OPTIMAL GENERATIVE AI MODELS FOR SMART SCHOOL LEARNING SYSTEMS

ABSTRACT

Modern smart school systems increasingly depend on advanced, data-driven AI models that integrate multiple educational data sources, including student performance metrics, learning platforms, and digital resources. Generative AI demonstrates significant potential in personalizing learning paths, creating adaptive content, and supporting assessment, yet a key challenge remains: identifying the most effective models that combine accuracy, efficiency, and interpretability. This study investigates various generative AI architectures and their applications in enhancing personalized learning within smart school environments. We analyze transformer-based models, variational autoencoders, and hybrid frameworks to determine their suitability for optimizing student engagement and learning outcomes.

Our findings show that models such as GPT-based architectures, T5, and diffusion models can efficiently generate tailored learning materials, adaptively track progress, and suggest individualized educational pathways. While transformer-based models excel in versatility and predictive performance, hybrid solutions provide a balance between effectiveness and interpretability for educators. Case studies in subjects like mathematics and programming illustrate that implementing optimal generative AI models improves learning outcomes while supporting adaptive, student-centered teaching approaches. These results suggest that smart schools can effectively integrate generative AI without compromising transparency or pedagogical quality.

АННОТАЦИЯ

Системы умных школ всё чаще опираются на интеллектуальные модели, работающие с большими массивами образовательных данных, включая показатели успеваемости учеников, платформы дистанционного обучения и мультимедийные ресурсы. Генеративный искусственный интеллект демонстрирует высокую эффективность в персонализации учебных траекторий, создании адаптивного контента и оценке знаний, однако важной задачей остаётся выбор оптимальных моделей, сочетающих точность, эффективность и интерпретируемость. В работе рассматриваются различные архитектуры генеративного ИИ — трансформеры, вариационные автокодировщики и гибридные подходы — и их применение для повышения эффективности образовательного процесса.

Результаты показывают, что модели на основе GPT, T5 и диффузионные модели могут создавать индивидуализированные учебные материалы, адаптивно отслеживать прогресс учеников и предлагать персонализированные образовательные маршруты. Трансформерные модели обеспечивают высокую точность и универсальность, а гибридные подходы создают баланс между эффективностью и удобством интерпретации для преподавателей. Примеры применения в математике и программировании демонстрируют, что использование оптимальных генеративных моделей ИИ способствует улучшению результатов обучения и поддержке адаптивного, ориентированного на ученика подхода.

Keywords: Generative Artificial Intelligence, Smart School, Personalized Learning, Adaptive Learning, Transformers, Variational Autoencoders, Educational Technology.

Ключевые слова: генеративный искусственный интеллект, умная школа, персонализированное обучение, адаптивное обучение, трансформеры, вариационные автокодировщики, образовательные технологии.

Introduction

Intelligent data-driven management in smart cities depends on integrating heterogeneous urban data—IoT sensors, geospatial streams, mobility traces, and citizen feedback—into models that can forecast events and recommend actions across traffic, energy, safety, and environment. While advanced machine learning (e.g., gradient boosting, deep learning) delivers strong predictive performance in these domains, decision transparency remains a critical barrier for municipal adoption, public trust, and regulatory alignment. Explainable AI (XAI) addresses this by revealing feature influences, local decision rationales, and model uncertainty, enabling stakeholders to understand, audit, and contest automated recommendations [1].

Modern smart school environments increasingly incorporate advanced analytical approaches that process diverse educational information, such as academic achievement data, digital learning platforms, electronic учебные материалы, and interactive multimedia content. The availability of these heterogeneous data sources makes it possible to design intelligent systems that support individualized instruction, automatically generate learning materials, and adapt assessment mechanisms to the needs of learners with different abilities and backgrounds. Despite the rapid progress of generative artificial intelligence, including transformer models, variational autoencoders, and diffusion-based approaches, educational institutions still face the challenge of identifying models that ensure not only high performance but also pedagogical relevance and operational efficiency [2].

Recent research emphasizes the growing role of generative models in automating instructional content development, providing personalized learning recommendations, and assisting teachers in lesson planning and evaluation processes. At the same time, educational datasets differ significantly from traditional technical datasets due to variations in learning behavior, incomplete or inconsistent student records, and continuous changes in learner progress over time. In this context, AI-generated outputs must be interpretable and educationally meaningful, as smart school systems are expected to support teachers and students rather than replace pedagogical decision-making [3]. Ensuring consistency with curriculum standards and learning objectives remains a critical requirement for the adoption of generative AI in education.

