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

TL;DR

This study investigates how four key learning factors—experience, clarity, comfort, and motivation—interrelate in AI-augmented education and how these relationships differ between middle and high school students, revealing developmental differences in perception structures.

Contribution

It provides the first comparative analysis of learning factor interrelationships in AI-mediated learning across different adolescent age groups, highlighting developmental differences.

Findings

Middle school students show strong positive correlations across all learning factors.

High school students exhibit weak or no correlations, indicating more independent evaluation of factors.

Developmental stage influences how students perceive and relate to AI learning environments.

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.