Pa ge 1 Pa ge 12 7 American Journal of Environmental Economics (AJEE) Enhancing Personalized Learning through Artificial Intelligence in Modern Education Systems H M Atif Wafik1, Sheikh Khurshid Alam Prince2, Avishek Reza Promise3, Muhammad Aminur Rahman1, Jafrin Jahan2, Shuvo Kumar Mallik4* Volume 4 Issue 1, Year 2025 ISSN: 2833-7905 (Online) DOI: https://doi.org/10.54536/ajee.v4i1.5441 https://journals.e-palli.com/home/index.php/ajee Article Information ABSTRACT Received: May 27, 2025 Accepted: June 30, 2025 Published: July 24, 2025 AI is transforming current education systems by providing personalized learning experiences and adjusting the pace of instruction, content delivery, and learning pace according to a student’s needs. This study investigates the personalization of education through AI in the context of current educational settings, primarily higher education. A systematic search of 41 academic databases identified 17,899 records, and after strict inclusion criteria, we included 45 studies. We followed the PRISMA approach to guarantee methodological transparency in selection, data extraction, and synthesis. The studies included were assessed using a standard bias instrument. The results suggest that the impact of AI-enabled solutions on adaptive learning, student engagement, and administrative efficiency is substantial. Helping to personalize learning, AI fosters better learning outcomes and increased student satisfaction. Yet, there are challenges to AI integration, including ethical issues, data privacy concerns, and the need for robust teacher training and institutional support. This review discusses the transformative potential of AI for education and makes calls for standards to evaluate the efficacy of AI methods, greater collaboration across disciplines, and long-term studies to ensure the fairness and effectiveness of AI implementations. These findings are crucial for educators, policymakers, and institutional leaders seeking to transform and sustain future- ready education systems in an era of AI. Keywords Artificial Intelligence, Education Systems, Learning Experiences 1 Department of Business Administration, University of Scholars, Dhaka, Bangladesh 2 Department of English, Reverie School, Dhaka, Bangladesh 3 Controller of Examination, University of Scholars, Dhaka, Bangladesh 4 Department of Economics, Southeast University, Dhaka, Bangladesh * Corresponding author’s e-mail: nextgenresearch.info@gmail.com INTRODUCTION Artificial intelligence (AI) is a game changer in the modern education system, promising to open up new pathways to personalized learning (Tapalova & Zhiyenbayeva, 2022). By being able to handle vast amounts of data and respond to the unique learning profiles of each student, AI is revolutionizing how we teach, particularly by personalizing instruction to accommodate differences in students’ learning rates, content, and teaching methods. AI-enabled personalized learning seeks to create more learner-centered experiences that foster increased engagement, motivation, and academic achievement (Zhao, 2025). Among the milestones of AI is the creation of adaptive learning spaces. These systems tailor instructional materials in real-time as they interact with students based on their performance, preferences, and learning needs (Strielkowski et al., 2025). For instance, intelligent tutoring systems can assess student interactions to provide immediate feedback or to infer the student’s knowledge level and serve as an appropriate learning resource. Educational technologies, such as machine learning- based learning management systems, track and analyze learner behavior, enabling instructors to make data-driven choices that improve their teaching and support students as effectively as possible. And AI enables personalized education at scale (Kaswan et al., 2024). AI tools equipped with machine learning techniques and natural language processing can scale to accommodate a diverse range of learners, including those with special needs, to provide customized access to support, translations, and alternative formats. These facets not only facilitate access to learning but also help promote equity in mixed classrooms and address the geographical divide (Assefa et al., 2025). However, the adoption of AI technologies in education has its limitations. This high-tech world raises ethical questions about the privacy of data, the transparency of algorithms, and the risks of bias in AI systems. Their personal and academic data are frequently collected and analyzed by artificial intelligence tools, raising important questions of consent, ownership, and security (Menard & Bott, 2025). It also creates a device that allows access to the internet. Still, the digital divide remains a limiting factor for access, particularly in areas where institutions or regions lack adequate digital infrastructure. There is another important role that instructors play in AI-supported learning environments (Cohn et al., 2025). Artificial intelligence can enhance the education process, but it cannot substitute for the human touch that goes with teaching empathy, working with students, and speaking to them sensitively. Hence, the successful integration of AI would involve adequate teacher training and professional development to prepare teachers to apply AI tools in meaningful and ethical ways. The impact of AI on personalized learning hinges on the extent of its incorporation within pedagogical models rather than Pa ge 12 8 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 