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This chapter explores how artificial intelligence (AI), particularly Large Language Models (LLMs), can assist teachers in informatics and IT education for lower secondary students (Year 8 and 9, aged 13–15). LLMs challenge educators by requiring them to manage the varying levels of students’ digital competencies, computational thinking, and motivation. However, LLMs provide a unique opportunity by enabling tailored instruction for distinct student groups, addressing their unique needs within a single classroom environment. This study investigates how LLMs can enhance personalised learning experiences, influence student engagement, and improve learning outcomes in Scratch and Python programming or presentation design. It also identifies the specific challenges and notable benefits of integrating LLMs into the educational process, particularly regarding students’ problem-solving skills and abilities. The tasks are designed around basic user skills and familiar applications like word processors, spreadsheets, and presentation tools to significantly enhance digital literacy among students. Those particularly interested in informatics engage in Scratch, while Python programming is intended for students with more advanced computing skills. This comprehensive approach allows teachers to manage diverse learning needs more effectively. The effectiveness of this innovative teaching model is assessed through a detailed analysis of LLM-generated data, including student dialogues with LLMs, and a follow-up survey.

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AI Literacy and Differentiated Learning in Programming Among Lower Secondary School Students Interacting with LLMs

  • 章节

  • 发表日期：

    2026年

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  • Martin Beneš,
  • Miroslava Černochová,
  • Hasan Selcuk

摘要

This chapter explores how artificial intelligence (AI), particularly Large Language Models (LLMs), can assist teachers in informatics and IT education for lower secondary students (Year 8 and 9, aged 13–15). LLMs challenge educators by requiring them to manage the varying levels of students’ digital competencies, computational thinking, and motivation. However, LLMs provide a unique opportunity by enabling tailored instruction for distinct student groups, addressing their unique needs within a single classroom environment. This study investigates how LLMs can enhance personalised learning experiences, influence student engagement, and improve learning outcomes in Scratch and Python programming or presentation design. It also identifies the specific challenges and notable benefits of integrating LLMs into the educational process, particularly regarding students’ problem-solving skills and abilities. The tasks are designed around basic user skills and familiar applications like word processors, spreadsheets, and presentation tools to significantly enhance digital literacy among students. Those particularly interested in informatics engage in Scratch, while Python programming is intended for students with more advanced computing skills. This comprehensive approach allows teachers to manage diverse learning needs more effectively. The effectiveness of this innovative teaching model is assessed through a detailed analysis of LLM-generated data, including student dialogues with LLMs, and a follow-up survey.