The rapid adoption of artificial intelligence (AI) in critical care nursing is transforming clinical workflows, enhancing diagnostic accuracy, and supporting complex decision-making processes. Despite these technological advancements, the ethical ramifications for nurses remain underexplored, particularly in terms of moral integrity and moral distress. Moral integrity, defined as adherence to personal and professional ethical principles, is critical in ICU settings where decisions directly impact patient outcomes (Wei et al., 2025). Moral distress occurs when nurses are constrained from acting in accordance with their ethical beliefs, potentially compromising patient safety and their own professional well-being (James et al., 2024). Understanding how AI integration influences these ethical dimensions is crucial for the development of interventions that preserve nurses' moral integrity in technologically advanced healthcare environments.

This study draws on Lazarus and Folkman's Cognitive Appraisal Theory and Rest's Four-Component Model of Moral Behavior to explicate the mechanism through which moral distress undermines nurses' moral integrity and to clarify how AI integration may intervene in this process. According to Cognitive Appraisal Theory, nurses are confronted with ethically challenging situations; first engage in primary appraisal, in which such situations are evaluated as threats to professional values or patient safety, followed by secondary appraisal, in which nurses assess their perceived control, available resources, and coping options (Lazarus & Folkman, 1984). Moral distress arises when nurses appraise ethical demands as exceeding their perceived capacity to act in accordance with their moral judgment. AI integration may modify this appraisal process by reducing clinical uncertainty, enhancing access to evidence-based recommendations, and increasing perceived decisional control, thereby attenuating threat appraisals and strengthening coping appraisals.

Within Rest's Four-Component Model, AI integration can be theoretically situated across all moral components. First, AI-supported clinical decision systems may enhance moral sensitivity by improving situational awareness, early risk detection, and recognition of ethically salient clinical cues. Second, AI tools that provide transparent, evidence-based recommendations may support moral judgment by assisting nurses in evaluating alternative courses of action and their potential consequences. Third, by reducing cognitive overload and decisional ambiguity, AI integration may strengthen moral motivation, enabling nurses to prioritize ethical values over competing organizational or workload pressures. Finally, AI-supported documentation and decision-tracking may reinforce moral character by facilitating consistent ethical action, persistence in morally appropriate behavior, and reduced moral residue over time. Through these mechanisms, AI integration may buffer the erosive effects of moral distress on nurses' moral integrity by supporting ethically aligned appraisal, judgment, and action (Rest, 1986). See Fig. 1. Proposed researchers' framework.

Moral distress is increasingly recognized as a pervasive psychological phenomenon in nursing, particularly in high-acuity environments where nurses face ethically complex situations and limited decision-making power. It occurs when nurses recognize the ethically appropriate action but are unable to act due to institutional, hierarchical, or resource-related barriers, leading to feelings of frustration, guilt, and powerlessness (Aljabery et al., 2024). In critical care settings, nurses frequently confront ethically fraught decisions involving life-sustaining interventions, end-of-life care, and conflicting patient-family-provider expectations, all of which heighten their vulnerability to moral distress (Palmryd et al., 2025). Over time, repeated exposure to these dilemmas can erode nurses' emotional resilience, contribute to burnout, and compromise their sense of professional and ethical identity (Shuai et al., 2024).

Artificial intelligence (AI) integration in nursing practice has emerged as a transformative force in clinical decision-making, workflow optimization, and patient monitoring. In this study, AI integration is conceptually defined as the extent to which AI-enabled systems are meaningfully embedded into nurses' clinical workflows and decision-making processes, including their accessibility, routine use, perceived usefulness, and alignment with nursing practice. This encompasses AI-supported decision-support algorithms, predictive analytics, and automated monitoring tools that assist nurses in interpreting clinical data, managing uncertainty, and prioritizing patient care activities. Such systems have been shown to enhance clinical accuracy, reduce cognitive workload, and facilitate the rapid interpretation of complex patient data in critical care environments (Bajwa et al., 2021; Porcellato et al., 2025; Vitorino et al., 2025). Accordingly, effective AI integration may mitigate ethical stressors by reducing clinical ambiguity, supporting ethically informed decision-making, and enhancing nurses' confidence when managing morally complex clinical situations. Within this framework, AI integration is theorized to function as a mediating mechanism that shapes how nurses cognitively appraise and respond to ethically challenging clinical contexts.

Moral integrity is a core ethical attribute reflecting an individual's commitment to moral values and the consistency between actions and principles (Mohi Ud Din & Zhang, 2023). In nursing, it underpins professional identity and ethical practice, shaping nurses' ability to uphold standards, advocate for patients, and engage meaningfully in ethical decision-making (Ne'eman-Haviv et al., 2025). Preserving moral integrity in high-stakes settings requires moral courage and ethical sensitivity, and is associated with greater job satisfaction, psychological well-being, and a stronger sense of professional purpose (Rushton et al., 2017). Conversely, moral distress, institutional constraints, and repeated ethical conflicts can erode moral integrity, contributing to emotional exhaustion, moral disengagement, and reduced care quality (He et al., 2025). AI technologies may help protect moral integrity by reducing uncertainty, supporting ethical decision-making, and enhancing nurses' ability to act in alignment with their values. Understanding how moral distress, AI integration, and moral integrity interact is key to strengthening ethical resilience in critical care environments.

Moral distress is widely recognized as a growing threat to nurses' psychological well-being and ethical functioning, particularly in critical care settings. Recent evidence shows that over 60% of critical care nurses experience moderate to high moral distress, contributing to burnout, turnover intentions, and reduced quality of care (Cerela-Boltunova et al., 2025). Prolonged exposure to ethical constraints can erode nurses' moral integrity, leading to moral disengagement and diminished patient advocacy (He et al., 2025). These trends highlight an urgent need to identify mechanisms that can mitigate the harmful effects of moral distress on ethical practice.

Artificial intelligence (AI) integration offers a promising pathway for supporting nurses facing complex ethical decisions. AI-enabled decision support tools have demonstrated significant improvements in diagnostic accuracy and reductions in cognitive workload, helping clinicians navigate uncertainty more effectively (Bajwa et al., 2021; Porcellato et al., 2025; Vitorino et al., 2025). By enhancing clarity and supporting ethically aligned decisions, AI may reduce the psychological strain associated with morally challenging situations, thereby protecting nurses' moral integrity. Examining AI integration as a mediator is therefore essential to understanding how technological innovations can strengthen ethical resilience and promote safer, ethically grounded nursing practice.