its technological adoption (Vorobyeva et al., 2025). In this paper, the role of AI in personalized learning in higher education is discussed, with the opportunities presented by AI highlighted and its challenges acknowledged. Universities and colleges are turning to AI for advanced learning to drive innovation, enhance learning opportunities, and prepare students for the jobs of the future (Mohamed Saad, 2025). Nevertheless, the literature is lacking in terms of a systematic investigation into the real-world impact of AI on personalized learning outcomes, especially in various diverse educational settings (Spaho et al., 2025). To fill this gap, the current paper attempts to consolidate results from the academic literature to investigate the efficacy, ethical issues, and implementation challenges of AI (Mohammadi & Maghsoudi, 2025). Through the use of a rigorous and transparent methodology, this review aims to provide practical recommendations for instructors, policymakers, and administrators who seek to leverage AI to enhance teaching and learning. LITERATURE REVIEW Artificial Intelligence (AI)- enabled educational systems have created a new learning paradigm in today’s era of rapid change in education (Strielkowski et al., 2025). AI and technology are transforming education to serve the personal, developmental, and learning styles of every student. This shift is evident in the development of adaptive learning systems, intelligent tutoring systems, and AI-based learning analytics (Strielkowski et al., 2025). AI-supported personalized learning involves tailoring learning speed, content, and teaching methods to individual learners (Abrar et al., 2025). AI-based systems can also analyze students’ strengths, weaknesses, and progress in real time and tailor the delivery of content. Such systems promote learner autonomy and motivation by offering appropriate learning materials and feedback in a timely manner, which may result in improved academic achievement and student satisfaction. AI algorithms help personalize the learning path through an adaptive learning environment (Strielkowski et al., 2025). Such platforms adapt learning paths based on the student’s interactions with the platform, leading to a more adaptable and responsive form of education. Therefore, students are automatically supported as they grapple with a concept and accelerate when they achieve mastery (Bøe et al., 2025). Besides content customization, AI-powered tools encourage student interaction through features such as virtual tutors, chatbots, and personalized learning dashboards. Speech recognition can deliver immediate responses, reminders, and performance data, enabling students to stay focused on their learning experience. AI systems also support teachers by providing detailed analytics to inform teaching decisions and pinpoint students who may require extra help (Koukaras et al., 2025). Despite these benefits, multiple barriers hinder the successful incorporation of AI in education. One of the biggest concerns is the ethical use of student data. AI-powered systems require extensive data collection, so problems of privacy, consent, and data security must be addressed to safeguard the rights of learners (Dhinakaran et al., 2025). There is also a question of the transparency and fairness of AI algorithms; data bias may result in unequal learning outcomes. Another barrier is the digital divide. The availability of AI tools is certainly not consistent across schools, particularly in underprivileged or rural areas (Khazanchi et al., 2025). Enabling AI-boosted learning for all students is another matter that requires massive investments in infrastructure and technology. Teacher preparedness is also key to successful implementation. However, the majority of these teachers do not have the skills or confidence to incorporate AI tools into their pedagogical methods (Filiz et al., 2025). Ultimately, without sound professional development and resourcing around AI in education, AI in education may have never truly realized its full potential. A recent technology revolutionizing large classrooms in higher education is AI. AI provides this support by automating administrative tasks, making student performance data accessible to instructors, and enabling the creation of more personalized course content (Al Nabhani et al., 2025). Yet, the broader impact of AI on performance and patterns in higher education is still being investigated. MATERIALS AND METHODS This paper follows a systematic literature review methodology to examine the use of artificial intelligence (AI) in personalized learning within contemporary education, with a particular focus on higher education. For methodological rigor, the review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which aim to promote transparency and reproducibility in research. This review aimed to identify and compile empirical evidence on the use of AI in the field of personalized (adaptive) learning or teaching. The review process was conducted using the evidence-based research method applied in systematic reviews. An organized procedure was employed to minimize bias in study identification and ensure uniform data collection and analysis. In February 2025, an extensive search was conducted in several academic databases, including Google Scholar, Web of Science, Scopus, ERIC, and PubMed. Search terms were constructed by combining string terms pertinent to our research, including artificial intelligence, personalized learning, higher education adaptive