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The article focuses on developing a web application that automates the creation of test tasks for engineering students. This development utilizes computer vision and machine learning technologies. The proposed approach involves analyzing images that contain text, formulas, diagrams, and graphs, followed by generating questions using artificial intelligence (AI) tools and a conceptual-thesis model. The web application has been built on the low-code platform FlutterFlow and includes three functional modules designed to process text data, mathematical formulas, and images. This structure enables the adaptation of test tasks to align with educational objectives and the knowledge levels of students. The paper also examines the potential of integrating AI to reduce the routine workload for tutors, enhance the accuracy of educational content analysis, and personalize the learning experience. The findings may help optimize pedagogical activities and improve adaptive educational technologies.

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Educational Tasks Automated Generation by Means of Computer Vision and Machine Learning Technologies

  • 会议论文

  • 发表日期：

    2025年

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  • Ivan Shyian,
  • Olena Havrylenko

摘要

The article focuses on developing a web application that automates the creation of test tasks for engineering students. This development utilizes computer vision and machine learning technologies. The proposed approach involves analyzing images that contain text, formulas, diagrams, and graphs, followed by generating questions using artificial intelligence (AI) tools and a conceptual-thesis model. The web application has been built on the low-code platform FlutterFlow and includes three functional modules designed to process text data, mathematical formulas, and images. This structure enables the adaptation of test tasks to align with educational objectives and the knowledge levels of students. The paper also examines the potential of integrating AI to reduce the routine workload for tutors, enhance the accuracy of educational content analysis, and personalize the learning experience. The findings may help optimize pedagogical activities and improve adaptive educational technologies.