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 language | English |
|---|---|
| Article number | 555590 |
| Journal | Open Access Journal of Education & Language Studies |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 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)