Keywords
artificial intelligence
AI-generated course design
cognitive load
educational content redesign
AI in education
How to Cite
Abstract
This study presents a controlled comparative experimental investigation of instructor-designed and AI-generated course structures in higher education. The research was conducted within a bachelor-level course “Intelligent Systems” and involved 100 students randomly assigned to two parallel instructional formats. To examine how these materials influenced learning outcomes, the study employed a mixed-methods research design. The analysis included academic performance evaluation, survey data on perceived cognitive load, conceptual mapping, and content analysis. Statistical procedures involved independent samples t-tests, chi-square tests, and correlation analysis. This approach made it possible to identify how differences in instructional structure shaped students’ engagement and overall learning experience.
The analysis showed that AI-generated content often includes a wider range of practical and interactive activities that are not always present in traditional course design. Students who worked with these materials showed higher cognitive load (M = 3.5) compared to the instructor-designed version (M = 2.6, p = 0.0044). However, this did not translate into poorer academic outcomes (p = 0.1558), indicating comparable learning outcomes across groups. At the same time, the group using traditional content noted greater clarity and a more predictable structure, which helped them move through the material with confidence.
The findings demonstrate that AI-generated instructional structures can function as a complementary instructional assistant rather than a replacement for instructor-designed content. Their integration requires careful pedagogical consideration, including attention to cognitive workload, the alignment of tasks with learning goals, and the instructor’s role in guiding students through unfamiliar formats. The novel contribution of this study lies in empirically conceptualizing AI as a mechanism of structural instructional redesign and examining its relationship with cognitive load dynamics and measurable academic performance. The results provide methodological guidance for balanced AI-supported course development in higher education.
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Copyright (c) 2026 Oleksandra Bulgakova, Viacheslav Zosimov, Sergiy Artemenko, Olga Olshevska