blockchain technology in education is rapidly emerging as a solution to data security and credential verification (Gunawan et al., 2021), (El Koshiry et al., 2023). Research shows that decentralized ledgers and smart contracts can effectively guarantee the integrity of students’ learning records, peer evaluations, and micro-credentials (Dewangan and Chandrakar, 2024). In collaborative environments like the ‘Digital-Intelligent Workshop’, blockchain provides a trusted data governance infrastructure where multiple stakeholders—including universities, enterprises, and students—can interact and share data securely without relying on a single centralized authority (Wang et al., 2025). By recording project progress and skill assessments on a blockchain network, the credibility of students’ digital skills is significantly enhanced in the eyes of future employers (Guo and Xu, 2025).

In summary, although fruitful results have been achieved in updating the concept of engineering education and building ‘Digital-Intelligent Workshop’ in colleges and universities in underdeveloped areas, there is still a need to deepen the creation of comprehensive platforms that enable cross-disciplinary collaboration, full-chain data-driven operations, and deep integration with real enterprise production environments. The ‘Digital-Intelligent Workshop’ concept proposed in this paper is based on previous research and further deeply integrates digital twin technology, IoT devices, and real enterprise engineering projects, exploring a new path for cultivating core digital skills that is systematic, visualized, and multi-party collaborative.

2.5 Blockchain in higher education and digital training

The integration of blockchain technology into higher education has recently gained significant academic attention, primarily aiming to address issues of data transparency, secure credentialing, and stakeholder trust. Gunawan demonstrated that smart contract innovations and blockchain-based tokenization can effectively motivate students and securely manage educational records (Gunawan et al., 2021). El Koshiry provided a comprehensive overview of how blockchain innovations lead to verifiable learning outcomes and decentralized academic systems (El Koshiry et al., 2023). Furthermore, in the context of digital-intelligent environments, Dewangan and Chandrakar explored the implementation of blockchain alongside deep learning to develop secure educational digital twins, ensuring that virtual simulation data remains immutable and reliable (Dewangan and Chandrakar, 2024). From a data sharing perspective, methodologies proposed by Wang for trusted sharing of complex engineering data using blockchain can be directly adapted to educational workshops to facilitate secure university-enterprise data exchange (Wang et al., 2025). Building upon these advancements, (Guo and Xu, 2025), emphasized that blockchain-enabled digital governance significantly improves learning assessment systems by establishing a transparent, cross-institutional trust mechanism (Guo and Xu, 2025). This study integrates these concepts by embedding a blockchain governance layer directly into the ‘Digital-Intelligent Workshop’ workflow.

3 Exploration and practice of talent cultivation in the digital field: taking the teaching of the school of artificial intelligence at guangxi minzu university as an example

The teaching team of the School of Artificial Intelligence at Guangxi Minzu University has integrated existing teaching resources into an online teaching platform. By developing micro-courses, practical guides, case libraries, and other methods, it enables teachers and students to collaborate both online and offline. On this basis, the college has introduced the ‘Digital-Intelligent Workshop’ into the entire talent training process. As a new teaching method that integrates digital platforms, practical scenarios, and process evaluation, it relies on learning behavior data, practice process data, and project outcome data to dynamically optimize the organization of teaching content, the design of practical tasks, and the methods of learning support, promoting the shift of course teaching from experience-driven to data-driven. The teaching team breaks traditional disciplinary barriers and reconstructs the relevant curriculum system using a modular approach: on one hand, it consolidates basic theoretical courses such as ‘Programming Fundamentals’ and ‘Database Principles’. On the other hand, additional courses are offered around professional directions and technological frontiers, such as intelligent control, data mining, industrial robot programming, cloud computing, and IoT applications, providing an effective medium for students to carry out targeted practice in the ‘Digital-Intelligent Workshop’. The integrated platform and curriculum system formed as a result not only enhance the coordination of teaching resource allocation and the organization of practical teaching, but also lay a solid foundation for subsequent teaching reforms and student project practice.

In the following, a representative case study is used to assess the related work, and the corresponding quantitative results are presented. As illustrated in Figure 1, the conventional Digital-Intelligent Workshop establishes a collaborative teaching reform framework involving government, universities, and industry. In this framework, the workshop serves as the core carrier for curriculum optimization, project guidance, process management, practice evaluation, and competency development. It reflects the basic operating logic of the Digital-Intelligent Workshop and provides the foundation for multi-party collaborative talent cultivation.

