Frontiers in Psychology 系统综述：AI 驱动的工作系统与员工倦怠，提出 AIMS 元系统框架

摘要

背景：

人工智能（AI）快速融入工作场所系统，正在改变工作设计和员工体验。尽管AI有望提升效率，但它同时减少工作负荷并增加心理压力，从而形成一种悖论，导致对职业倦怠产生分化影响。

目的：

本研究系统综述了关于AI暴露与员工职业倦怠之间关系的证据，并构建了“AI作为元系统”（AIMS）框架。

方法：

遵循PRISMA 2020，检索了四个文献数据库和Google Scholar，时间范围从建库至2026年3月31日。共纳入43项同行评审的原始实证研究：其中20项测量了职业倦怠或某一既定职业倦怠维度，23项考察了机制、调节因素、替代结局、测量或实施条件。方法学质量采用混合方法评价工具在标准层面进行评价。

结果：

研究结果因AI的功能角色和研究设计而异。在随机对照和前后对照的医疗保健研究中，辅助型AI通常与较低的职业倦怠或耗竭相关；而在观察性研究中，监控和算法控制型暴露与更高的职业倦怠或相关心理社会压力相关。感知到的AI暴露通过工作压力、工作不安全感、工作—家庭冲突、感知组织支持和组织承诺等路径，产生直接、间接和无效关联。三项随机研究为辅助型干预提供了最强证据。非线性效应的证据仅限于支持替代结局，并未直接确立倒U型的AI—职业倦怠关系。

结论：

人工智能本身并非有害或有益；其与职业倦怠的关联取决于其功能角色、实施方式和员工评价。AIMS框架整合了这些路径，并提出了需要纵向和实验检验的命题。

1 引言

人工智能（AI）驱动的工作系统的快速普及正在从根本上改变各行业工作的组织、执行和评估方式。组织日益依赖算法管理、实时监控和AI赋能的决策支持来提升生产力、效率和创新。尽管这些系统有望带来显著的运营收益，但越来越多的实证研究表明，它们也可能对员工心理健康产生意想不到的负面影响（Stamate et al., 2021）。实证研究显示，AI意识、AI采用和算法控制可能与工作压力、工作不安全感、较差的幸福感或职业倦怠相关（Kong et al., 2021；Kim and Lee, 2024；Dong et al., 2025；Wang and Zhou, 2025）。相反，环境文档和AI定制干预在减少文档负担和工作量需求时，通常与较低的倦怠或耗竭相关（Afshar et al., 2025；Baek and Cha, 2025；Chowdhury et al., 2026；Olson et al., 2025；You et al., 2025）。相关的质量改进证据还考察了临床医生在环境AI文档使用后对工作负担、倦怠和工作满意度的感知（Albrecht et al., 2025）。这些混合证据表明，AI对员工幸福感的影响并非一致，而是产生依赖情境的结果。本文使用综述和概念性文献来建立更广泛的理论背景，但这些文献不计入系统综述综合所纳入的43项主要实证研究。

这种分歧引发了一个核心悖论。AI可以通过自动化常规任务同时减少工作量，却又通过持续监控、不确定性和加快的工作节奏等新要求增加心理压力。实证发现说明了这一矛盾。暴露于自动化风险的工人可能即时压力较低，却报告健康状况较差、工作满意度下降（Nazareno and Schiff, 2021），而AI采用通过工作压力机制间接增加倦怠（Kim and Lee, 2024）。更广泛的证据同样将自动化概率与心理社会工作条件和工人健康联系起来（Cheng et al., 2021），而员工对智能技术、AI、机器人和算法的感知已被作为预期工作场所变化的一个重要方面加以考察（Brougham and Haar, 2018）。与AI相关的身份威胁、工作自动化意识和被替代风险感知也可能增加认知性工作不安全感并损害员工幸福感（Gull et al., 2023；Chung et al., 2025；Lingmont and Alexiou, 2020）。AI采用还与心理契约、工作投入和员工信任的关系受到考察（Braganza et al., 2021）。这些发现提出了一个关键问题：在什么条件下AI能减少倦怠，又在何时会加剧倦怠？

