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Learning Factors in AI-Augmented Education: A Comparative Study of Middle and High School Students

Gaia Ebli, Bianca Raimondi, Maurizio Gabbrielli

TL;DR

This study investigates how four learning dimensions—experience, clarity, comfort, and motivation—interact in AI-mediated education and whether their interdependencies differ between middle and high school students. Using a cross-sectional design in authentic CS classrooms, it combines correlation analysis with text mining to reveal that younger students exhibit holistic, highly interconnected perceptions, while older students show more differentiated, modular evaluations. The findings offer developmental insights for designing age-appropriate AI integration in K-12 programming education and underscore the need for tailored interventions, prompt engineering, and teacher professional development. Overall, the work extends established learning-dimension theories to AI-augmented contexts and highlights how developmental stage moderates student–AI interaction patterns.

Abstract

The increasing integration of AI tools in education has led prior research to explore their impact on learning processes. Nevertheless, most existing studies focus on higher education and conventional instructional contexts, leaving open questions about how key learning factors are related in AI-mediated learning environments and how these relationships may vary across different age groups. Addressing these gaps, our work investigates whether four critical learning factors, experience, clarity, comfort, and motivation, maintain coherent interrelationships in AI-augmented educational settings, and how the structure of these relationships differs between middle and high school students. The study was conducted in authentic classroom contexts where students interacted with AI tools as part of programming learning activities to collect data on the four learning factors and students' perceptions. Using a multimethod quantitative analysis, which combined correlation analysis and text mining, we revealed markedly different dimensional structures between the two age groups. Middle school students exhibit strong positive correlations across all dimensions, indicating holistic evaluation patterns whereby positive perceptions in one dimension generalise to others. In contrast, high school students show weak or near-zero correlations between key dimensions, suggesting a more differentiated evaluation process in which dimensions are assessed independently. These findings reveal that perception dimensions actively mediate AI-augmented learning and that the developmental stage moderates their interdependencies. This work establishes a foundation for the development of AI integration strategies that respond to learners' developmental levels and account for age-specific dimensional structures in student-AI interactions.

Learning Factors in AI-Augmented Education: A Comparative Study of Middle and High School Students

TL;DR

This study investigates how four learning dimensions—experience, clarity, comfort, and motivation—interact in AI-mediated education and whether their interdependencies differ between middle and high school students. Using a cross-sectional design in authentic CS classrooms, it combines correlation analysis with text mining to reveal that younger students exhibit holistic, highly interconnected perceptions, while older students show more differentiated, modular evaluations. The findings offer developmental insights for designing age-appropriate AI integration in K-12 programming education and underscore the need for tailored interventions, prompt engineering, and teacher professional development. Overall, the work extends established learning-dimension theories to AI-augmented contexts and highlights how developmental stage moderates student–AI interaction patterns.

Abstract

The increasing integration of AI tools in education has led prior research to explore their impact on learning processes. Nevertheless, most existing studies focus on higher education and conventional instructional contexts, leaving open questions about how key learning factors are related in AI-mediated learning environments and how these relationships may vary across different age groups. Addressing these gaps, our work investigates whether four critical learning factors, experience, clarity, comfort, and motivation, maintain coherent interrelationships in AI-augmented educational settings, and how the structure of these relationships differs between middle and high school students. The study was conducted in authentic classroom contexts where students interacted with AI tools as part of programming learning activities to collect data on the four learning factors and students' perceptions. Using a multimethod quantitative analysis, which combined correlation analysis and text mining, we revealed markedly different dimensional structures between the two age groups. Middle school students exhibit strong positive correlations across all dimensions, indicating holistic evaluation patterns whereby positive perceptions in one dimension generalise to others. In contrast, high school students show weak or near-zero correlations between key dimensions, suggesting a more differentiated evaluation process in which dimensions are assessed independently. These findings reveal that perception dimensions actively mediate AI-augmented learning and that the developmental stage moderates their interdependencies. This work establishes a foundation for the development of AI integration strategies that respond to learners' developmental levels and account for age-specific dimensional structures in student-AI interactions.
Paper Structure (15 sections, 3 figures, 5 tables)

This paper contains 15 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Student Perceptions of Educational Technology
  4. Learning Dimensions
  5. Methodology
  6. Study Design and Context
  7. Data Collection
  8. Analysis Approach
  9. Results
  10. Statistical Analysis
  11. Overall Perception Patterns
  12. Dimension-Level Analysis
  13. Text Mining Analysis
  14. Discussion
  15. Conclusion and Future Work

Figures (3)

  • Figure 1: Correlation matrices between questionnaire items by school level, revealing markedly different patterns of association between middle and high school students.
  • Figure 2: Correlations between aggregated dimensions (Experience, Clarity, Comfort, Motivation) for each school level, showing substantial differences in evaluation coherence between age groups.
  • Figure 3: WordCloud and co-occurrence network for Q7 responses, showing student perceptions of the AI tool's impact on learning for middle and high school. The WordCloud highlights the most frequent terms, while the co-occurrence network reveals semantic relationships between key concepts.