AI-Assisted Learning and Dropout Risk in Romanian Secondary Education: A Logistic Regression Approach to Predictive Risk Assessment

Authors

• Adrian NICOLAU

Abstract

Student dropout remains a persistent challenge in Romanian secondary education, yet most interventions operate reactively rather than through systematic risk assessment. This study investigates whether exposure to AI-assisted learning tools is associated with lower academic vulnerability among secondary school students, and whether a compact logistic regression model can function as a predictive risk scoring instrument applicable in educational management contexts. Using survey data from 1,702 respondents (680 students, 873 parents, 149 teachers), the study constructs a binary academic vulnerability variable from self-reported learning difficulties across core subjects, used as a proxy for dropout risk, and estimates a regularised logistic regression with three predictors: AI tool usage, gender, and grade level. Results indicate that respondents engaged with AI-assisted learning are approximately 95% less likely to report academic vulnerability (OR = 0.051, 95% CI: 0.002, 0.652), though this estimate is sensitive to limited variance in AI usage across the sample. Female respondents exhibit moderately higher stated risk (OR = 3.576, p < 0.001), while higher grade levels are associated with marginally increased vulnerability (OR = 1.224, p < 0.01). The model achieves high discriminatory power (AUC = 0.992) and classification accuracy exceeding 95% at the conventional threshold, though the near- universal AI usage rate (99.4%) likely inflates the AUC. These findings reposition AI not merely as a pedagogical tool but as a component of institutional risk management, offering educational leaders a framework for early identification and targeted support of at-risk students.

Keywords: educational risk assessment; dropout prediction; artificial intelligence; logistic regression; educational management; secondary education

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APA: Adrian NICOLAU (2026). AI-Assisted Learning and Dropout Risk in Romanian Secondary Education: A Logistic Regression Approach to Predictive Risk Assessment. Internal Auditing & Risk Management, Vol. 21, pp. 1-18. https://doi.org/10.5281/zenodo.19352827

BibTeX:

@article{adriannicolau2026536,
  title = {AI-Assisted Learning and Dropout Risk in Romanian Secondary Education: A Logistic Regression Approach to Predictive Risk Assessment},
  author = {Adrian NICOLAU},
  journal = {Internal Auditing & Risk Management},
  year = {2026},
  volume = {21},
  pages = {1--18},
  doi = {10.5281/zenodo.19352827}
}

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