AI-Enabled Platforms for Collaborative Knowledge Generation in Research Ecosystems

Abstract

With the rise of Artificial Intelligence, research activities may be conducted in a more traditional way automated,
smart and scalable mechanisms enable the generation knowledge. This paper focuses on construction and
implementation of AI powered tools that help in the collaborative knowledge creation in complex research ecosystems.
The goal is to study what influence do such platforms have on interdisciplinary collaboration, innovation from data,
and real time monitoring activity of researchers. The method involves multi-phase system design using semantic
knowledge graphs, natural language processing, and machine learning recommendation engines to perform intelligent
content curation and researcher matchmaking. Evaluation of the AI system is performed by measuring its predefined
Ke performance indicators against available benchmarks from institutional networks of collaborative research-across
frameworks. It was discovered that AI powered platforms enhance the efficiency of collaboration, reduce duplication
of effort in surveying literature, and increase the relevancy of collaborative research outcomes. The research has shown
that AI technology integration into research ecosystems changes the ecosystem towards more dynamic, distributed,
and democratized knowledge generation. The study compares AI powered platforms with traditional platforms and
analyzes their output quality, completion time, and user satisfaction. The importance of this study rests on the
possibility of informing policies, institutional frameworks, and even technological decisions regarding investments in
research infrastructure by analyzing the impact of AI on knowledge ecosystems paradigms.