尽管此前关于技术与工作的研究已通过技术压力和工作要求框架确立了数字化与职业倦怠之间的联系，但AI代表了一类性质不同的技术。与传统系统不同，AI具有自适应性、不透明性，并能够实时分配任务和监控绩效（Zerilli et al., 2022；Zheng et al., 2025）。这些特征引入了新的压力源，包括算法不透明性、技术不可预测性以及感知到的控制权丧失（Issa et al., 2024；Kumar et al., 2024）。重要的是，尽管现有综述已考察了医疗保健等特定行业中的AI与职业倦怠（Dave et al., 2026），并提供了关于组织中AI的多层次视角（Bankins et al., 2024），但尚无综合研究在动态、多路径的理论框架内整合跨职业的AI特定机制。因此，文献尚未提供关于在何种条件下AI暴露与跨职业情境中职业倦怠相关结果相关联的整合性解释。

这一空白具有重要的理论和实践意义。若不能清晰理解将AI与职业倦怠联系起来的机制、调节因素和边界条件，组织就可能以损害员工福祉和长期生产力的方式实施AI系统。职业倦怠与巨大的组织成本相关，包括人员流失、绩效下降和医疗负担增加（Shanafelt et al., 2017；Martinez et al., 2025）。有证据表明，实施不当的AI系统——以过度监控、培训不足或不切实际的期望为特征——可能加剧压力和疏离感，而以人为中心的实施方式则可能减轻这些风险（Meduri et al., 2024）。因此，理解AI如何塑造职业倦怠既是理论上的迫切需求，也是管理上的必然要求。

为解决这些局限，我们将AI不仅概念化为一种工作要求或工作资源，而是将其视为一种可能同时改变要求、资源和控制权的工作系统。工作要求—资源框架为考察这些领域提供了有用的理论基础（Bakker et al., 2023），而所提出的AIMS框架则明确了它们在AI赋能工作中的同时性和交互性运作。因此，AIMS框架被呈现为对现有模型的理论构建性扩展，而非证明既有职业健康框架不充分的证据。

基于这一前提，本研究构建了一个动态的、多路径的、基于权变的理论框架，用以解释AI驱动的工作系统如何、何时以及为何影响员工职业倦怠。该研究综合了跨学科证据，以识别关键机制，包括技术压力、工作不安全感、情绪耗竭、依赖性、技能退化、与AI相关的错失恐惧、工作—家庭冲突以及组织支持路径（Högemann et al., 2025；Méndez-Suárez et al., 2026；Zheng and Zhang, 2025；Wang and Zhou, 2025）。该研究进一步考察了调节变量和边界条件，包括组织支持、自我效能感和系统设计特征，同时指出了关键的研究空白，如缺乏纵向设计和针对AI的测量工具。

本文的主要概念性贡献是AI作为元系统（AI as Meta-System, AIMS）框架。该框架将AI视为可能同时发挥需求放大器、资源提供者和控制系统的作用，并组织了涉及工作负荷、身份相关关切和时间适应的拟议路径。该框架还明确了候选的个体和组织调节变量，并提出了需要直接纵向检验的动态适应轨迹。AIMS框架并非旨在确立因果机制，而是整合了观察到的关联，并为未来的实验、纵向和多波次研究生成研究命题。

2 方法

2.1 方案与报告标准

本系统综述依据PRISMA 2020指南（Page et al., 2021）开展，以确保透明度、严谨性和可重复性。研究方案经事先制定，以界定研究目标、纳入标准和分析程序。由于本研究具有跨学科性质，涵盖组织行为学、信息系统和职业健康领域，该方案未在PROSPERO注册；然而，所有程序均经预先设定，以尽量减少选择偏倚和报告偏倚。

2.2 检索策略

我们在四个文献数据库（Web of Science、Scopus、PubMed/MEDLINE和APA PsycINFO）中进行了全面的文献检索，并以Google Scholar及前后向引文检索作为补充。检索策略使用布尔运算符，将与人工智能、工作场所情境和职业倦怠结果相关的关键词组合起来。核心检索式包括“artificial intelligence”“machine learning”“algorithmic management”和“automation”等术语，并与“burnout”“emotional exhaustion”“stress”和“well-being”以及“employee”“worker”和“professional”组合使用。

