Aug 2026· International Journal of Interactive Mobile Technologies (ijim)· Vol 20· 0 citations· 4 references
TL;DR
Based on the results of all evaluations performed, it has been found that AI-based adaptive learning systems provide greater motivation, more successfully comprehend course content, and a higher level of academic performance compared to the use of traditional mobile learning apps.
Abstract
The use of adaptive learning systems with artificial intelligence (AI) in education has changed how educators teach by providing a customized learner experience that matches the particular characteristics of each learner, thus increasing both learner engagement and learning outcomes. This article provides a description of the design, development, and evaluation of a generative AI-based interactive mobile learning system designed to increase learner engagement, increase the ability for the learner to acquire knowledge, and provide improved access to educational resources. By using a mobile-based learning platform, learners have access to real-time tutoring, generated, determined feedback, generated instructional materials, and interactive learning experiences, 24 hours a day, seven days a week. The system architecture consists of user profiling, a learning analytics engine, and a content generation process for creating dynamic course materials using GenAI and providing feedback and making recommendations. These four components of the system work together to create personalized learning paths for individual learners and to enhance the ongoing learning processes of all learners. A thorough assessment of the proposed approach was conducted by utilizing multiple methods with students from a wide variety of educational backgrounds in order to measure the effectiveness of the AI-based adaptive learning system. Students’ performance (effectiveness) was measured using both quantitative metrics and qualitative feedback, including measures of learning success (i.e., test scores), levels of engagement (i.e., how often students engage with the material), completion rate (number of tasks completed vs. total number of tasks assigned), and satisfaction (whether learners felt satisfied with their adaptive learning experience). Based on the results of all evaluations performed, it has been found that AI-based adaptive learning systems provide greater motivation, more successfully comprehend course content, and a higher level of academic performance compared to the use of traditional mobile learning apps.
In modern engineering education, the integration of artificial intelligence (AI) with intelligent control systems (ICS) is creating fundamentally new opportunities to transform the teaching and learning process. These technologies enable the personalization of educational pathways, support the continuous development of students’ digital competence, and contribute to improving overall educational effectiveness. Unlike traditional instructional models that apply uniform teaching strategies, AI-driven systems can dynamically respond to the individual needs, learning pace, and cognitive characteristics of each student, thereby fostering a more student-centered learning environment.This study proposes a novel AI-driven control architecture specifically designed for higher engineering education. The architecture is based on adaptive algorithms capable of analyzing learner behavior, performance data, and interaction patterns in real time. By processing these data streams, the system continuously adjusts instructional content, feedback mechanisms, and task complexity to optimize learning trajectories. Such an approach allows the educational process to function as a closed-loop intelligent control system, where monitoring, analysis, and pedagogical adjustment occur automatically and continuously.To validate the proposed framework, we designed and implemented a working prototype of the system and deployed it within a pilot group of engineering students. The effectiveness of the approach was evaluated using a combination of quantitative and qualitative metrics, including measurable learning gains, levels of student engagement, and self-reported development of digital competence. Comparative analysis with traditional instructional methods demonstrates that the AI-driven control framework significantly improves knowledge acquisition, promotes active participation, and enhances students’ confidence in using digital technologies.
Abdumanonov Axrorjon Adxamjonovich· Journal of Educational Techn...· 0 citations
The adaptive learning system described in this research incorporates several different elements in order to provide each student (learner) with a rich and interactive learning environment. The four components work together to provide the authentication and identification of each learner as well as personalized content delivery and gamification elements (e.g., points, badges, etc.) that drive learners' motivation to participate in the game or learning experience. Over eight weeks, 200 learners evaluated the adaptive learning system and saw a 73% gain in their overall learning; averaged 15 hours of use per student; and achieved approximately 85% mastery on the modules within the system. In addition to these gains, the gamification components also provided added motivation to the learners, and the analytics component indicated areas where learners needed to focus additional effort in order to improve performance. In conclusion, the authors of the paper indicate that the adaptive learning system developed was a success in meeting the goal of providing personalized and effective learning experiences, increasing learner engagement, and continuing to provide motivation for learners to participate in the learning process.
M.Malleswari, N. M, Mohamed Nazurudeen et al.· International Conference Com...· 0 citations
The world of education is evolving and so is the role of Artificial Intelligence (AI) in the education process as we see the introduction of AI as a tool to create personalized learning experiences tailored to meet the needs, abilities and learning preferences of the student. The blended learning may not be able to standardize the traditional classroom learning, may not be able to adapt to the learning speed and learner's cognitive development. Artificial Intelligence (AI) enabled personalized learning systems are using cutting-edge technologies like NLP, machine learning, predictive analytics and intelligent tutoring systems to provide custom learning content, evaluations, and feedback. This research paper delves into the concept of learner-led teaching and learning, as well as the impact of AI on the learning environment. It addresses the opportunities that AI driven platforms can provide for differentiated instruction, student engagement, learning outcomes enhancement, constant assessment, and reporting back to educators about learning gaps. The study also looks at the possibilities of using AI in digital learning environments, in the context of improving collaborative learning, accessibility, and inclusive education. Outside of the educational benefits, the paper also discusses some of the challenges faced by the implementation of AI in educational settings, such as privacy concerns, algorithmic bias, ethical issues, digital injustice, infrastructure and the role of teachers in an AI classroom. The results indicate that the use of personalized AI in learning shows great promise for enhancing the performance of teachers and students if it is integrated into an effective teaching model with adequate policy, teacher training, and ethical oversight. According to the results of this study, in order to create learning environments that are adaptive, equitable and sustainable in the 21st century, the balance between technology and human-based learning practices is crucial to the successful use of AI in education.