Building upon this conventional framework, this study further proposes an updated blockchain-enabled Digital-Intelligent Workshop system, as shown in Figure 2. Compared with the original framework, the new system introduces a blockchain-based data governance layer to support secure storage, traceability, data integrity, access control, and trusted sharing of teaching and practice data. Through this mechanism, learning behavior data, project process data, evaluation records, and university-industry collaboration data can be maintained in a more transparent, reliable, and tamper-resistant manner. Furthermore, the platform utilizes smart contracts to automatically execute evaluation rubrics when students reach specific project milestones, objectively recording their achievements. A blockchain-based tokenization mechanism is also tentatively integrated to reward active collaboration and high-quality code commits, which not only incentivizes student engagement but also generates a verifiable portfolio of digital skills that partner enterprises can trust.

3.1 Building a practice platform and base for multi-party collaborative co-construction

In terms of the construction of practice platforms and bases, with ‘Digital-Intelligent Workshop’ as the core concept, we are building a collaborative school-government-enterprise practice platform, continuously enriching and innovating the training content. Through the involvement of real corporate projects, schools and enterprises work together to tackle the difficulties during productions, establishing a professional system that closely connects the industrial chain and the innovation chain. In this process, universities, enterprises, and the government work together, regularly and dynamically improving professional talent training programs, forming a flexible and adaptable operating mechanism, enabling students to develop rigorous, lean, and innovative professional qualities in real production environments (Edström and Kolmos, 2014). At the same time, improve the quality assurance system for internships and practical training, benchmark against the professional accreditation standards of engineering education, establish a quality monitoring and feedback loop from the school to the college and then to the program level, and continuously optimize assessment and evaluation methods, comprehensively evaluating students’ overall quality and practical ability through internship journals, process assessments, project defenses, and other aspects (Zhong, 2017). On this basis, establish sound practical training management rules and regulations, forming a complete chain covering plan formulation, organization and implementation, process supervision, and evaluation and improvement, to ensure that every link of internship and practical training teaching follows regulations and has a basis, ultimately creating a good engineering practice ecosystem. Collaborate with companies such as Huawei and iFLYTEK to build an intelligent internship and training platform, integrating teaching resources, parameter configuration, training monitoring, and result analysis modules, and providing support for government and industry decision-making through data visualization.

3.2 Building a systematic policy and management system

At the level of systematic policy and management support, integrate intelligent information technology and digital skills training into the medium- and long-term development plans or professional construction plans of institutions, specifying development directions, implementation paths, and resource allocation plans. At the school level, guiding documents are issued to systematically regulate each link in practical teaching, including teaching objectives, teaching models, teacher training, venue construction, and funding investment, providing operable standards and procedures for colleges and majors to carry out specific work. At the same time, the college encourages students to participate in various skill competitions and advocates promoting teaching through competitions, such as the Guangxi University Student Artificial Intelligence Design Competition, the China Robotics and Artificial Intelligence Competition, and the Ruikang Robot Developer Competition, achieving fruitful results. Students, parents, and society have responded enthusiastically to this approach, and the college has thus received a university-level teaching award (Grieves, 2014). Strengthen the link between industry enterprises and universities by regularly holding seminars or joint meetings to promptly share talent training needs, assess the progress of practical training, and plan and implement continuous improvements; at the same time, establish diversified project and funding support mechanisms to guide enterprises to deeply invest in practical training projects, introduce advanced technologies and management concepts, thereby achieving the combination of policy implementation and resource coordination. Fully consolidate the combined efforts of all departments in terms of direction, intensity, and goals, forming a systematic support from top-level planning to implementation, providing a stable environmental guarantee for talent development.