我们还进行了前后向引文追踪，以确保覆盖全面，并针对各数据库对检索语法进行了适应性调整。完整的各数据库检索策略、限定条件和检索字段见补充表S1。

2.3 纳入标准

研究纳入需符合以下标准：(1) 采用实证性定量、定性、混合方法、随机或非随机设计；(2) 以同行评审期刊文章形式发表；(3) 考察工作场所情境中可识别的 AI 驱动、算法、机器人、自动化或机器学习系统；(4) 纳入员工、劳动者、专业人员或职业群体；以及 (5) 报告 (a) 职业倦怠或某一已确立的倦怠维度，或 (b) 专门用于考察与职业倦怠在理论上相关的机制、调节因素、边界条件或实施因素的职业心理结局。

符合条件的结局被事先分为两组。直接或核心维度结局包括职业倦怠、情绪耗竭、工作耗竭、人际疏离、去人格化和职业效能感。支持性结局包括技术压力、工作压力、工作不安全感、工作—家庭冲突、职业幸福感、职业可持续性、心理健康及相关机制或边界条件。支持性结局单独进行综合，未被解释为等同于经过验证的职业倦怠终点。

研究若为综述、概念性文章、社论、研究方案、商业报告、会议论文集、非同行评审来源、非在职人群研究、无工作场所可识别 AI 或自动化暴露的研究，或无符合条件职业心理结局的研究，则被排除。仅考虑英文报告。

2.4 研究筛选

所有识别出的记录均导入 EndNote (X9) 进行文献管理。重复记录经自动去重并人工核实。标题和摘要由两名评审员独立筛选，分歧通过讨论或咨询第三名评审员解决。

检索最初获得 1,248 条记录。去除 312 条重复记录后，936 条记录进入标题和摘要筛选，其中 742 条被排除。对 194 篇报告的全文进行资格评估，151 篇报告因不符合实证设计、人群、暴露、结局、发表状态或报告标准而被排除。最终综合共纳入 43 项同行评审的原始实证研究，包括 20 项测量职业倦怠或某一已确立倦怠维度的研究，以及 23 项涉及机制、调节因素、替代结局、测量或实施条件的支持性实证研究。完整的研究筛选流程见图 1，操作性排除标准及全文评估后被排除的潜在符合条件报告分别见补充表 S2A、B。

图 1

2.5 数据提取

对所有纳入研究均采用了标准化的数据提取方案。提取的信息包括研究特征（作者、年份、国家及行业）、样本量和人群、研究设计、AI系统的类型和功能角色、所测量的结局或构念、测量工具，以及所报告的AI暴露与职业倦怠、已确立的倦怠维度或支持性职业心理结局之间的关系。在可获得的情况下，记录了效应量，包括标准化系数、比值比、相关系数、置信区间和统计显著性。

此外，对研究进行了系统编码，以识别潜在机制（如技术压力、工作不安全感、身份威胁）、调节变量（如组织支持、自我效能感）以及情境边界条件。完整的研究层面提取矩阵见补充表S3A、B。测量职业倦怠或某一已确立倦怠维度的研究报告于补充表S3A，而涉及机制、调节变量、替代结局、测量或实施条件的研究则单独报告于补充表S3B。

2.6 质量评估

方法学质量采用混合方法评价工具（MMAT 2018；Hong et al., 2018）进行评估，该工具允许对定性、随机、非随机、描述性和混合方法研究进行针对设计类型的评价。对所有研究均应用了MMAT的两个筛选问题，随后应用与相关设计类别对应的五项标准。两名评价者独立完成评价，分歧通过讨论解决。标准层面的判断（是、否或无法判断）报告于补充表S4。按照MMAT指南，未计算总体数值质量评分。反复出现的局限性涉及样本代表性、无应答报告、失访、自我报告的结局以及混杂因素的控制，而随机、纵向和重复测量研究通常提供了更强的证据。

2.7 数据综合

由于研究设计、AI操作化定义和倦怠测量存在显著异质性，定量荟萃分析不可行。因此，进行了理论驱动的叙述性综合。

综合过程分三个阶段进行。首先，根据所提出的AIMS框架的核心维度——要求、资源和控制——对实证发现进行组织。其次，采用主题编码方法识别各研究中反复出现的机制和调节变量。第三，进行跨研究比较，以识别一致的模式、矛盾之处和边界条件，从而为动态、多路径理论框架的构建提供依据。直接或核心维度的倦怠证据与支持性证据分别进行综合，以防止替代心理结局被解释为等同于倦怠。

