Abstract: Artificial intelligence (AI) is becoming a new source of help in the workplace, reshaping how employees solve work-related problems and maintain relationships with coworkers. Prior research on workplace help-seeking has mainly examined interpersonal exchanges between help seekers and human help providers. However, when employees can also seek help from AI, help-seeking becomes a triadic process involving help seekers, AI, and coworkers. This research addresses how employees strategically coordinate help-seeking from AI and help-seeking from coworkers, and proposes a theoretical framework of employees' interpersonally oriented AI-directed help-seeking strategies.
The framework distinguishes between two strategies that reflect different interpersonal orientations. The interpersonal promotion-oriented AI-directed help-seeking strategy refers to integrating help-seeking from AI with help-seeking from coworkers to pursue interpersonal gains. Employees using this strategy may first turn to AI to clarify problems, generate preliminary solutions, organize questions, or explore alternative ideas, and then approach coworkers for experience-based judgment, feedback, discussion, and contextualized support. In this strategy, AI does not replace coworkers; instead, it helps employees improve the quality of subsequent interpersonal help-seeking and create more valuable coworker interactions. By contrast, the interpersonal prevention-oriented AI-directed help-seeking strategy refers to using help-seeking from AI to replace or reduce help-seeking from coworkers to avoid interpersonal risks. Employees using this strategy may rely on AI to solve problems while reducing direct exposure to coworkers, thereby avoiding displays of incompetence, possible rejection, or negative social evaluation. This typology clarifies that AI-directed help-seeking is not merely a matter of human-AI interaction. It may either strengthen or weaken interpersonal engagement depending on whether employees use AI to complement or substitute for help from coworkers.
This research further explains how these two strategies emerge. Drawing on social cognitive theory, we propose that employees' AI capability and team cooperative climate jointly shape their strategy choices. AI capability gives employees the ability and confidence to incorporate AI into problem solving, but its interpersonal implications depend on how employees perceive coworkers within their team context. When the team cooperative climate is strong, coworkers are more likely to be viewed as partners for resource integration. Under this condition, employees with higher AI capability are more likely to develop a cooperative motivational state and use AI to enhance coworker interaction. When the team cooperative climate is weak, coworkers are more likely to be viewed as sources of evaluative pressure, potential resource loss, or competitive threat. Under this condition, employees with higher AI capability are more likely to develop a competitive motivational state and reduce interpersonal exposure through AI.
This research then specifies the consequences of the two strategies for help seekers. Based on conservation of resources theory, we identify two mechanisms: actors' contextualized tacit knowledge acquisition and actors' momentary social-evaluative pressure. The interpersonal promotion-oriented strategy may increase contextualized tacit knowledge acquisition because it preserves coworker interaction and enables employees to access task-specific experience, team routines, organizational norms, and interpersonal knowledge that are difficult to obtain from AI alone. At the same time, it may increase momentary social-evaluative pressure because employees need to present problems, acknowledge limitations, and receive coworker feedback. Conversely, the interpersonal prevention-oriented strategy may reduce contextualized tacit knowledge acquisition by limiting coworker interaction, but it may also reduce momentary social-evaluative pressure by lowering direct interpersonal exposure. These two mechanisms explain why both strategies may have ambivalent effects on employees' daily work engagement and subsequent job performance.
Finally, the research examines coworker responses. Drawing on sociometer theory, it argues that coworkers may infer employees' AI-directed help-seeking strategies from observable interaction cues, such as whether employees involve them in problem solving, seek their advice after using AI, bypass collaboration routines, or rely on AI outputs in team discussions. When employees use an interpersonal promotion-oriented strategy, coworkers may perceive that their competence, experience, and relational value remain important in the AI era, which enhances perceived competence and warmth evaluations and promotes social support. When employees use an interpersonal prevention-oriented strategy, coworkers may perceive that their expertise is being devalued or that their relational value is being discounted, which may reduce these evaluations and increase social undermining.
Overall, this research extends workplace help-seeking research by showing how AI changes the relationship between help-seeking from AI and help-seeking from coworkers. It contributes to research on workplace help-seeking, human-AI interaction, and interpersonal dynamics in organizations, and offers practical implications for guiding employees to use AI while maintaining constructive coworker relationships.

Key words: help-seeking behavior, artificial intelligence, interpersonal promotion-oriented AI-directed help- seeking strategy, interpersonal prevention-oriented AI-directed help-seeking strategy