learning,” and AI in education through Boolean connectors. Search strings were adapted for each database to achieve the most effective retrieval of the literature. To enhance the credibility of this systematic review and prevent duplication, it will be registered in PROSPERO, an international database of systematic review protocols. During the search, filters were set up to select only peer- reviewed, English-language empirical studies conducted within a particular period. Pa ge 12 9 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 Eligibility Criteria The inclusion and exclusion criteria were designed to maintain focus on the research objective. Studies were included if they met the following conditions: • Published between January 2023 and 2025. • Focused on the use of AI in personalized learning within higher education institutions. • Peer-reviewed and written in English. • Based on empirical evidence involving primary data collection or experimental analysis. Information Sources and Search Strategy We conducted a comprehensive search using a prespecified strategy based on combinations of the following keywords, employing Boolean logic, to identify eligible studies. A search query for each database was customized according to its indexing system. The search strings were composed of AI, personalization, and education, with modifications to optimize the matching of the words to the most practical education. The titles and abstracts of the articles obtained from the primary search were screened, and the full texts were reviewed according to the eligibility criteria. Duplicates were removed, and the retrieved studies were evaluated based on methodological quality and relevance. The last set of studies was reviewed and analyzed, and the main themes of implementation effectiveness and challenges of AI in providing personalized learning were identified. This systematic and transparent approach to reviewing the literature enables a comprehensive examination of how AI is impacting personalized learning within contemporary educational systems, providing valuable information for educators, researchers, and policymakers. Table 1: Literature Review Sources: Search Databases, Search Strings, and Number of Results Database Search String Results PubMed (artificial intelligence OR AI) AND (personalized learning OR adaptive learning) AND (education OR teaching” OR students) AND (higher education) 10 Scopus TITLE-ABS-KEY (artificial intelligence OR AI) AND TITLE-ABS-KEY (personalized learning OR adaptive learning) AND TITLE-ABS-KEY (education” OR teaching) AND TITLE-ABS-KEY (higher education) 328 Web of Science TS = (artificial intelligence OR AI) AND TS = (personalized learning OR adaptive learning) AND TS = (education OR “teaching OR students) AND TS = (“higher education”) 107 ERIC (artificial intelligence OR AI) AND (personalized learning OR adaptive learning) AND (education OR teaching) AND (higher education) 54 Google Scholar (artificial intelligence OR AI) AND (personalized learning OR adaptive learning) AND (education” OR teaching) AND (higher education) 17,400 Total 17,899 Study Selection Process A systematic search for research projects fostering personalization and artificial intelligence applied to the modern personalized education system was carried out. This procedure initially yielded 17,899 articles from multiple databases. Reference management tools were used to remove the duplicates. After de-duplication, a systematic screening was processed. Review of titles and abstracts of the search results were based on predetermined inclusion criteria reflecting AI-driven personalization in education. A full-text assessment of the articles that seemed epidemiologically relevant was conducted to determine eligibility. Data Extraction To ensure consistency in data handling, a structured extraction form was developed. This form captured essential information from each study, including: • Author(s) and publication year • Description of the AI intervention or personalized learning approach • Reported outcomes and conclusions related to learner performance and engagement • Any noted limitations or strengths in the study’s methodology • Assessment of bias in reporting and analysis Quality Assessment Reported bias was assessed by critically appraising the quality of the studies included. For conformity to the standard practice of systematic review methodology, a graphic rating method was used to evaluate the quality of included studies. It was scored on the following five key dimensions of AI in personalized education: • The theoretical foundation of the AI tool • Access to and adequacy of IT resources • Type and context of learning space • Pedagogical interventions involving AI personalization • To the theme, you have now aligned the converted and personalized Table 2 Pa ge 13 0 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 Data Synthesis The narrative synthesis method was utilized to synthesize and present findings in an organized and holistic way, taking into account anticipated heterogeneity in the study, AI, and personalized learning intervention design. Unique patterns and trends in the AI-driven personalized learning approaches were identified using a thematic analysis. Primary points of interest were adaptive learning algorithms, student engagement, and AI tools’ pedagogical efficacy. The data have been analyzed in NVivo through a prescribed coding framework focusing on core elements such as AI-based recommendation systems, intelligent tutoring systems, and