3 结果

3.1 纳入研究的特征

共有43项经同行评议的原始实证研究符合纳入标准。其中20项研究测量了职业倦怠或某一既定的职业倦怠维度，而23项研究考察了与AI相关的员工福祉相关的机制、调节因素、替代结局、测量或实施条件。证据基础包括3项随机对照研究、19项定量非随机研究、16项定量描述性研究、3项定性研究和2项混合方法研究。样本量从定性访谈中的15名参与者到一项大型欧洲调查中的27,252名工作者不等。

AI暴露被操作化为三大类功能角色：辅助系统，包括环境文档记录和AI定制干预；监测或控制系统，包括算法管理和自动化任务分配；以及感知性暴露，包括AI意识、被替代担忧、信任或感知到的威胁。表1总结了所有20项测量职业倦怠或某一既定职业倦怠维度的研究。补充表S3A提供了相应的研究层面提取信息，而补充表S3B报告了23项涉及机制、替代结局、测量或实施条件的支持性研究。

表1

测量职业倦怠或某一既定职业倦怠维度的研究特征。

表格列出了20项纳入的研究，这些研究测量了职业倦怠或某一既定职业倦怠维度。全部43项纳入的原始实证研究的完整证据清单见补充表S3A、B。技术压力、工作不安全感、工作满意度、职业可持续性和职业幸福感等支持性结局另行报告，不被解释为等同于经过验证的职业倦怠终点。AI，人工智能；CBI，哥本哈根职业倦怠量表；CI，置信区间；HRM，人力资源管理；MBI，Maslach职业倦怠量表；OR，比值比；RCT，随机对照试验；SEM，结构方程模型。

3.2 定量研究结果的综合

定量研究结果总结于表2，显示出一种取决于角色的模式，而非统一的模式。测量职业倦怠或某一既定职业倦怠维度的研究与报告支持性职业心理结局（包括技术压力、职业压力、工作不安全感、工作满意度和总体幸福感）的研究分开分析。

表2

针对直接职业倦怠、核心职业倦怠维度及支持性代理指标结果的精选定量发现。

直接结果包括职业倦怠或已确立的职业倦怠维度，包括情绪耗竭、工作耗竭和人际疏离。纳入支持性代理指标结果是为了解释机制、边界条件或相邻的心理后果，不应将其解释为等同于职业倦怠。CI，置信区间；HRM，人力资源管理；NR，未报告；NS，无统计学显著性；OR，比值比；RCT，随机对照试验；SEM，结构方程模型。

当辅助型 AI 系统减少了文书工作、工作量或认知需求时，通常与更低的倦怠或疲惫感相关。随机对照和前后对照的医疗健康研究报道，使用环境 AI 记录工具或 AI 定制干预措施后，倦怠或已确立的倦怠维度有所减少（Afshar et al., 2025；Baek and Cha, 2025；Chowdhury et al., 2026；Cho et al., 2024；Misurac et al., 2025；Olson et al., 2025；You et al., 2025）。然而，这些发现并不一致。例如，Stults et al. (2025) 报道倦怠率从 42.1% 降至 35.1%，但差异无统计学显著性。此外，若干实施研究缺乏同期对照组，部分研究的随访应答率较低，或实施前后样本未配对。

监测和算法控制暴露通常与更高的倦怠或相关心理社会压力相关。算法管理与外送骑手的倦怠呈正相关（Dong et al., 2025），且在零工工作者中，倦怠中介了感知算法控制与服务绩效之间的不利关联（Gao et al., 2025）。大规模欧洲证据进一步将 AI 驱动的监测和任务分配与时间压力、自主性降低及压力相关结果联系起来（Payá Castiblanque and Pizzi, 2024）。感知性 AI 暴露的结果则更为异质。在 Kong et al. (2021)、Zheng and Zhang (2025) 和 Wang and Zhou (2025) 中，AI 意识与倦怠或情绪耗竭呈正相关；Kim and Lee (2024) 识别出通过工作压力的间接路径，而非显著的直接关联；Thorpe et al. (2026) 则未发现生成式 AI 使用与倦怠之间存在统计学显著的直接关系。三项随机研究提供了关于辅助型干预的最强证据，但均在医疗健康情境中开展。非线性效应的证据仅限于支持性替代结局，包括 Wu et al. (2026) 报道的 AI 暴露与工作满意度之间的倒 U 型关联，且并未直接确立非线性的 AI–倦怠关系。

