aiqbench analysis
This research identifies two critical friction points in AI-assisted software engineering: the mandatory cognitive load of validating AI outputs and the mental strain caused by an excess of tool suggestions. For engineering teams, these factors represent tangible productivity risks that cannot be ignored as AI integration deepens. The paper calls for practical strategies to manage human oversight and mitigate cognitive overload in daily development workflows.
Human Oversight and Overload: Two Hidden and Costly Burdens of AI-Assisted Software Engineering
arXiv:2606.05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs. In this paper, we characterize two such often-overlooked burdens: (1) the constant need for human oversight and inspection of AI-generated artifacts; and (2) the growing cognitive overload on software engineers from receiving large amounts of suggestions from AI tools. The need for human oversight is not optional-engineers must review, validate, and sometimes rework what AI produces. At the same time, the flood of AI suggestions, prompts, and possible solutions can leave developers mentally stretched. By blending evidence from recent opinions from practitioners, we highlight these often-overlooked challenges and open a conversation about how teams can handle them in day-to-day AI-assisted software engineering.
Abstract:AI is changing how software engineers work, but it often comes with hidden burdens and costs. In this paper, we characterize two such often-overlooked burdens: (1) the constant need for human oversight and inspection of AI-generated artifacts; and (2) the growing cognitive overload on software engineers from receiving large amounts of suggestions from AI tools. The need for human oversight is not optional-engineers must review, validate, and sometimes rework what AI produces. At the same time, the flood of AI suggestions, prompts, and possible solutions can leave developers mentally stretched. By blending evidence from recent opinions from practitioners, we highlight these often-overlooked challenges and open a conversation about how teams can handle them in day-to-day AI-assisted software engineering.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2606.05770 [cs.SE]
(or arXiv:2606.05770v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2606.05770 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 215 words