Abstract

Artificial Intelligence (AI) is rapidly transforming the landscape of research and impact management, offering opportunities to streamline how research impact is created, captured, and communicated. From identifying trends and emerging fields of inquiry to automating the evaluation of research outcomes, AI has the potential to streamline impact-related processes such as planning and assessment. However, with these advancements come significant ethical responsibilities. AI adoption must be cautiously approached, ensuring that its application supports sustainability, equity, and transparency.
This presentation will outline AI's good, bad, and ugly aspects related to its integration into the research impact lifecycle. It will examine tools and AI processes across three key areas: (1) Creating Research Impact, where AI tools can assist in identifying novel research directions, fostering interdisciplinary collaborations, and generating innovative solutions to global challenges; (2) Capturing Research Impact, where AI can enhance the accuracy and efficiency of impact measurement by analysing large datasets, tracking societal and policy influences, and uncovering hidden patterns in research uptake; and (3) Communicating Research Impact, where AI-driven tools, such as natural language processing (NLP) and automated reporting, can help tailor messages for diverse stakeholders, ensuring that impact narratives are accessible, inclusive, and potentially free from bias (or are they?).
The presentation will touch on the ethical challenges of using AI, including data privacy issues, algorithmic bias, and the need for human oversight. We will provide a framework for research managers to adopt AI responsibly, ensuring its benefits are realised without compromising the integrity and inclusivity of the research ecosystem. Ultimately, this session aims to contribute to the development of a sustainable, AI-enhanced future for research impact management that is aligned with broader societal goals.