ChatUniversity educators traditionally identified struggling students only after midterm exams were failed; predictive machine learning models analyze continuous digital engagement to trigger early proactive student counseling.
College student dropout rates represent a major crisis in higher education, resulting in wasted tuition, diminished economic mobility, and institutional attrition.
Academic advisors historically lacked real-time visibility into student performance, relying on sporadic exam grades that revealed learning failures too late in the semester to rescue student GPA.
This study develops an explainable machine learning predictive model trained on multi-dimensional learning management system (LMS) interaction metrics, forum participation, and quiz submission regularity across university cohorts.
Deploying predictive early-warning systems allows academic institutions to deliver targeted tutoring and psychological support weeks before midterm assessments, significantly boosting course retention and graduation rates.
Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance
This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values < 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode.
Smart city initiatives frequently imported glossy foreign technological blueprints that failed in regional municipal governments; empirical ICT auditing maps local technological capabilities to design realistic e-government roadmaps.
Architectural floor planning for elder-care facilities required weeks of manual drafting to balance accessibility with regulatory safety codes; Conditional Generative Adversarial Networks (cGANs) autonomously generate optimized retirement home layouts.
Online digital classrooms were accused of isolating learners and degrading human social skills; mining learning management system interactions reveals that big data analytics can actively track and foster student collaborative social competencies.
Modern workforces demand complex, multi-dimensional problem-solving skills that traditional standardized tests fail to quantify; structural equation modeling validates the eComplexity instrument to measure real-world complex thinking competencies.
Mathematics students frequently faced uniform lecture pacing that alienated struggling learners and bored advanced pupils; intelligent tutoring systems leverage predictive diagnostic algorithms to deliver personalized mathematical instruction.
Generative AI swept into university lecture halls with disruptive speed, leaving academic governance struggling to adapt; bibliometric analysis maps global patterns of AI adoption and pedagogical transformation across higher education.
Emerging AI educational tools are predominantly engineered for high-bandwidth, resource-rich institutions; this empirical analysis explores the opportunities and infrastructural bottlenecks of deploying AI learning platforms within Cuban schools.
Artificial intelligence effortlessly solves historical substitution ciphers; modern military-grade encryption remains completely immune to large language model attacks. By evaluating top commercial models on an extensive cryptanalysis benchmark, cybersecurity researchers proved that modern mathematical encryption cannot be cracked by pattern-matching AI.
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