real-time learning analytics. This approach to analysis facilitated an interrogation of how the key themes recurred and what that signified in terms of the quality of personalization within higher education. The characteristics and main results of the studies included were also summarized by descriptive statistics. Tables and figures were used to help organize and show these data in appealing and comprehensible ways. The common goal of the data syntheses was to provide an informed overview of the current research landscape, to outline best practices in the implementation of AI-based personalization, and to underscore critical gaps in need of further study. Ethical Considerations Because this study relied only on publicly available literature, it did not require ethical approval. However, the moral standards were strictly conducted in the research. This comprised transparency in the choice of the studies, precision when interpreting and reporting data, and considerations related to intellectual property rights, including accurate source attribution. Besides the research ethics, the review extended its attention to wider ethics concerning AI in education. Student privacy, the possibility of algorithmic bias, and academic integrity were identified as salient ethical considerations to be considered. These are critical considerations in defining ethical practices for the use of AI in education, to have personalization initiatives that are fair, safe, and consistent with the foundational beliefs of contemporary education systems. RESULTS AND DISCUSSION This section outlines the findings of the systematic review, including the study selection process, a detailed summary of the included studies, and an analysis of the extracted data. The review focuses on how artificial intelligence is being utilized to enhance personalized learning across various educational settings. Study Selection Results The review selection process followed the PRISMA approach to ensure methodological quality and study transparency. A first search using academic databases generated a search result of 17,899 articles from AI in personalized learning in contemporary education. After excluding duplicates and using predefined inclusion and exclusion criteria, eight studies were included in the Table 2: Quality Assessment / Risk of Bias for AI-Powered Personalized Learning Interventions Bias Category Low Risk Moderate Risk High Risk Algorithmic Design Bias (A) Clearly explains AI models, logic, or personalization frameworks that underpin the learning intervention. Limited discussion of the AI design, with vague or generic references to personalization mechanisms. No mention of how AI or personalization was implemented. Resource Transparency Bias (R) Comprehensive details on computational resources, data requirements, training time, and infrastructure used in the AI system. Partial description of resources, lacking specific information on system requirements or implementation feasibility. No mention of technical or resource- related considerations. Learning Context Bias (L) Clearly outlines learner demographics, learning environments, and contextual factors affecting AI personalization. Some contextual data provided, but not detailed enough to assess the environment’s influence on AI effectiveness. No learner characteristics or context described. Pedagogical Integration Bias (P) Provides detailed explanation of instructional strategies used alongside AI, including adaptive techniques, feedback loops, and learning paths. Brief mention of teaching strategies or learning activities with limited linkage to AI integration. No mention of educational strategies or their alignment with AI tools. Content Delivery Bias (C) Includes access to AI- generated learning materials, adaptive content examples, and tools used to deliver personalized experiences. Some educational content or tools mentioned, but insufficient to replicate or evaluate the intervention. No educational materials or delivery mechanisms described. Pa ge 13 1 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 final analysis. They were considered most pertinent for the simple reason that they focused on AI applications specifically serving personalization learning strategies. The step-by-step selection process is presented in a PRISMA flow diagram (Figure 1) to demonstrate the exclusionary process of the articles from identification to inclusion. Figure 1. PRISMA Flow Diagram The initial search identified 17,899 records. After removing duplicates, 17454 remained for screening. Of these, 699 underwent full-text review, with 654 excluded for reasons including a lack of empirical data, a non- higher-education focus, and methodological weaknesses. The final review included 45 studies. Characteristics of Included Studies The 45 studies included in this review explored various applications of AI in per- signalized learning in higher education. The characteristics of the included studies are summarized in Table 3. Table 3: Summary of Included Studies on AI for Personalized Learning in Education Authors & Year AI Intervention/Development Key Outcomes for Personalized Learning Fu & Weng, (2024) Ethical and infrastructural review of AI in education AI enhances efficiency but raises ethical concerns Aljabr & Al-Ahdal, (2024) Examines ethical/social implications of AI in pedagogy Benefits exist, but risks include bias and integrity issues Wang, (2023) IoT-AI based recommendation system Improves personalization in course selection Simbeck, (2024) Dataset