The integration of artificial intelligence (AI) into education has the potential to revolutionize how students engage in academic activities and tasks. This research empirically analyses the influence of AI on creative ideation within educational settings to validate AI's role in enhancing human creativity since creative tasks, which inherently rely on human intuition, emotion and divergent thinking, may be stifled by systematic AI tools. The study explores whether ChatGPT can aid the creative process or inadvertently limit human creativity with a mixed‐method approach consisting of a randomized controlled experiment with third‐year engineering students in which a total of 728 ideas were obtained, along with a structured survey. The results revealed a predominantly positive perception towards AI‐assisted ideation; nevertheless, concerns were raised about AI's influence on creativity and innovation. While no significant differences in ideation outcomes were observed between the groups that used AI and those that did not, significant differences emerged between students who had prior experience with ChatGPT and those who did not. Qualitative insights provided a nuanced view of student experiences on blended human–AI ideation processes, shedding light on its advantages and disadvantages in educational practices. This research also underscores critical considerations and potential risks associated with the adoption of AI, suggesting that while AI has a place in educational settings, its role should be carefully calibrated to support rather than stifle student creativity and innovation. From the findings, the study provides practical recommendations and best practices regarding the integration of AI tools in education.

This study explores the potential of RGB image data for forest fire detection using deep learning models, evaluating their advantages and limitations, and discussing potential integration within a multi-modal data context. The research introduces a uniquely comprehensive wildfire dataset, capturing a broad array of environmental conditions, forest types, geographical regions, and confounding elements, aiming to reduce high false alarm rates in fire detection systems. To ensure integrity, only public domain images were included, and a detailed description of the dataset’s attributes, URL sources, and image resolutions is provided. The study also introduces a novel multi-task learning approach, integrating multi-class confounding elements within the framework. A pioneering strategy in the field of forest fire detection, this method aims to enhance the model’s discriminatory ability and decrease false positives. When tested against the wildfire dataset, the multi-task learning approach demonstrated significantly superior performance in key metrics and lower false alarm rates compared to traditional binary classification methods. This emphasizes the effectiveness of the proposed methodology and the potential to address confounding elements. Recognizing the need for practical solutions, the study stresses the importance of future work to increase the representativeness of training and testing datasets. The evolving and publicly available wildfire dataset is anticipated to inspire innovative solutions, marking a substantial contribution to the field.

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

Early prediction of academic performance is vital for reducing attrition in online higher education. However, existing models often lack comprehensive data integration and comparison with state-of-the-art techniques. This study, which involved 2,225 engineering students at a public university in Ecuador, addressed these gaps. The objective was to develop a robust predictive framework by integrating Moodle interactions, academic history, and demographic data using SMOTE for class balancing. The methodology involved a comparative evaluation of seven base learners, including traditional algorithms, Random Forest, and gradient boosting ensembles (XGBoost, LightGBM), and a final stacking model, all validated using a 5-fold stratified cross-validation. While the LightGBM model emerged as the best-performing base model (Area Under the Curve (AUC) = 0.953, F1 = 0.950), the stacking ensemble (AUC = 0.835) did not offer a significant performance improvement and showed considerable instability. SHAP analysis confirmed that early grades were the most influential predictors across top models. The final model demonstrated strong fairness across gender, ethnicity, and socioeconomic status (consistency = 0.907). These findings enable institutions to identify at-risk students using state-of-the-art interpretable and fair models. These findings enable institutions to identify at-risk students using state-of-the-art, interpretable, and fair models, advancing learning analytics by validating key success predictors against contemporary benchmarks.