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The rapid adoption of generative AI raises questions not only about its transformative potential but also about its cognitive and societal risks. This study contributes to the debate by presenting cross-country experimental data (n = 150; Germany, Switzerland, United Kingdom) on how individuals engage with generative AI under different conditions: human-only, human + AI (unguided), human + AI (guided with structured prompting), and AI-only benchmarks. Across 450 evaluated responses, critical reasoning was assessed via expert rubric ratings, while perceived reflective engagement was captured through self-report indices. Results show that unguided AI use fosters cognitive offloading without improving reasoning quality, whereas structured prompting significantly reduces offloading and enhances both critical reasoning and reflective engagement. Mediation and latent class analyses reveal that guided AI use supports deeper human involvement and mitigates demographic disparities in performance. Beyond theoretical contributions, this study offers practical implications for business and society. As organisations integrate AI into workflows, unstructured use risks undermining workforce decision making and critical engagement. Structured prompting, by contrast, provides a scalable and low-cost governance tool that fosters responsible adoption, supports equitable access to technological benefits, and aligns with societal calls for human-centric AI. These findings highlight the dual nature of AI as both a productivity enabler and a cognitive risk, and position structured prompting as a promising intervention to navigate the emerging challenges of AI adoption in business and society.
Michael Gerlich (Thu,) studied this question.