for learning analytics Enables adaptive learning through data Bognár et al. (2024) AI vs. classical learning theories Higher engagement with blended AI-classical models Stahl & Eke, (2024) Analytical review on ChatGPT ethics Pedagogical value balanced by ethical issues Chan & Hu (2023) Student perception of generative AI Mostly positive with minor concerns Chan & Tsi (2024) Generational comparison on AI Gen Z shows higher enthusiasm Idrisov & Schlippe, (2024) Adaptive AI learning modules Instructor support enhances AI outcomes Pa ge 13 2 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 Eltahir & Babiker, (2024) AI impact on e-learning performance Improvement in student performance Davison et al. (2024) Ethical analysis of generative AI Benefits with significant integrity concerns Abulibdeh et al. (2025) Strategic AI integration in higher ed Enhances admin efficiency and transformation Pardos & Bhandari, (2024) ChatGPT for math support Better engagement with minor limitations Nguyen & Habók, (2024) Assessment practices using digital tools AI enhances assessment, teacher training needed Yakubov et al. (2024) Predictive AI in gamified learning Allows early performance intervention Crompton et al. (2024) AI to support English learning Improved proficiency and engagement Result Analysis The Overview of Studies, in selected papers, shows that AI occupies the central position supporting the personalization of learning since; by using it, the system can adjust content and pedagogy to a particular learner. In this regard, recommendation systems using AI technology have been identified as highly effective tools enabling content specialization for teaching, which can promote student interest in learning and enhance knowledge consolidation. It has been shown in previous studies that with adaptive feedback, students perform significantly better in academic tasks with intelligent tutoring systems. Moreover, AI-driven real-time learning analytics enable early predictability of at-risk students, triggering timely and focused pedagogic interposition. The innovations demonstrate AI’s power to adapt instruction to individual learners, transforming education to be more adaptive and learner-centered, whether in classrooms or online. Across multiple trials, this and other AI-enhanced tools improved motivation, test scores, and satisfaction with learning, according to students who used the tools in the studies. Adaptative AI in higher education settings has demonstrated the potential to make a substantial impact on both students’ academic performance and course completion rates, thus demonstrating AI’s potential in tertiary education scenarios. Nevertheless, the embedding of AI tools in contemporary educational systems is not exempt from its difficulties. Ethical considerations concerning data privacy, transparency, and potential biases also persist. The widespread collection and analysis of data on education from higher educational institutions come with additional challenges while focusing on privacy laws and ethical guidelines. Establishing trust among students and educators involves clear communication about how data is used and protected. The successful deployment of AI tools also depends a lot on teacher readiness. One of the common challenges discussed in several research works is the inadequacy of teachers’ training, which can hinder the effective incorporation of AI into the curriculum. Widespread availability of practical and pedagogical experience of AI technologies There is limited knowledge among faculty in many institutions of the operational and pedagogical aspects of AI technologies, which highlights the need for structured professional development. However, several researches have shown the potential of scalability and personalization of AI- based educational applications. Their effective translation relies, however, on thoughtful application in context and collaborative efforts from different groups of actors (among educators, technologists, and policymakers). The methodological quality of the studies was rated moderate to high, with the majority assessed as low risk of bias. The robustness is brought about by rigorous research designs, and findings were published in credible academic journals. Discussion This review emphasizes the revolutionary integration of artificial intelligence (AI) in personalized learning at the higher education level. AI-driven solutions have consistently proven to be able to personalize instruction for individual students, increase student engagement and motivation, and yield increased academic achievement (Mallik, 2024). As follows, the findings described, implications for pedagogies and institutional support, concerns and limitations regarding the existing body of evidence, and suggestions for further research are discussed. Artificial intelligence is increasingly being tapped to deliver a more personalized learning experience. Adaptive systems, recommendation algorithms, and intelligent tutoring systems have shown their potential to customize content to each learner, leading to better retention and degree of satisfaction. Real-time analytics and feedback also facilitate interventions as students at risk are discovered, and teachers can more accurately respond. In addition to improved learning results, students appreciate the fun, motivation, and game-like experience of these learning resources. The enabling power of AI to adjust to various learning styles and rates helps to create a more inclusive learning environment. But these