University as an Orchestrator of Ecosystemic Learning: Integrating LLM Assistants and Prompt Engineering into Core Disciplines and Assessing Their Impact on the Productivity of Students and Faculty

The article analyzes the transformation of higher education under the influence of generative Artificial Intelligence and proposes a conceptual framework for the university-orchestrator model, which deliberately governs the emergent educational ecosystem. The aim of the study is to construct and theoretically substantiate this model by examining the key activity domains of the university: the incorporation of prompt engineering as a new academic literacy into curricula, the assessment of the impact of large language models (LLM) on the productivity of learners and faculty, and the management of associated risks. The methodological base includes a systematic literature review and a content analysis of industry reports. The results show that the widespread use of LLM assistants (more than 86% of students) has generated a shadow ecosystem that requires universities to shift from reactive measures to proactive orchestration. It is established that LLM tools increase productivity: learning outcomes improve by up to 30%, and faculty save more than two hours per week. At the same time, this effect is mediated by the emergence of new invisible work for verification and editing. In conclusion, it is argued that effective orchestration is a necessary condition for maximizing the positive effects of LLM while simultaneously mitigating technological, pedagogical, and ethical risks. The information presented will be of interest to university administrators, program directors, and researchers studying the societal impact of AI.