AI-driven analysis of teacher perspectives on neoliberal educational policies in preschool education

Yifan Cai ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 1452-1459

Abstract: Neoliberal education policies have transformed preschool education through the implementation of standardized testing, accountability, and formalized resource allocation. Although these policies are intended to improve the quality of education, issues remain regarding heightened workload demands, reduced pedagogical freedom, and institutional inequalities. This research utilizes an artificial intelligence-based method, namely autoencoders, to examine teachers' views on these policy reforms by extracting underlying sentiment patterns from large-scale text data. Extensive sensitivity analysis was conducted to optimize key hyperparameters, including learning rate, batch size, latent dimension size, dropout, and the number of network layers. The final model was validated against performance metrics such as Mean Squared Error (MSE), Reconstruction Loss, Classification Accuracy, F1-Score, Silhouette Score, Precision, Recall, and AUC. Findings indicate that workload pressure was perceived as the most negatively impacting factor, with excessive administrative overload curtailing teaching flexibility. In contrast, policies on resource allocation were perceived differently, where well-funded schools gained and underfunded schools faced compliance issues. Longitudinal sentiment analysis also indicates that veteran teachers are more resilient to neoliberal policies, while newer teachers are more flexible. Differences at the institutional level were apparent, with higher-end schools registering more uniform sentiment scores and low-end institutions experiencing increased dissatisfaction and burnout. Such findings support that an adaptive, teacher-centric policy design, with a balance between accountability and pedagogical control, is warranted. The suggested AI-driven sentiment analysis approach in this study introduces a new framework for policy evaluation, advocating for equal resource allocation, reduced bureaucratic burden, and increased teacher involvement in policymaking to create an effective and sustainable preschool education sector.

Keywords: Autoencoders; Sentiment Analysis; Deep Learning; Workload Pressure; Educational Reform; Neoliberal Educational Policies; Pedagogical Autonomy; Preschool Education; Artificial Intelligence. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:2:p:1452-1459:id:5474

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