This paper presents a structured approach to analyzing and selecting suitable generative AI models for deployment in smart school learning systems. The proposed methodology evaluates different model architectures based on their adaptability to educational tasks, computational efficiency, and ease of use for educators. Transformer-based models, variational autoencoders, and hybrid generative solutions are examined using real educational scenarios, with a particular focus on personalized content generation and adaptive learning support. Both quantitative performance indicators and qualitative factors, including interpretability and practical usability, are considered in the evaluation. The results demonstrate that appropriately selected generative AI models can enhance learning outcomes while maintaining transparency, educational quality, and a learner-centered approach.

Figure 1. Generative AI Models: Smart School Performance

Figure 1 shows the performance comparison of four generative AI model architectures in smart school learning systems. Transformer-based models achieve the highest scores, reflecting strong adaptability to educational tasks and personalized content generation. Hybrid solutions also perform well, balancing efficiency and usability. Variational autoencoders and diffusion models demonstrate moderate results, highlighting challenges in interpretability and alignment with curriculum standards. The comparison emphasizes the need to select models that combine technical accuracy with pedagogical relevance [3].

To address these limitations, this research focuses on real-world educational data obtained from learning management systems and student assessment records. Model-agnostic evaluation techniques are applied to ensure an objective comparison of generative AI models across different learning scenarios. By prioritizing both educational effectiveness and practical applicability, the proposed approach contributes to the development of intelligent learning systems that are reliable, adaptive, and consistent with pedagogical and ethical standards of contemporary smart schools.

Methodology

This section presents the methodological framework used to design, implement, and evaluate generative AI models for smart school learning environments. The central aim is to identify architectures that achieve a balance between computational performance, interpretability, and pedagogical relevance. Since educational datasets are heterogeneous, often incomplete, and subject to continuous changes in learner progress, the methodology emphasizes both technical rigor and practical applicability for teachers and students.

A. Dataset

For the study, we rely on publicly available learning analytics and student performance datasets, which include indicators such as exam scores, digital platform activity, access to electronic materials, and adaptive assessment outcomes. Each record represents a student profile with quantitative and categorical attributes, along with aggregated indices of learning achievement. The diversity of these features allows us to evaluate how different generative models adapt to real-world educational contexts [4].

The dataset ResearchInformation3.csv contains 493 student records with 16 variables that capture both academic and socio-demographic characteristics. Key features include department affiliation, gender, high school (HSC) and secondary school (SSC) scores, family income levels, hometown, computer usage, study preparation time, gaming habits, attendance, and employment status. Additional variables such as English proficiency, extracurricular participation, semester level, and cumulative grade point averages (Last and Overall) provide a comprehensive view of student performance. This heterogeneous structure makes the dataset suitable for analyzing how personal background, engagement patterns, and academic indicators collectively influence learning outcomes in smart school environments [4].

Figure 2. Sample Data Structure

Figure 2 illustrates the sample educational data structure. Each row represents an individual student, with features such as department, gender, high school (HSC) and secondary school (SSC) scores, family income, and hometown. Additional variables include computer usage, study preparation time, gaming habits, attendance, and employment status. The dataset also contains indicators of English proficiency, extracurricular participation, semester level, and cumulative grade point averages (Last and Overall), which together provide a composite measure of student performance in smart school environments [8].

B. Data Preprocessing

Data preprocessing is a crucial step in preparing the ResearchInformation3 dataset before building generative AI models for smart school learning systems. In this study, missing or inconsistent values are addressed through imputation techniques: numerical indicators such as HSC, SSC, Last, and Overall scores are replaced by median values, while categorical attributes (e.g., Gender, Income, Attendance) are filled with the most frequent category. Since the dataset contains variables measured on different scales—ranging from exam scores to categorical descriptors—normalization is applied to ensure comparability across dimensions and to prevent bias during model training [5].

 Figure 3. Smart School Student Performance Comparison

Figure 3 illustrates the trend of average student performance across semesters based on the ResearchInformation3 dataset. The line graph shows how the cumulative Overall score evolves as students progress through different academic stages. Each point on the curve represents the mean performance for a given semester, highlighting variations in achievement and potential shifts in learning outcomes over time [6]. This visualization provides a clear perspective on the dynamics of student success, allowing educators and researchers to identify patterns of improvement or decline and to evaluate the effectiveness of smart school learning systems.

In addition, categorical features such as Department, Hometown, Preparation, and Gaming are encoded numerically using label encoding. Each unique category (e.g., “Village” vs. “City” for Hometown, or “Low”, “Middle”, “High” for Income levels) is assigned an integer value, allowing machine learning algorithms to process the data effectively [7]. This transformation ensures that all features are compatible with training procedures, while avoiding the introduction of artificial ordinal relationships where none exist.

