This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust trajectories, and cultural responses. Participants span academic institutions and diverse firms, enabling contextual comparison. A dynamic sample continuous, intermittent, and wave-specific respondents mirrors real organizational variability and strengthens ecological validity. Methodologically, the study integrates quantitative longitudinal modeling with qualitative thematic analysis to capture temporal, structural, and cultural patterns in AI uptake. We trace AI acculturation through phases of initial resistance, exploratory adoption, and cultural embedding, revealing distinctive trust curves and problem-solving strategies by context: academic environments tend to collaborative, deliberative integration; business environments prioritize performance, speed, and measurable outcomes. Framing adoption as bidirectional challenges deterministic views: AI both reflects and reconfigures norms, decision-making, and cognitive engagement. As the first comparative longitudinal study of its kind, this work advances methodological rigor and offers actionable foundations for human-centred, culturally responsive AI strategies-supporting evidence-based policies, training, and governance that align cognitive performance, organizational goals, and ethical commitments.

翻译：本文提出一个严谨的框架，用于考察学术与商业环境中人工智能（AI）、人类认知、问题解决及文化适应之间的双向关系。它通过探讨AI如何重塑认知过程与组织规范，以及文化价值观与制度背景如何随时间塑造AI的采纳、信任与使用，从而填补了一个关键的研究空白。我们采用三波纵向设计，追踪AI知识、感知能力、信任轨迹及文化响应。参与者涵盖学术机构与各类企业，以实现情境化比较。动态样本包含持续、间歇及波次特定的受访者，反映了真实的组织差异性并增强了生态效度。在方法论上，本研究整合定量纵向建模与定性主题分析，以捕捉AI采纳过程中的时间性、结构性与文化性模式。我们通过初始抗拒、探索性采纳及文化嵌入三个阶段追踪AI的文化适应过程，揭示了不同情境下独特的信任曲线与问题解决策略：学术环境倾向于协作性、审慎性的整合；商业环境则优先考虑绩效、速度与可衡量的结果。将采纳过程框架化为双向互动挑战了决定论观点：AI既反映也重构了规范、决策制定及认知参与。作为首个此类比较纵向研究，本工作提升了方法论的严谨性，并为以人为中心、文化响应式的AI策略提供了可操作的基础——支持基于证据的政策制定、培训与治理，以实现认知绩效、组织目标与伦理承诺的协同。