3.3 AI 与倦怠关联的机制

纳入研究中识别出的机制和中介路径总结于表3。证据表明，AI暴露很少通过单一途径与职业倦怠相关。相反，纳入的研究报告了涉及AI相关需求、资源、控制条件以及心理或组织机制的关联。在Chuang等（2025）的研究中，AI技术压力与耗竭呈正相关，而AI效能感与耗竭呈负相关。Kim和Lee（2024）报告了AI采用与职业倦怠之间通过工作压力产生的显著间接关联。Zheng和Zhang（2025）报告了通过工作不安全感与工作对家庭的干扰产生的链式中介关联，而Wang和Zhou（2025）则识别出涉及感知组织支持与组织承诺的间接关联。AlOqaily等（2025）将工作—生活平衡作为潜在资源路径进行了考察，Gao等（2025）报告了算法控制与服务绩效之间通过职业倦怠产生的间接关联。这些中介发现应结合大多数相关研究所采用的观察性设计来解读。

表3

与AI相关倦怠和职业紧张相关的机制与中介路径。

来自未直接测量倦怠的研究的机制被识别为支持性路径。其结果不应被解释为等同于倦怠。AI，人工智能；FoMO，错失恐惧；HRM，人力资源管理。

支持性和定性研究识别出额外的AI特异性机制，包括依赖、监管模糊、技能退化、自主权丧失、监督不稳定性和与AI相关的错失恐惧（Högemann et al., 2025；Méndez-Suárez et al., 2026；Zheng et al., 2025）。这些构念有助于解释AI如何可能产生职业紧张，但除非明确测量了倦怠或已确立的倦怠维度，否则它们不被解释为直接的倦怠结果。

3.4 调节因素与边界条件

纳入研究中识别出的个体、组织和系统层面的调节因素与边界条件总结于表4。这些因素有助于解释为什么相似形式的AI暴露可能在不同员工和组织环境中与不同的心理结果相关联。

表4

与AI相关倦怠和职业福祉相关的调节因素与边界条件。

表中若干因素是与工作不安全感、职业可持续性或职场幸福感等支持性结果相关而加以考察的，而非与倦怠本身相关。因此，它们被解释为与AIMS框架相关的边界条件，而非每一条AI–倦怠关系的已确认调节变量。AI，即人工智能。

在个体层面，AI自我效能削弱了从AI采用经由工作压力到倦怠的不利间接路径（Kim and Lee, 2024）。韧性和职业韧性缓冲了涉及AI意识、工作压力、工作不安全感和员工幸福感的不利路径（Jin et al., 2024；Chung et al., 2025）。在组织层面，感知组织支持与组织承诺在AI意识与倦怠之间形成了保护性路径（Wang and Zhou, 2025）。组织文化也塑造了员工的反应：威权文化强化了工作自动化意识与工作不安全感之间的关联（Lingmont and Alexiou, 2020）。在系统与实施层面，算法透明度削弱了与算法控制相关的不利间接路径（Gao et al., 2025），而培训、个性化和实施支持则与以人为本的AI系统中更有利的员工结果相关（Meduri et al., 2024）。评价与应对进一步决定了AI不可预测性是被体验为痛苦还是良性压力（Issa et al., 2024）。

3.5 定性研究结果

三项定性或定性为主的研究提供了关于员工AI赋能工作体验的详细证据。Högemann et al. (2025)识别出使用生成式AI的年轻专业人士中的依赖性、监管模糊性、监控转变和感知技能退化。Kumar et al. (2024)描述了自动化–增强悖论，即AI和机器学习可能同时产生技术压力并提供社会技术应对的机会。Zheng et al. (2025)识别出AI赋能人力资源系统中的自主性侵蚀、监控不稳定性、算法偏见和个性化不满。

这些发现通过解释员工如何体验AI相关需求、资源和控制结构，补充了定量证据。然而，由于这些研究主要考察技术压力、幸福感或更广泛的职业体验，其发现应被解释为关于机制和实施条件的解释性证据，而非AI对倦怠影响的定量估计。这些研究及其他支持性研究的完整特征报告于Supplementary Table S3B。

