Publications of Emma Graupner

While AI is transforming professional practice and education, evidence indicates a persistent gender gap in AI use. Integrating gender theory with the Unified Theory of Acceptance and Use of Technology (UTAUT), we examine gender differences in predictors of AI use and performance outcomes in a global sample of business students. Results show that women report lower AI use than men. Performance expectancy predicts use for both genders but more strongly for men. Women’s use is additionally shaped by effort expectancy and negatively affected by social influence. Higher AI use was associated with increased performance among men but not women, indicating a gender difference in the relationship between AI use and performance. Targeted interventions that build AI literacy, self-efficacy, and emphasize voluntary engagement may promote more equitable AI adoption and performance outcomes.

Artificial Intelligence (AI), and Large Language Models (LLM) in particular, can advance global learning by enabling cross-cultural collaboration through translation, inclusive participation, enhanced communication, and feedback. Yet benefits vary with language proficiency and cultural biases in LLM design and outputs. Non-native speakers of English often face higher cognitive load and lower output quality in low-resource languages, while AI translation tools often miss cultural nuances and reflect U.S.-centred norms. In a survey of 502 students worldwide, language proficiency and nationality predicted LLM use frequency. Our main study will test how English language proficiency and cultural background influence LLM use frequency, perceived effectiveness of LLM, and perceived learning outcomes in international contexts

Artificial Intelligence (AI) agents introduce new challenges and opportunities across team processes. This study examines the impact of AI on transition, action, and interpersonal phases, and explores team processes central to human-AI collaboration. We surveyed 632 global virtual team members and interviewed nine AI experts. Survey results reveal that AI is seen as most useful during transition phases, less in action, and least in interpersonal phases. Usefulness ratings declined over time across all phases, especially interpersonal, indicating unmet expectations. Experts valued AI in action phases but expressed concerns about losing control during transition phases. Extending an established team processes model, we identify six processes and practices that foster effective human-AI collaboration: AI introduction, expectation management, positive storytelling, change management, navigating role shifts, and social interaction. Our findings highlight the need for targeted strategies to support these processes and manage perceptions of AI in teams.

This paper explores the integration of Artificial Intelligence (AI) into workplace teams and its influence on interpersonal dynamics. Drawing on Marks et al.'s (2001) team processes framework, we examine how AI influences interpersonal processes like conflict management, motivation, and affect management. Findings suggest that while AI can enhance objectivity, motivation, and efficiency, it may also introduce new tensions and anxieties about job security. We critically analyze both the opportunities and risks associated with AI integration, including the role of trust and individual perceptions. To address gaps in existing research, we outline a qualitative research agenda exploring the nature and distribution of interpersonal processes in human-AI teams. Preliminary interviews reveal that AI has strong motivational potential, though emotional responses vary, with some team members expressing fears and differing adaptation speeds.