Evaluation of Policy Making in Russell Group Universities Employing AI-driven NLP Method

    Research output: Contribution to journalArticlepeer-review

    55 Downloads (Pure)

    Abstract

    This study explores how Russell Group universities can evaluate their policymaking and strategic decisions by employing AI-driven Natural Language Processing (NLP) methods. Through a large-scale case study based on social media data during the COVID-19 pandemic, the research assesses how university policies-particularly those related to governance, crisis management, and higher education-impact institutional reputation and stakeholder engagement. By leveraging computational social science and machine learning algorithms to detect patterns in public sentiment and stakeholder behavior, the study demonstrates how AI can enhance policy and decision-making within the higher education sector. Additionally, the study sheds light on AI’s role in promoting transparency, accountability, and effective reputation management, positioning the Russell Group as a key player in shaping the future of global academia.
    Original languageEnglish
    Article number555590
    Journal Open Access Journal of Education & Language Studies
    Volume2
    Issue number3
    DOIs
    Publication statusPublished - 18 Oct 2024

    Keywords

    • policymaking
    • reputation management
    • stakeholder engagement
    • higher education
    • computational social science
    • decision-making
    • crisis management
    • Russell Group
    • NLP
    • AI
    • Machine learning (ML)

    Fingerprint

    Dive into the research topics of 'Evaluation of Policy Making in Russell Group Universities Employing AI-driven NLP Method'. Together they form a unique fingerprint.

    Cite this