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The purpose of this paper is to present an innovative web-based eLearning platform called ICT-FLAG that provides e-assessment tools with general-purpose formative assessment services featuring learning analytics and gamification.

The paper reports on the technical development of the platform driven by the Reference Model for Open Distributed Processing software methodology, which guides the platform construction, including the analysis and design steps.

The ICT-FLAG platform is technically tested by integrating it into a real e-assessment tool. Results are positive in terms of functional and non-functional aspects as well as user’s satisfaction on usability, emotional state, thus validating the platform as a valuable educational tool.

Because of the chosen technical paper as article type, validation of the impact of the ICT-FLAG platform in the learning process is not provided. Ongoing research with this platform is to measure the learning outcomes of its use in a real context of eLearning.

The paper shows implications of the main technical issues and challenges encountered during the integration of the ICT-FLAG platform with external eLearning tools, involving relevant aspects of interoperability, security, modularity, scalability, portability and so on.

This platform can fill the gap of many e-assessment systems, which currently do not have built-in analytical and gamification tools for learning, thus providing them with the experience to improve the quality of education and learning.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.