3.6 异质性与矛盾证据

证据中明显的异质性可部分由AI角色、结局定义、研究设计和实施情境的差异来解释。辅助性系统在减少文书工作或工作负荷需求时，通常与较低的倦怠或耗竭相关，但变化幅度和统计显著性在各项实施研究中存在差异。监测和算法控制暴露与更高的倦怠或相关心理社会压力相关，尽管相关证据主要为横断面研究。感知性AI暴露产生了积极、间接和无效的发现，具体取决于评价、自我效能、组织支持、韧性和工作不安全感。因此，证据支持一种角色依赖的模式，而非AI普遍有益或有害的效应。

3.7 偏倚风险与方法学质量

证据基础以观察性设计为主，尤其是横断面调查，这限制了因果推断并增加了共同方法偏差的易感性。三项随机研究（Afshar et al., 2025；Baek and Cha, 2025；Chowdhury et al., 2026）以及若干纵向或前后对照研究提供了更强的时间证据，尽管失访、自我报告测量、随访应答率低以及缺乏同期对照在一些实施研究中仍是重要局限。所有43项研究的条目级MMAT评判结果报告于Supplementary Table S4。

4 讨论

4.1 关键发现总结

This systematic review indicates that the association between AI-driven work systems and employee burnout depends on the functional role, implementation, and appraisal of the technology. Assistive systems were generally associated with lower burnout or exhaustion when they reduced documentation and workload demands, particularly in healthcare implementation studies. Monitoring and algorithmic-control systems were associated with greater burnout or psychosocial strain through intensified demands and reduced autonomy. Perceptual exposure, including AI awareness and replacement concerns, produced direct, indirect, and null associations through pathways involving job stress, job insecurity, work–family interference, perceived organizational support, organizational commitment, and resilience. Because most studies were observational, these patterns should be interpreted as associations rather than universal causal effects.

The review identified technostress, exhaustion, job insecurity, work–family interference, work–life balance, organizational support, and autonomy as recurrent pathways. AI-specific dependency, ambiguity, skill degradation, and FoMO emerged as additional potential stress mechanisms. Evidence for a nonlinear AI–burnout relationship remained limited. Wu et al. (2026) reported an inverted U-shaped association for job satisfaction, which was classified as a supporting proxy outcome rather than a direct burnout endpoint. The proposed shock–adjustment–mastery trajectory should therefore be interpreted as a theoretical proposition requiring direct longitudinal testing.

4.2 Theoretical contributions

4.2.1 The AIMS model: AI as a meta-system beyond JD–R

The findings support a reconceptualization of AI as a meta-system that simultaneously reshapes job demands, resources, and control, extending the Job Demands–Resources (JD–R) model. Figure 2 presents the conceptual AI as Meta-System (AIMS) framework, in which AI is theorized to reshape job demands, job resources, and control structures simultaneously. These pathways may be associated with burnout through mechanisms including technostress, exhaustion, job insecurity, and emotional labor, while individual and organizational conditions may modify their strength or direction. The pathways shown in Figure 2 represent theory-derived relationships rather than causal effects established across all included studies.

Figure 2

Empirical evidence illustrates this dual role. Empirical findings are consistent with this dual pattern. Chuang et al. (2025) reported that AI-related technostress was positively associated with emotional exhaustion (γ = 0.33), whereas AI efficacy was negatively associated with exhaustion (γ = −0.16). Similarly, Kim and Lee (2024) identified a significant indirect association between AI adoption and burnout through job stress, while the direct association was not statistically significant. These findings are consistent with multiple psychological pathways linking AI exposure to burnout-related outcomes but do not, by themselves, establish universal causal effects.

The AIMS framework extends JD–R by introducing three key elements: (1) simultaneous influence on demands, resources, and control; (2) non-linear pathways; and (3) dynamic adaptation over time. This shifts the conceptualization of work from task execution to continuous meta-cognitive regulation, where employees must interpret and respond to algorithmic systems.

4.2.2 A role-based typology: assistive, monitoring, and perceptual AI

A central contribution of this review is the introduction of a role-based typology that reconciles conflicting findings in the literature. Distinguishing between assistive, monitoring, and perceptual AI clarifies why prior studies have reported divergent effects.