3.3 Strengthen the construction of the internship and training teaching staff team

In terms of faculty team development, there is a dual-mentor system and a combination of full-time and part-time staff, with corporate experts and school teachers jointly forming an open internship and practical training team. Enterprise mentors provide a real industry project environment and technical guidance, while school mentors are responsible for academic guidance, thesis writing, and process management. The two complement each other and can effectively address the shortcomings of college teachers in practical ability and industry frontier dynamics. At the same time, universities adopt multiple measures to enhance teachers’ practical skills, organize teachers to take part-time positions in enterprises for training, participate in joint training or actual projects, and regularly carry out experience exchanges and workshops both within and outside the institution. The supporting assessment and incentive mechanism recognizes teachers’ contributions in practical teaching and industry-education integration, forming a multi-dimensional incentive system that includes career development, professional title evaluation, and performance appraisal, encouraging more teachers to engage in teaching reform and innovation in internships and practical training. Through this series of measures, on-campus and off-campus educational resources can deeply integrate with industry resources, and the practical teaching that students receive can better align with real production environments and social needs. At the same time, encourage teachers to act as corporate technology commissioners, participating in work such as conducting policy briefings within enterprises, facilitating communication channels, assisting in strategic planning, carrying out technical research, promoting the transformation of results, improving R&D systems, and enabling growth through technology. As the artificial intelligence industry accelerates toward the center stage, the academy is also seizing the opportunity, guiding teachers to carry out skills training for various traditional industries, further expanding the application scenarios of technology empowerment, and directly promoting teachers’ in-depth understanding of social needs and professional development directions.

3.4 Figure 3D Implementation and practice of reform

To ensure the effective implementation of the ‘Digital-Intelligent Workshop’ teaching model, the college has carried out the following detailed steps and methods. First, clarify project-driven as the core principle, organizing students in groups to take on project tasks from enterprises or simulated real environments, and collaborate around processes such as requirement analysis, system development, and test iterations. Second, to bridge the gap between teaching and industry needs, actively promote deep involvement of corporate engineers in the teaching process, provide technical requirements and business pain points, and offer targeted guidance and evaluation during review and defense sessions. Third, using the IoT sensors and log analysis tools equipped in the ‘Smart Digital Workshop’ to monitor and diagnose students’ learning behaviors and project progress in real time, forming visualized learning data. Fourth, based on data analysis, teachers promptly grasp the level of students’ skill mastery and differences in team collaboration, identify problems, and provide personalized guidance, continuously optimizing teaching strategies. Fifth, in terms of evaluation methods, there is an attempt to build a multi-dimensional evaluation system that combines the learning process with outcomes. Through project stage assessments, presentations of practical results, and peer evaluations among students, the goal is to comprehensively examine students’ problem-solving process, team interaction, communication and expression, and innovative thinking, among other comprehensive abilities. Sixth, through the collaboration of industry, academia, and research, real business cases and data are integrated into teaching. Outstanding group projects are selected for pilot testing or prototype verification in enterprises, achieving a ‘win-win’ situation for both educational outcomes and industrial applications. In order to effectively implement the ‘Digital Intelligence Workshop’ model, special attention must be paid to the interaction between teachers and students, stimulating students’ enthusiasm for active participation, and, combined with data-driven process management, providing targeted improvement suggestions to achieve continuous optimization and iteration.

4 Data analysis results

To investigate the implementation effect of the ‘Digital-Intelligence Workshop’ in cultivating digital skills, the project team designed a questionnaire focusing on aspects such as students’ ability improvement, theoretical understanding, engineering practice, learning process experience, and platform usage experience. The survey targeted undergraduates, postgraduates, and top-up students of the School of Artificial Intelligence, distributed online, mainly using Likert five-point scale questions, supplemented by a small number of basic information questions and open-ended opinion questions. The content of the questionnaire is mainly used to reflect students’ subjective perception of the ‘Digital Intelligence Workshop’ teaching model, and to cross-validate it with platform behavior data and project outcome data.

The formal survey covered a cumulative sample of about 2,000 people, with a total of 1,624 valid questionnaires collected, yielding a validity rate of 81.2%. To more intuitively present the results of the data analysis, this paper visually integrates multiple dimensional indicators such as sample structure, questionnaire validity, student evaluation results, and changes in the learning process, as shown in Figure 2.

4.1 Sample coverage and data foundation

The data foundation of this study is mainly composed of questionnaire survey data, platform behavior data, course process records, and student project outcome data. The platform behavior data and project outcome data are periodically anchored to the blockchain network to ensure their authenticity and prevent ex-post modification. Among them, questionnaire data is used to reflect students’ subjective perception of the ‘Digital-Intelligence Workshop’ teaching model, platform behavior data is used to record students’ performance in task completion, collaborative interaction, and learning engagement, and course process records and project outcome data are used to assist in verifying the rationality of the questionnaire results. Table 1 depicts the questionnaire dimensions and representative items for the “digital-intelligence workshop”.