opportunities are accompanied by the possibility to implement and equitably distribute responsibly (Mallik et al., 2025). AI is changing the image and nature of teaching beyond mere content personalization. There is a trend towards data- driven, student-centered education, with the assistance of AI in personalizing teaching strategies. Adaptive systems that allow educators to spot needs and intervene where necessary based on what a student has and has not been able to do are used in these cases (Mallik et al., 2025). Also, contemporary students seem to be more open- minded about AI tools, indicating a generation gap in Pa ge 13 3 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 4(1) 127-135, 2025 perspectives on technology and education. These changes demand changes in curriculum design, focusing on digital literacy and the use of AI in learning. As pedagogical techniques change, teachers will need to acquire new skills to utilize AI to augment traditional instruction effectively. AI is not only transforming the classroom. Administrative tasks such as scheduling and allocation of resources and student support services are more and more moving to AI-based automation (Mallik, 2024). Such routine tasks can be left to chatbots and automated systems, allowing educators and administrators to focus their time and energy on higher-level decision-making and interaction with students. This integration enables institutional efficiency and scalable, responsive academic models. To maintain this innovation, universities need to devise approaches that use AI in a manner consistent with long-term objectives, taking into account cost, accessibility, and the role of humans in supervision. Institutions that embrace structured, ethical frameworks for the use of AI are most well-equipped to catalyze systemic change. Although it cannot be denied that the advantages are obvious, ethics issues still play a crucial role. A conversation continues to be had on data privacy, algorithm bias, academic integrity issues, and transparency in order to move forward in a way that’s responsible. Poor regulation or misuse of student data will diminish trust and exacerbate inequalities in education (Mallik & Rahman, 2024). Limitations Although revealing, there are some limitations of the studies reviewed. Some results lack generalizability because of differences in the quality and design of the included studies and dependence on self-report. In multiple instances, the context, generalizability, and long- term sustainability of AI solutions are often poorly retained. Moreover, these researches are conducted in industrialized regions, which may not address the challenges of under-served zone institutions. Ethical implications are not consistently considered, and the studies rarely provide concrete measures for the reduction of the risks of the use of AI in education. Future Research In order to enhance the empirical foundation, we call for further research utilizing stricter and longitudinal experimental designs. The field requires that there be standardized frameworks that measure learning outcomes, engagement, and ethics consistently. This will broaden the scope of cross-cultural and cross- institutional comparisons to find out the scale and the portability of AI systems in a variety of educational environments. Additionally, further examining the ethical implications is essential. Researchers, educators, and policymakers should develop best practice guidelines to ensure the responsible, inclusive, and transparent integration of AI. Studies that intertwine quantitative data with students’ and teachers’ qualitative viewpoints could be instrumental in informing the development of effective AI-based strategies for personalized learning. CONCLUSIONS This paper confirms the significant impact of AI in the enhancement of personalized learning in contemporary higher educational institutions. Customized Learning Content – Highly effective in helping students better retain information, individualized learning puts customized content at the center of study. Intelligent Institutions – From children spilling hot soup to goosing a child…artificial intelligence in education has numerous concerns. AI aids in delivering responsive, effective, and learner-centric education by facilitating adaptive learning and real-time analytics. AI integration also involves critical challenges. To ensure responsible use, ethical issues covering data privacy, algorithm transparency, and academic deception need to be considered. The successful implementation of AI tools would also demand considerable investments in teacher training, institutional preparedness, and supportive infrastructure. The published literature demonstrates the enormous potential, although methodological differences exist. The research designs, samples, and measures vary in many studies, which makes the comparison and generalization of findings difficult. These constraints underscore the demand for standardized evaluation practices, long- term studies, and context-sensitive works that can more genuinely estimate the enduring impact and persistence of AI-driven interventions in education. REFERENCE Abrar, M., Aboraya, W., Khaliq, R. A., Subramanian, K. P., Al Husaini, Y., & Al Hussaini, M. (2025). AI- Powered Learning Pathways: Personalized Learning and Dynamic Assessments. International Journal of Advanced Computer Science & Applications, 16(1). Abulibdeh, A., El-Masri, M., & Almarashdeh, I. (2025). 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