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Talk · International Conference on Computer Science and Engineering · September 1, 2026

A Generative AI-Based Technology for Developing Level-Based Instructional Tasks in Grade 9 Informatics Education

Sapura Sattarova
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Abstract

Developing level-based instructional tasks for secondary school informatics is a time-consuming and complex process that requires alignment with curriculum objectives, competency requirements, and students’ cognitive levels. This paper presents a Generative Artificial Intelligence (GenAI)-based technology for automatically developing differentiated instructional tasks for Grade 9 Informatics education. The proposed technology integrates curriculum analysis, prompt engineering, large language models (LLMs), expert validation, and a structured workflow to generate instructional tasks at three learning levels based on Bloom’s taxonomy. The overall process is represented using an IDEF0 functional model, which describes the interactions among curriculum standards, competency requirements, AI-assisted content generation, pedagogical review, and classroom implementation. The developed technology was applied to Grade 9 Informatics lessons, and a quasi-experiment was performed in secondary schools to examine its effectiveness. Results indicate that the AI-supported approach reduced the workload associated with preparing teaching materials, while improving the quality of differentiated learning tasks and student achievement. The developed technology shows that generative AI can be used successfully to help teachers create high-quality leveled teaching tasks.

Artificial Intelligence in EducationEducational Assessment and PedagogyIntelligent Tutoring Systems and Adaptive Learninggenerative AIinformatics educationBloom's taxonomy

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