Assistive AI was generally associated with lower burnout or exhaustion when it reduced documentation, workload, or cognitive demands, particularly in healthcare intervention and implementation studies (Afshar et al., 2025; Baek and Cha, 2025; Chowdhury et al., 2026; Olson et al., 2025; You et al., 2025). Monitoring and algorithmic-control exposures were associated with greater burnout or related psychosocial strain through time pressure, reduced autonomy, surveillance, and intensified control (Dong et al., 2025; Gao et al., 2025; Payá Castiblanque and Pizzi, 2024). Perceptual exposure produced more heterogeneous findings and operated through mechanisms such as job stress, job insecurity, work–family interference, organizational support, and self-efficacy (Kim and Lee, 2024; Zheng and Zhang, 2025; Wang and Zhou, 2025). This typology advances prior research by explicitly linking AI functionality to psychological outcomes, rather than treating AI as a homogeneous construct.

4.2.3 Dynamic and non-linear pathways

The included evidence does not directly establish an inverted U-shaped temporal trajectory between AI exposure and employee burnout. Wu et al. (2026) identified a nonlinear association between AI exposure and job satisfaction, but job satisfaction was treated as a supporting proxy outcome rather than a burnout measure. The proposed shock–adjustment–mastery curve is therefore presented as a conceptual model rather than an empirical estimate. It proposes that initial uncertainty and learning demands may increase strain, while subsequent adaptation, training, system familiarity, and organizational support may reduce strain over time.

AI-specific longitudinal evidence remains limited. Existing multi-wave, randomized, and pre–post studies generally cover relatively short implementation periods and do not test the complete proposed trajectory. Direct examination of within-person burnout before implementation, during adjustment, and after sustained use is therefore required. Figure 3 presents this theory-derived three-phase trajectory as a conceptual illustration requiring direct longitudinal testing.

Figure 3

4.2.4 Mechanisms and moderators

The review identified both established and emerging mechanisms relevant to AI-related burnout. Established pathways included technostress and exhaustion (Chuang et al., 2025), job stress (Kim and Lee, 2024), job insecurity and work interference with family (Zheng and Zhang, 2025), work–life balance (AlOqaily et al., 2025), and perceived organizational support and organizational commitment (Wang and Zhou, 2025). Emerging mechanisms included dependency, skill degradation, regulatory ambiguity, surveillance precarity, and AI-related FoMO (Högemann et al., 2025; Zheng et al., 2025; Méndez-Suárez et al., 2026).

Individual-level boundary conditions included AI self-efficacy, resilience, career resilience, and protean career orientation (Kim and Lee, 2024; Jin et al., 2024; Chung et al., 2025; Kong et al., 2023). Organizational and system-level conditions included perceived organizational support, organizational commitment, organizational culture, implementation quality, and algorithmic transparency (Meduri et al., 2024; Gao et al., 2025; Wang and Zhou, 2025; Lingmont and Alexiou, 2020). Complementary evidence also highlights trust, threat, and protean orientation in employee–AI collaboration and career sustainability (Duong et al., 2026), the role of generative AI in employees’ work-goal progress (Zhang et al., 2026), and the relationship between leadership AI awareness, hindrance/challenge stressors, and employee voice (Zhou and Lyu, 2025).

4.2.5 Research propositions derived from the AIMS framework

The following propositions are theory-derived deductions from the AIMS framework and were not tested in the present systematic review.

P1: AI-driven job demands, including monitoring intensity, cognitive overload, and replacement concerns, are expected to be positively associated with employee burnout through technostress and job insecurity.

P2: AI-driven job resources, including automation of routine work and decision support, are expected to be negatively associated with burnout when they reduce workload and cognitive demands.

P3: AI-driven control mechanisms, including algorithmic monitoring and automated task allocation, are expected to be positively associated with burnout through reduced perceived autonomy.

P4: Professional identity threat is expected to mediate the association between perceived AI replacement risk and emotional exhaustion.

P5: AI self-efficacy is expected to weaken adverse associations between AI-driven demands and burnout.

P6: Perceived organizational support is expected to weaken adverse associations between AI exposure and burnout.

P7: AI-related burnout may follow nonlinear adaptation trajectories over time, with their direction and shape contingent on implementation quality, learning, job redesign, and organizational support.

P8: Algorithmic transparency is expected to weaken adverse associations between AI monitoring and burnout when it is accompanied by explanation, employee voice, and meaningful recourse.

4.3 Practical implications

The findings should be interpreted as design and implementation guidance rather than evidence that one form of AI will produce the same outcome in every organization. Assistive systems may reduce burnout or exhaustion when they demonstrably reduce documentation, workload, and cognitive demands. Organizations should therefore evaluate changes in workload and burnout before and after implementation rather than assuming that automation automatically improves well-being.