Dr. Daisy. R, Dr. K. Sudhakar· Scriptora International Jour...· 0 citations
Artificial Intelligence (AI) is emerging as a disruptive technology in digital education by enabling personalized learning systems that adapt to individual learners’ needs. Traditional digital learning platforms mostly rely on standardized content delivery, which often fails to accommodate differences in learners’ prior knowledge, cognitive abilities, learning pace, motivation, and preferences. This limitation can lead to disengagement and ineffective learning. AI-based personalized learning systems address these challenges by integrating technologies such as machine learning, data analytics, natural language processing, and intelligent decision-making algorithms. These systems collect and analyze learner data—including behavioral patterns, assessment results, interaction history, and contextual information—to provide customized content, feedback, assessments, and learning paths in real time. The proposed framework follows a closed-loop learning approach where continuous monitoring of learners helps refine instructional strategies. Key AI techniques used include supervised and unsupervised learning, reinforcement learning, knowledge tracing, and recommendation systems. A review of existing intelligent tutoring systems, adaptive learning platforms, and learning analytics models highlights their strengths, limitations, and future potential. The paper also proposes a modular AI architecture consisting of learner profiling, content modeling, adaptive decision engines, and feedback mechanisms. Mathematical formulations for learner modeling and personalization optimization provide theoretical support. Simulated case studies demonstrate that AI-driven personalization significantly improves learner engagement, retention, and assessment performance compared to traditional e-learning systems. However, challenges such as data privacy, algorithmic bias, scalability, and system interpretability remain important concerns. The study concludes that AI-personalized learning systems will play a crucial role in the future of education and emphasizes the need for further research on ethical, explainable, and human-centered AI in digital learning.
Karen Lewis, Richard Evans· International Journal of Mod...· 0 citations
The study examined Artificial Intelligence and its impact on the learning experience of students in Nnamdi Azikiwe University, Awka. Artificial Intelligence has witnessed major developments such as intelligent tutoring systems, automated feedback, virtual assistants, and personalized learning platform which have led to major changes in the process of teaching and learning within the domain of education. In this study, it was intended to evaluate the extent to which Artificial Intelligence impacts students’ engagement, academic performance, access to learning materials and learning experience generally. The qualitative research method was used, and qualitative data were analyzed by reviewing related literature. The results show that artificial intelligence increases learning experience through personalized learning experience, increased accessibility of academic resources, provision of immediate feedback, and allowing learners to learn at their own pace. Besides, the use of AI systems increases learners' motivation and engagement through creation of interactive and flexible learning environment. However, some issues such as lack of technical facilities, data privacy issues, lack of digital literacy, and over-reliance on AI technologies might affect effective implementation. In summary, artificial intelligence is an important technology that increases learners' experience and can be used for innovative learning experience at Nnamdi Azikiwe University, Awka.
Unknown authors· International journal of res...· 0 citations
Artificial Intelligence (AI) has become a disruptive force in the current field of education, which allows one to create intelligent e-learning and provide a learner with personalized, adaptive, and learner-centric educational experience. Conventional e-learning communication models mostly embrace one fit model and neglect the individual learner in terms of cognitive abilities, learning pace, background knowledge, motivation, as well as learning preferences. This disadvantage results in in optimal interaction, high turnover, and learning imbalances. To overcome these difficulties, AI-based e-learning systems are used, which is based on machine learning algorithms, learning analytics, natural language processing, recommendations systems, and relevant learning contents, assessments, and feedback is delivered to particular learners in real time. The following paper gives a detailed research of AI-based e-learning websites in personalized learning. It explores the theoretical basis of personalized learning, the history of intelligent tutoring systems, and how AI methods like supervised learning, reinforcement learning, deep learning, and knowledge tracing are applied in adaptive learning. The paper also examines the architectures, data streams, learner control strategies and evolutional decision making systems that support modern AI-driven learning systems. Some of the most essential issues regarding data privacy, bias in algorithms, interpretability, scalability, and morality are evaluated crucially. There is a comprehensive methodology that is proposed and includes system design, dataset preparation, feature engineering, personalization algorithms, and evaluation metrics. The results of the experiment are discussed based on the comparative performance analysis, indicators of the learner engagement, and the improvements in the learning outcomes. The results show that AI-based personalization benefits lead to substantial satisfaction with the learners, retention of knowledge, and academic results as opposed to conventional e-learning sites. The paper also ends by emphasizing subsequent research interests, which include explainable AI in learning, multimodal learning analytics, and lifelong personalized learning ecosystems.
Isabella A, N. R.· International Journal of Art...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.