{"ID":2848011,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10961","arxiv_id":"2512.10961","title":"AI as Equalizer or Amplifier? Task Complexity as the Moderating Factor for Human Expertise in Hybrid Intelligence Systems","abstract":"A growing body of empirical research suggests that generative AI narrows performance gaps between novice and expert workers on routine tasks--the so-called \"equalizer\" effect. This paper challenges the generality of that conclusion. Drawing on cognitive augmentation theory, expert-novice research, and structured observations of in-house generative-AI use across a small software product team, we argue that AI functions primarily as a cognitive amplifier: a system whose output quality depends fundamentally on the expertise of the human who directs it. We present a framework comprising three layers of human contribution (problem definition, quality evaluation, iterative refinement) and three levels of engagement (passive acceptance, iterative collaboration, cognitive direction), demonstrating that domain expertise--not prompt engineering skill--determines amplification effectiveness. We reconcile the equalizer and amplifier perspectives by proposing that AI equalizes performance on well-structured, routine tasks while amplifying pre-existing differences on complex tasks requiring deep judgment. This reconciliation carries direct implications for hybrid human-AI system design: rather than building AI that replaces expertise, we should build AI that rewards and develops it. We outline a research agenda for the HHAI community centered on expertise-sensitive AI design, adaptive collaboration interfaces, and longitudinal studies of human capability development in AI-augmented work.","short_abstract":"A growing body of empirical research suggests that generative AI narrows performance gaps between novice and expert workers on routine tasks--the so-called \"equalizer\" effect. This paper challenges the generality of that conclusion. Drawing on cognitive augmentation theory, expert-novice research, and structured observ...","url_abs":"https://arxiv.org/abs/2512.10961","url_pdf":"https://arxiv.org/pdf/2512.10961v2","authors":"[\"Tao An\"]","published":"2025-10-30T11:55:34Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[]","has_code":false}