Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM’s black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS (LLM Agency Theory-Led Adoption and Alignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using the organizational LLM adoption phases and the agency theory as concepts. Our approach results in (1) providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and (2) providing a first LLM alignment problem-solutionspace.

The introduction of generative artificial intelligence (GAI), exemplified by ChatGPT, has sparked discussions in higher education about its implications. While GAI offers new ways of acquiring knowledge, concerns about plagiarism and changes in the role of educators have emerged. Using semi-structured interviews, we explore faculty perceptions of GAI and its impact on faculty professional identity. Findings reveal perceived opportunities for efficient knowledge acquisition and risks such as data quality and loss of personal development. Educators' roles are evolving toward critical guardianship, emphasizing the importance of guiding students in information acquisition and evaluation. Our study contributes to the understanding of the transformative effects of GAI in higher education and highlights the evolving professional identity of educators.