These preprocessing steps guarantee that the dataset is consistent, complete, and suitable for subsequent modeling and explainability analysis in the context of smart school environments.

C. Feature Extraction

Feature extraction plays a critical role in determining which student-related attributes contribute most significantly to predictive and generative learning models in smart school environments. Given the heterogeneous nature of educational data combining academic performance indicators, behavioral patterns, and socio-demographic characteristics it is essential to identify the most informative features while minimizing redundancy and noise [8].

In this study, Random Forest feature importance is employed as a primary mechanism for estimating the contribution of individual variables to model predictions. Random Forests provide an ensemble-based, model-agnostic approach that evaluates feature relevance by measuring the average decrease in impurity across decision trees. This method is particularly suitable for educational datasets, as it captures non-linear relationships between student behavior, background characteristics, and learning outcomes [9].

To further ensure robustness and interpretability, a Pearson correlation matrix is computed to analyze interdependence among features. Highly correlated variables such as closely related academic scores or overlapping engagement indicators are examined to prevent multicollinearity effects that could distort generative model outputs [10]. The correlation heatmap serves as a diagnostic tool, revealing latent relationships between academic achievement, attendance, preparation time, and digital engagement.

Additionally, SHAP (SHapley Additive exPlanations) values are applied to provide fine-grained interpretability at both global and individual student levels. SHAP analysis enables quantification of how each feature positively or negatively influences predicted learning outcomes, making the results more transparent and pedagogically meaningful for educators.

Figure 4.  Correlation Heatmap

Figure 4 presents the correlation heatmap derived from the ResearchInformation3 dataset, illustrating the relationships between key student attributes, including attendance, preparation time, computer usage, gaming habits, and cumulative academic performance indicators (Last and Overall scores). Strong positive correlations are observed between preparation level and academic performance, as well as between attendance and overall achievement. Moderate associations are also identified between computer usage and performance, suggesting the relevance of digital literacy in smart school learning systems. Conversely, gaming frequency shows weak or negative correlations with academic outcomes, highlighting potential behavioral risk factors [11].

Figure 5.  Feature Importance with Learning Performance

Figure 5 summarizes the feature importance results obtained from the Random Forest model alongside their correlation with the overall learning performance index [12]. The analysis identifies Attendance, Preparation Level, and Overall GPA as the most influential predictors, with Attendance emerging as the dominant feature. This finding indicates that consistent engagement in learning activities remains the primary driver of student success, even in technology-enhanced smart school environments.

Socio-demographic factors such as family income and hometown exhibit moderate influence, suggesting that contextual background still plays a role in shaping learning outcomes. Digital behavior indicators, including computer usage, demonstrate secondary but non-negligible importance, reinforcing the need for balanced integration of digital tools in personalized learning systems.

Figure 6.  Radar Chart of Student Attributes by Department

Figure 6 presents an integrated visualization framework that combines feature importance rankings, correlation analysis, and comparative performance distributions across student groups. These visual diagnostics collectively provide a multidimensional perspective on learning success factors in smart school environments. By linking behavioral, academic, and contextual variables, the system enables educators to identify critical intervention points and tailor instructional strategies accordingly [13].

The visualization framework demonstrates that effective smart school learning is not achieved through isolated technological enhancements alone. Instead, it emerges from a synergistic interaction between student engagement, preparation habits, and adaptive digital support mechanisms. Attendance and preparation act as central catalysts, amplifying the effectiveness of generative AI-driven personalization [14].

In conclusion, the proposed feature extraction methodology establishes a transparent and interpretable analytical pipeline that transforms raw educational data into actionable insights. By integrating correlation analysis, Random Forest feature importance, and SHAP-based explainability, this approach moves beyond simple prediction to uncover the underlying drivers of student performance.

The results confirm that student engagement and learning discipline are the core determinants of success in smart school learning systems, while generative AI models serve as enablers that adapt content and feedback to these fundamental factors. This alignment with Explainable AI principles ensures that generative recommendations remain understandable, trustworthy, and pedagogically grounded—supporting informed decision-making by teachers and school administrators [15].

D. Model Training and Implementation

To evaluate the effectiveness of machine learning approaches for predicting student learning outcomes in smart school environments, five supervised learning models were trained and compared: Linear Regression, Decision Tree, Random Forest, Gradient Boosting (XGBoost), and Neural Networks (MLP). These models represent a combination of linear, tree-based, ensemble, and deep learning approaches, offering a comprehensive perspective on predictive accuracy, robustness, and interpretability in educational analytics.