TABLE 1. Questionnaire dimensions and representative items for the “digital-intelligence workshop”.

4.2 Overall performance of students’ skill improvement

The survey results show that the ‘Digital-Intelligence Workshop’ has a significant promoting effect on students’ improvement in digital skills. As shown in Figures 3A,B, total samples amount 2000, and usable samples 1624 from Undergraduate Level 1-4, and Postgraduates level 1-3 and college upgrade program level 1-3. As shown in Figure 3C, 95.2% of students believe that their abilities in data analysis, algorithm programming, and system modeling have improved. 91.4% of students believe that project-based teaching helps deepen their understanding of theoretical knowledge. 87.6% of students believe that the enterprise cases and real tasks in the platform enhance their engineering practice ability and career adaptability. After weighting by valid samples, the three positive evaluations correspond to approximately 1,546, 1,484, and 1,423 people, respectively.

From the distribution of results, students show the highest recognition of ‘digital skills enhancement,’ followed by ‘enhanced theoretical understanding,’ and then ‘improvement in engineering practice and career adaptability,’ indicating that the ‘Digital Intelligence Workshop’ has a more direct promoting effect on the cultivation of application-oriented abilities.

4.3 Changes in learning behavior and practice process

In addition to the questionnaire survey, the project team also combined the platform’s log system and, taking the teaching platform ‘Chaoxing’ used by the School of Artificial Intelligence at Guangxi Minzu University as an example, conducted a quantitative analysis of students’ learning behaviors in the ‘Digital Intelligence Workshop’. The results show that after implementing this model, the average project completion cycle for students was shortened by about 18%, and the frequency of student peer evaluation and collaborative behaviors increased by 34%. As shown in Figure 3D, this model demonstrates significant effects in optimizing task progress efficiency and enhancing learning interaction.

Further analysis indicates that the shortening of project completion cycles not only reflects an improvement in task execution efficiency but also demonstrates the platform’s optimization in task organization and process support; the increase in the frequency of collaborative behaviors suggests that this model effectively promotes the shift of students from individual learning to collaborative learning.

4.4 Comprehensive analysis

By combining the results of the questionnaire with platform behavior data, it can be seen that the ‘Digital-Intelligence Workshop’ demonstrates a characteristic of multidimensional collaborative improvement in the overall talent cultivation of the college. As shown in Figure 3E, it performs at a relatively high evaluation level across the three core dimensions of digital skills, theoretical understanding, and engineering practice.

Specifically, firstly, the ‘Digital-Intelligence Workshop’ has a synergistic promoting effect on students’ digital skills, theoretical understanding, and engineering practice abilities. Secondly, it has to a certain extent improved the efficiency of task advancement and the level of collaborative interaction in the project implementation process. Third, promote the transformation of practical teaching from the traditional experience-driven model to a digital-intelligent model that combines task-driven and data-supported approaches.

Overall, within a sample of about 2,000 people, the ‘Digital Intelligence Workshop’ has demonstrated good teaching adaptability and platform support capability.

5 Conclusion and outlook

The ‘Digital-Intelligence Workshop,’ as an integrated practical platform that combines virtual and physical elements and spans from data collection to intelligent applications, provides a new approach for universities to cultivate high-quality talents. Based on this platform, this study conducted a series of explorations in curriculum system optimization, teaching method innovation, evaluation reform, and school-enterprise collaboration, specifically leveraging blockchain technology to establish a trusted data governance ecosystem. Initially verifying the positive value of the ‘Digital-Intelligence Workshop’ in enhancing students’ practical abilities and innovative awareness. With the continuous iteration of digital and intelligent technologies, the status and role of the blockchain-enabled ‘Digital-Intelligent Workshop’ in talent cultivation in universities will become increasingly prominent.

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Footnotes

Edited by: Chengzu Dong, Lingnan University, Hong Kong, SAR China

Reviewed by: Frank Jiang, Deakin University, Australia

Albert Husnoo, Qassim University, Saudi Arabia

Angela An, Deakin University, Australia