Integrating agile methodologies and AI-assisted learning in web programming education: a theoretical framework for CS curriculum transformation

This paper proposes a conceptual framework integrating agile methodologies with artificial intelligence tools to transform web programming education. Through design science research methodology combining systematic literature synthesis with thematic analysis of published implementation studies, we derive a three-pillar architecture combining iterative learning cycles, AI-augmented scaffolding, and continuous assessment. The framework addresses critical gaps between traditional educational approaches and contemporary professional practices while providing strategies for managing AI over-reliance, ensuring accessibility for diverse learners including neurodiverse and multilingual students, and maintaining assessment integrity. We position this work as a theoretical contribution requiring empirical validation, offering testable propositions and implementation guidance for future research.

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Publication Details

Journal
Discover Education
Published
2026-02-03
DOI
https://doi.org/10.1007/s44217-026-01179-5
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating agile methodologies and AI-assisted learning in web programming education: a theoretical framework for CS curriculum transformation

Pavlo V. Zahorodko, Serhiy O. Semerikov
Discover Education
Teaching and Learning Programming
article

Integrating agile methodologies and AI-assisted learning in web programming education: a theoretical framework for CS curriculum transformation

Pavlo V. Zahorodko, Serhiy O. Semerikov
article en

Abstract

This paper proposes a conceptual framework integrating agile methodologies with artificial intelligence tools to transform web programming education. Through design science research methodology combining systematic literature synthesis with thematic analysis of published implementation studies, we derive a three-pillar architecture combining iterative learning cycles, AI-augmented scaffolding, and continuous assessment. The framework addresses critical gaps between traditional educational approaches and contemporary professional practices while providing strategies for managing AI over-reliance, ensuring accessibility for diverse learners including neurodiverse and multilingual students, and maintaining assessment integrity. We position this work as a theoretical contribution requiring empirical validation, offering testable propositions and implementation guidance for future research.

Discover EducationVol. 5(1)
Kryvyi Rih State Pedagogical University (UA), Zhytomyr Polytechnic State University (UA), National Academy of Educational Sciences of Ukraine (UA), Academy of Cognitive and Natural Sciences
Quality Education
Openalex Percentile: Top 75%
Teaching and Learning Programming
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Integrating agile methodologies and AI-assisted learning in web programming education: a theoretical framework for CS curriculum transformation — Pavlo V. Zahorodko, Serhiy O. Semerikov · Discover Education (2026) | TGRS Research Map | TGRS