Code Comprehension Gaps in AI-Assisted Development: A Conceptual Framework for Knowledge Erosion Patterns — Patel et al. (2026)

The increasing adoption of AI-powered code generation tools has introduced a critical challenge in software engineering, the potential erosion of developer comprehension of codebases increasingly composed of AI-generated artifacts. Before examining this erosion, we establish that human code comprehension remains a functional requirement in AI-augmented development on four independent grounds: regulatory accountability obligations, the need for human oversight of AI failures, organizational resilience against tool unavailability, and the causal reasoning demands of incident response. This paper examines how AI-assisted development may create knowledge gaps within engineering teams by presenting a conceptual framework for understanding code ownership levels and proposing a taxonomy of knowledge voids that may emerge in AI-augmented development. We propose theoretical models for understanding how AI-generated code may differ from human-written code in ways that affect long-term comprehension and maintenance. Our analysis leads us to hypothesize that AI-generated code may exhibit patterns including lower explanatory documentation, different structural complexity characteristics, and greater implementation diversity, factors that could increase comprehension challenges during maintenance. We conclude with hypothesis-driven implications for development practices and identify critical needs for longitudinal research to validate these theoretical concerns in real-world development contexts. This Brief reads the authors' abstract and conclusion in the authors' own words. Abstract source: pdf-extraction Authors: M.S. Patel, R. Dhawan, L. Bramhanwade, D.J. Mishra Publication date: 2026 Read the paper Source license: Creative Commons Attribution 4.0 International Narration: Read verbatim by an AI-generated voice. The authors and publisher do not sponsor or endorse this recording. Full episode