Mini Review

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

Homa Molavi*

The University of Manchester, Oxford Road, Manchester, UK

Submission:October 02, 2024; Published: October 18, 2024

*Corresponding author:Homa Molavi, The University of Manchester, Oxford Road, Manchester, UK

How to cite this article:Homa M. Evaluation of Policy Making in Russell Group Universities Employing AI-driven NLP Method. Open Access J Educ & Lang Stud. 2024; 2(3): 555590. DOI:10.19080/OAJELS.2024.02.555590.

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.

Keywords:Russell Group; AI; NLP; policymaking; reputation management; stakeholder engagement; higher education; computational social science; decision-making; crisis management

Health Psychology

In recent decades, there has been a significant transformation in the organizational structures governing universities. The traditional notion of the university as a republic of scholars has given a way to the emerging concept of the university as a stakeholder organization [1]. Universities need to fulfill the roles of teaching and research [2] as two inseparable components in higher education (HE) [3] by satisfying their stakeholders’ expectations [4-8].

The landscape of the education sector starkly reveals a disconnect between performance, reputation scores, and evaluations, and the real-time data tracking systems in place. This misalignment presents considerable challenges, particularly as policies must remain agile to effectively respond to evolving circumstances. The dynamic nature of policymaking necessitates frequent adjustments to accommodate shifting dynamics. In contemporary society, decision-making processes increasingly rely on big data rather than traditional empirical assessments and surveys [9,10]. Researchers emphasize the limitations inherent in traditional methods such as surveys or focus groups, citing prevalent issues like recall bias and question framing bias [11]. Furthermore, the constraints posed by limited historical data can result in a reliance on intuition-driven decisions when historical context is lacking [12]. Such reliance on intuition introduces biases that may undermine the accuracy and reliability of the collected data. This underscores the critical need for the adoption of more nuanced and unbiased approaches in both research methodologies and data sources.

Furthermore, stakeholders exhibit diverse behaviors in response to reputation-related stimuli during crises, resulting in varied individual outcomes due to socio-cognitive processes [13]. This variability complicates the prediction of stakeholder behaviors, particularly when dealing with terabytes of data generated by social interactions during crises. Unlike traditional approaches relying on historical data, the absence of such data significantly limits decision-making adaptability, as past experiences cannot be extrapolated to the unprecedented circumstances [12]. Consequently, the missing aspect is understanding how stakeholders react to the actions and responses of organizations and how organizations should make their policies and strategic plans considering stakeholders’ reactions?

Case Study

To provide some context for my notion, let’s examine how data-driven policy and decision-making present additional challenges for the Russell Group and its stakeholders in light of the COVID-19 pandemic. The goal of the 24 elite members of the Russell Group is to unite in order to better influence policy decisions as a collective. At the local, national, and international levels, Russell Group universities have a significant impact on society, the economy, and culture. Together, they contribute more than two-thirds of the world-class research produced by UK universities, advancing a variety of fields [14].

Utilizing social media dataset of Russell Group universities’ posts as a response to the pandemic, there are a total of 17,507 posts for the whole group. Among these, 13,119 tweets received no comments or interactions, representing 74.10% of the total. This percentage is detailed in Table 1 and illustrated in Figure 1. These preliminary statistics inherently expose the behavioral patterns of stakeholders and their interactions with crisis response efforts at Russell Group universities. The findings suggest a neutral pattern in stakeholder behavior, as indicated by the high percentage of tweets without any comments.

Conclusion

This study demonstrates the significant potential of AIdriven Natural Language Processing (NLP) methods to enhance policymaking within Russell Group universities. Our findings reveal a disconnect between the 17,507 social media posts analyzed and the low engagement levels, with 74.10% of tweets receiving no comments or interactions [15-20]. This indicates a neutral stakeholder response, highlighting the need for universities to refine their communications strategies to better align with stakeholder interests. The research underscores the importance of adopting data-driven approaches in policymaking, especially during crises where traditional methods may be inadequate. By utilizing AI and machine learning, universities can identify patterns in public sentiment and stakeholder behavior, facilitating timely and informed policy responses [21- 30]. This not only promotes transparency and accountability but also positions Russell Group universities as proactive leaders in higher education. As universities navigate crisis management and stakeholder engagement complexities, integrating AI methodologies will be crucial for adaptive policymaking. By embracing these technologies, Russell Group universities can enhance their policy influence and resilience in an evolving academic landscape [31,32].

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