CMM-AISE: A Cognitive Maturity Model for AI-Assisted Software Engineering
1 — Universidad de los Andes, Bogotá, Colombia
2 — Tres Astronautas, Bogotá, Colombia
3 — Blend 360, Bogotá, Colombia
Esta es la versión preprint (de los autores). Fue aceptada para publicación en ICSME 2026, pero aún no ha pasado por la edición y composición finales. La versión definitiva aparecerá en IEEE Xplore — por favor cita la versión publicada cuando esté disponible.
EL ARTÍCULO ESTÁ PUBLICADO EN INGLÉS
The rapid integration of artificial intelligence (AI) systems into software engineering (SE) is significantly transforming how researchers and practitioners perform tasks across the Software Development Lifecycle (SDLC). While prior work has largely focused on productivity gains and automation, significantly less attention has been given to how AI reshapes the cognitive development of software engineers. Traditional models of expertise assume a progression from novice to expert through increasingly complex reasoning, abstraction, and system-level understanding. However, AI systems can both support and disrupt this progression by redistributing cognitive effort between human and machine. In this paper, we argue that AI-assisted engineering introduces a critical tension between cognitive scaffolding, which enhances learning and reasoning, and cognitive substitution, which risks bypassing essential cognitive processes. Furthermore, we argue that substitution risk is not uniform across a practitioner’s skillset but peaks on the least developed skills where the judgment is less consolidated and harder to catch. To address this gap, we propose a Cognitive Maturity Model for AI-Assisted Software Engineering (CMM-AISE) that characterizes how engineers develop and exercise cognitive capabilities across SDLC stages under different levels of AI mediation. The model integrates capability dimensions, maturity levels, stage-dependent cognitive demands, and human responsibilities. We present how cognitive maturity manifests differently across SDLC stages and discuss implications for engineering practice, tool design, and education. Our work provides a foundation for understanding and guiding the future of human–AI collaboration in software engineering.
- cognitive maturity
- AI-assisted software engineering
- SDLC
- human–AI teaming
- expertise development
D.A. González-Rueda, M. Linares-Vásquez, D. Soto, and A. Hurtado, “CMM-AISE: A Cognitive Maturity Model for AI-Assisted Software Engineering,” Proc. IEEE International Conference on Software Maintenance and Evolution (ICSME), 2026, to appear.
BibTeX
@inproceedings{gonzalezrueda2026cmmaise,
author = {Gonz\'{a}lez-Rueda, Daniel Alejandro and
Linares-V\'{a}squez, Mario and
Soto, Daniel and
Hurtado, Andr\'{e}s},
title = {{CMM-AISE}: A Cognitive Maturity Model for
{AI}-Assisted Software Engineering},
booktitle = {Proc. IEEE International Conference on Software
Maintenance and Evolution (ICSME)},
year = {2026},
note = {To appear}
}