The target variable for prediction is the Overall academic performance score, while the input features include attendance, preparation level, digital engagement indicators, socio-demographic attributes, and prior academic records extracted during the feature engineering stage. All models were trained using identical preprocessed datasets to ensure fair comparison [16].

To enhance generalization performance, 5-fold cross-validation was applied. Model evaluation relied on the coefficient of determination (R²) and Mean Absolute Error (MAE), as these metrics provide complementary insights into predictive accuracy and deviation from actual student outcomes [17].

Figure 7. Performance Comparison of Regression Models

Figure 7 compares the models using 5‑fold cross‑validated R² and Mean Absolute Error (MAE). Gradient Boosting and Random Forest achieve the highest predictive accuracy (R² > 0.90), reflecting their ability to capture non-linear interactions and feature interdependencies [19]. Logistic Regression provides a baseline for linear explainability, while SVR and MLP demonstrate competitive but less consistent performance due to sensitivity to scaling and hyperparameters [18].

To optimize the final model, hyperparameter tuning is performed via RandomizedSearchCV. For XGBoost, Figure 8 illustrates the parameter space exploration, highlighting the impact of n_estimators, max_depth, learning_rate, and subsample on predictive performance. This tuning process maximizes R² on a held-out validation set, balancing computational efficiency with accuracy gains [20].

Figure 8. 3D Parameter Space Analysis for XGBoost

Results

The performance of the trained models was assessed using a comprehensive set of evaluation metrics designed to measure both predictive accuracy and classification quality in identifying student learning outcomes within smart school systems. For evaluation purposes, student academic achievement was discretized into higher-performing and lower-performing categories based on the Overall performance indicator.

To account for potential class imbalance and to ensure robust evaluation, Precision, Recall, and F1-Score were employed. Precision reflects the proportion of correctly classified high-achieving students among all students predicted as high-performing, while Recall measures the model’s ability to correctly identify students who truly belong to the high-performance group [19]. The F1-Score serves as a harmonic mean of Precision and Recall, providing a balanced assessment of classification effectiveness. The combined use of accurate predictive models and explainable analytical methods strengthens the foundation for adaptive learning, early intervention, and personalized educational support within smart school environments.

Table 1.

Model performance metrics for Smart City Index prediction

As shown in Table 1, the Random Forest model demonstrates the strongest overall performance among the evaluated approaches, achieving the highest values across all key metrics. Its superior ROC-AUC score indicates a robust ability to distinguish between students with differing academic achievement levels, while the elevated Precision and Recall values highlight its reliability in identifying both high-performing and at-risk learners.

Overall, these findings suggest that ensemble learning techniques, particularly Random Forest, are well suited for predictive tasks in smart school learning systems where accuracy and robustness are critical. At the same time, simpler and more interpretable models such as Logistic Regression complement these approaches by enhancing transparency and supporting pedagogically informed decision-making.

Conclusion

This research demonstrates that the effective application of artificial intelligence in smart school learning systems depends not solely on maximizing predictive accuracy, but on achieving a meaningful balance between model performance, transparency, and pedagogical usability. Advanced machine learning and generative models—such as ensemble methods and neural architectures—show strong capabilities in modeling complex educational data and predicting student learning outcomes. However, their internal decision-making processes are often opaque, which limits their direct adoption in educational practice.

To address this challenge, the study integrates explainability mechanisms, including feature importance analysis and SHAP-based interpretations, allowing complex models to remain understandable to educators and academic administrators. This approach enables teachers to trace AI-generated predictions and recommendations back to concrete student attributes, such as attendance, preparation habits, and academic history, thereby supporting informed instructional decisions rather than replacing human judgment.

A further constraint identified in this work is the predominantly static nature of the available educational data. The absence of explicit temporal information restricts the ability of models to capture dynamic learning processes, such as gradual performance improvement, disengagement trends, or shifts in student behavior over time. To mitigate this limitation, the study highlights the importance of advanced feature engineering techniques, including the construction of progression-based indicators, historical performance aggregates, and behavior-driven proxies that approximate temporal learning dynamics.

Overall, the proposed methodology establishes a foundation for reliable, interpretable, and ethically aligned AI-driven learning systems. By combining robust predictive modeling with transparent analytical tools, smart school platforms can leverage generative AI to enhance personalization, support early academic interventions, and improve learning outcomes while maintaining trust, accountability, and alignment with educational standards. This balance between technical sophistication and pedagogical responsibility is essential for the sustainable integration of artificial intelligence into future smart education ecosystems.

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