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51 articles for “machine learning in education”
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance
Abstract: Artificial intelligence (AI), machine learning (ML), and big-data methods are increasingly used to improve educational decision making through personalization, early-warning systems, scalable feedback, and operational analytics. This manuscript proposes a practical end-to-end framework for educational AI/ML projects, covering problem definition, data engineering, modeling, evaluation, intervention design, and responsible governance. To provide a complete and reproducible template without exposing sensitive student data, we present a synthetic demonstration study that mirrors typical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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Evaluating AI-Driven Adaptive Learning Models in Mathematics: A Contemporary Perspective
Abstract: Artificial Intelligence (AI) continues to transform mathematics education through data-driven personalization and adaptive learning technologies. This study investigates how AI-enabled adaptive platforms influence student performance and engagement in mathematics classrooms. Using a quantitative approach across two institutions, pre- and post-assessment results were compared between students using AI-assisted adaptive learning tools and those receiving conventional instruction. The findings reveal that AI-driven learners demonstrated significantly higher gains in conceptual understanding and engagement …
Published in Recent Trends in Mathematics · Vol. 3, Issue 1, 2026 · pp. 8–12 Read article
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AI and ML-Driven Immersive Technologies: A New Era in Education
Abstract: The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 116–123 Read article
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A Study on the Role of Artificial Intelligence in Indian Higher Education System
Abstract: In the present era, Artificial Intelligence (AI) has been transforming learning, teaching, and engaging, by making them more personalized and efficient in every field. In Indian Higher Education system, the application of Artificial Intelligence (AI) technology is improving learning techniques, by natural language processing, machine leaning etc. by students for analyzing data, using algorithms, discovering patterns and making the predictions for outcomes; and instructors may make lessons for each student …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 08–12 Read article
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FlexEdify: Adaptive Learning Platform
Abstract: FlexEdify is an innovative adaptive learning platform revolutionizing the educational experience by customizing content to meet the unique needs of each student. Utilizing state-of-the-art machine learning algorithms, the system promptly analyzes students' real-time errors, meticulously storing them in a centralized database. This database serves as the cornerstone for adaptive questioning, allowing the platform to dynamically adjust subsequent content based on individual weaknesses. The platform features a user-friendly interface, guaranteeing accessibility …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 2, 2024 · pp. 20–24 Read article
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Machine Learning Based Sentiment Analysis of Student Feedback in Higher Education
Abstract: Educational institutions routinely collect feedback from students to understand their perceptions of academic programs, infrastructure, and campus facilities, to improve the overall quality of the college environment. In current practice, feedback is often gathered using numerical or grade-based rating systems, which tend to oversimplify student opinions and may overlook important details related to their level of satisfaction. In contrast, open-ended textual feedback allows students to clearly express their views, concerns, …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 01–10 Read article
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Artificial Intelligence and the Future of Education
Abstract: Artificial Intelligence (AI) refers to the capability of a computer system to mimic human intelligence by performing tasks such as learning, reasoning, problem-solving, and decision-making. As a rapidly evolving and emerging technology, AI holds the potential to transform a wide range of sectors, including healthcare, transportation, business, and notably, education. Its application in the educational domain is gaining momentum, aiming to create more efficient, engaging, and personalized learning environments. Incorporating …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 39–44 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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Integrated Qualitative Response Assessment System
Abstract: Examinations for universities and yearboards are traditionally administered offline, with a significant number of students opting for subjective exams. This preference stems from the labor-intensive nature of evaluating subjective responses, which requires considerable time and effort from educators. Additionally, subjective grading can be influenced by the evaluator’s mood, leading to inconsistencies. In contrast, multiple-choice and objective questions are prevalent in entrance and competitive exams due to their ease of automated …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 18–24 Read article
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Charting the Path Forward: An In-Depth Analysis of Breakthroughs and Hurdles in Artificial Intelligence
Abstract: Recent years have witnessed tremendous progress in artificial intelligence (AI), fueled by exponential increases in processing power and data accessibility. These developments have made it possible for AI to be widely used in a variety of industries, such as healthcare, finance, autonomous driving, and more. Significant difficulties are presented by the "black-box" nature of many AI systems, which lack transparency and the capacity to explain. By encouraging algorithms that can …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 1, 2025 · pp. 13–23 Read article
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ConnectED-AI: Connecting Education with Artificial Intelligence
Abstract: In a rapidly evolving educational landscape, the role of technology and artificial intelligence (AI) in enhancing learning experiences has gained immense importance. The “ConnectED-AI” project represents a pioneering effort to leverage AI to empower students with personalized and effective educational resources. This report provides an overview of the “ConnectED-AI” project, which is designed to address the diverse and dynamic learning needs of students. Our project aims to connect students with …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 36–50 Read article
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Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review
Abstract: Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 36–48 Read article
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Enhanced Diabetes Prediction: A Comparative Study of Machine Learning Models
Abstract: Excessively high blood glucose levels lead to diabetes, a condition that can be better managed with early detection, resulting in a longer life and improved health. Machine learning models are essential tools in diagnosing diabetes, especially when trained on appropriate and relevant datasets. In this study, a combination of ensemble methods and nine distinct machine learning algorithms were utilized to develop a predictive model for diabetes diagnosis based on a …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 2, 2025 · pp. 1–10 Read article
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AI-Driven Handwriting Identification and Verification Using Textural Features
Abstract: The last few decades have seen handwriting recognition and verification earn their mark in areas like forensics, healthcare, education, and digital security. This study delves into the role of artificial intelligence (AI), machine learning (ML), and deep learning techniques in handwriting analysis. It highlights the extraction of textural features as a precursor to identifying narrows between original handwriting and its forgery, whereby a few distinctive patterns such as stroke width, …
Published in International Journal of Electronics Automation · Vol. 3, Issue 1, 2025 · pp. 35–44 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis
Abstract: The proliferation of artificial intelligence (AI) and machine learning (ML) technologies has transformed the digital education landscape by enabling adaptive e-learning systems capable of personalizing content and optimizing learning paths. This study provides a systematic analysis of adaptive e-learning algorithms within the framework of heutagogy, an educational paradigm that emphasizes learner autonomy, self-direction, and capability development. The convergence of adaptive technologies with heutagogical principles offers new avenues for creating more …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 33–38 Read article