Monitoring and algorithmic-control systems require particular caution because observational studies associated these exposures with greater psychosocial strain, time pressure, reduced autonomy, or burnout. Where monitoring is necessary, organizations should provide transparent decision rules, employee voice, appeal or recourse mechanisms, clear accountability, and limits on data collection. These recommendations are also consistent with broader organizational research emphasizing the implications of transparency for privacy, organizational learning, operational control, and motivation (Bernstein, 2012; Brandes and Darai, 2017).

Training, personalization, technical support, and realistic performance expectations may improve implementation outcomes. Organizations should also monitor job insecurity, work–family interference, AI self-efficacy, resilience, and organizational support because these factors may shape employee responses. Burnout imposes substantial organizational costs, but future research should directly quantify whether responsible AI implementation reduces these costs over time.

4.4 Limitations and future research

Several limitations should be considered. First, the evidence base was dominated by observational and cross-sectional studies, limiting causal inference and increasing vulnerability to common-method and self-report bias. Three randomized studies were identified, but they were all conducted in healthcare and primarily examined assistive AI systems. Their findings may not generalize to monitoring, algorithmic-control, or non-healthcare contexts.

Second, direct comparisons of AI-based monitoring and human supervision using validated burnout outcomes were not identified. Gu et al. (2025) compared algorithmic and human supervision but was excluded from the systematic-review sample because it reported performance rather than an eligible psychological outcome. Future studies should compare these supervisory arrangements using longitudinal or experimental designs and validated occupational psychological endpoints.

Third, AI-specific longitudinal evidence remains insufficient to establish a shock–adjustment–mastery trajectory. Future studies should use repeated within-person measurements before implementation, during initial adjustment, and after sustained use. Fourth, cross-national and cross-occupational comparisons remain limited despite likely differences in regulation, labor-market conditions, organizational culture, and employee voice.

Fifth, measurement remains fragmented. Cross-national measurement research further underscores the importance of measurement invariance in burnout assessment (De Beer et al., 2020). Burnout instruments were not always AI-specific, and supporting outcomes such as technostress, job insecurity, job satisfaction, and well-being were operationalized inconsistently. Contextual scale-development studies outside the included AI-specific sample indicate growing interest in technology-related burnout measurement, but no comprehensive instrument currently integrates AI-specific demands, resources, control, identity threat, and adaptation.

Finally, experimental evidence concerning transparency, recourse, organizational support, resilience, and human-versus-algorithmic supervision remains limited. Future research should test these moderators directly rather than inferring their effects from cross-sectional associations.

5 Conclusion

AI was not uniformly associated with either beneficial or adverse burnout outcomes. Across the 20 studies measuring burnout or an established burnout dimension, assistive systems, examined predominantly in healthcare settings, were generally associated with lower burnout or exhaustion, whereas monitoring and algorithmic-control exposures were generally associated with greater burnout in observational studies. Perceptual AI exposures produced positive, indirect, and null associations. The 23 supporting studies clarified potential mechanisms, moderators, and implementation conditions but were not treated as direct burnout evidence. The AIMS framework organizes these findings into theory-derived pathways involving demands, resources, and control. Longitudinal, cross-occupational, and experimental studies are required before causal or temporal conclusions can be established.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

Author contributions

ZF: Investigation, Formal analysis, Writing – review & editing, Methodology. LH: Formal analysis, Investigation, Writing – review & editing. TM: Investigation, Formal analysis, Writing – original draft.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2026.1922281/full#supplementary-material

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Keywords

algorithmic management, artificial intelligence, employee burnout, job demands–resources model, systematic review, technostress

Citation

Fang Z, Han L and Ma T (2026) Artificial intelligence-driven work systems and employee burnout: a systematic review of mechanisms, moderators, and a dynamic meta-system framework. Front. Psychol. 17:1922281. doi: 10.3389/fpsyg.2026.1922281

Received

06 July 2026

Revised

31 July 2026

Accepted

06 August 2026

Published

02 October 2026

Volume

17 - 2026

Edited by

Suryo Wibowo, Krida Wacana Christian University, Indonesia

Updates

Copyright

© 2026 Fang, Han and Ma.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zicheng Fang, 4032530641@smail.lnu.edu.cn; Te Ma, mate987654321@163.com

† These authors share first authorship

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.