Mobile-Optimized AI-Driven Personalized Learning: A Case Study at Mohammed VI Polytechnic University

The purpose of this paper is to compare and contrast characteristics of use and adoption of mobile learning in higher education in developed and developing countries. A comparative case study based on a survey questionnaire was conducted with 189 students (undergraduate and postgraduate) from Makerere University in Uganda and the University of Adelaide in Australia. The Unified Theory of Acceptance and Use of Technology (UTAUT) was employed as the theoretical framework. The results indicated that higher education students in developed and developing countries use a range of technologies for learning, with major differences between Uganda and Australia. The study concludes that mobile learning in higher education in developed and developing country contexts is still at an experimental stage with students using mobile devices in pedagogically limited ways.

This article presents the results of a review of practice and policy in relation to mobile learning and its potential to enhance inclusive and equitable access to higher education in Africa. We reviewed academic literature on potential barriers. Then, we explored the current state of the mobile learning policy environment in 10 African countries through an analysis of how these policies have tried to address the prominent challenges in the adoption of mobile learning as identified in the literature. The findings reveal that significant resourcing inequalities and epistemological, sociocultural, and institutional barriers remain and affect mobile learning adoption. The analysis also reveals that there is still a policy vacuum in relation to mobile learning specific policies within African higher education institutional and governmental policies. Thus, the formal integration of mobile learning in higher education to facilitate equitable access is very much in its infancy. This article suggests a strong need for institutional, cross-institutional, national and African-wide mobile learning specific policies to ensure better implementation of mobile learning. As interest in mobile learning continues to grow, this review will provide insights into policy and strategic planning for the adoption of mobile learning to achieve inclusive and equitable access to higher education.

In recent years, there has been an explosion in the growth of mobile learning across all sectors of education. Keen interest in mobile learning has led to a proliferation of views, perspectives, and diverse activities underpinned by different learning theories. This chapter focuses on various dimensions of mobile learning, including definitions, theoretical dimensions, mobile learning applications in higher education, and future research directions. Mobile learning is still an emerging and immature field. The chapter provides broad definitions and discussions of mobile learning drawing upon existing work. By exploring the experiences and views of various researchers, the chapter reveals the opportunities and challenges involved with mobile learning.

El presente artículo tiene como objetivo identificar, a través de un análisis de componentes principales, los factores relacionados con el aprendizaje móvil o m-learning en educación superior. Para esto, se llevó a cabo una investigación mixta, que retomó los hallazgos de una investigación más amplia, que tuvo como resultado la determinación de catorce factores, y que este artículo reduce a tres a través de un análisis multifactorial. Además, para la recolección de los datos se aplicó un instrumento con escala tipo Likert a 30 personas expertas investigadoras universitarias. Para el procesamiento de los datos se empleó un análisis de componentes principales, que arrojó como resultado los siguientes factores: capacitación, características de estudiantes y falta de gestión administrativa, los cuales guardan correlación con los factores falta de lineamientos m-learning y el de infraestructura. En conclusion, los factores extraídos deben ser tenidos en cuenta a la hora de trazar políticas y lineamientos para incluir el aprendizaje móvil en la educación superior, así como en los planes de formación docente, en respuesta a lo que el estudiantado está demandando.

In successful mobile learning (ML) integration, factors associated with live-ware, software, and infrastructure are important. The main objective of this study is to investigate and model the influencing factors for learners and teachers at once to adopt ML in higher education. The proposed model consists of five impact factors: teacher, learner, mobile devices, ML tools, ML contents, communication technologies, and higher education institutes. Then the proposed model was implemented using a modified Moodle mobile application and evaluated using 60 teachers and 60 learners attached to the University of Kelaniya, Sri Lanka in 2021. According to the experimental research design approach, the proposed impact model was assessed as pre-test and post-test surveys using seven questionnaires. According to the Pearson correlation coefficient test, the most significant factor for learners and teachers to adopt ML is the mobile device. Learning content and communication technology were elected as the second most significant adoption factors for teachers and learners consecutively. However, higher correlation values were obtained for all factors denoting that they are greatly influenced the participant to adopt ML. The significant influencing factor of each impact factor was also investigated. In conclusion, it was recommended that featured smart devices, quality learning content, user-satisfied communication technology, academic enriched ML tools and higher education institutes with sound educational facilities are crucial for the university community to adopt ML in higher education. These findings help design academic community acceptable ML environments for higher education context.

The use of mobile devices in learning is increasing, allowing students to access learning resources. In the ever-growing digital era, mobile learning not only offers innovative learning alternatives but also shapes the future of education. This study provides a comprehensive bibliometric analysis of 8352 articles on mobile learning in higher education from 2002 to 2023. With the help of VOSViewer, the researcher can explore complex relationships between scientific information, gain deep insights into specific subject areas, and make decisions supported by powerful bibliometric analysis. Key findings reveal a peak in 2019 with 673 publications, and the most cited work is “Current Status, Opportunities And Challenges Of Augmented Reality In Education” by Wu et al. (2013) with 1285 citations. Prolific authors include Hwang, Huang, Ogata, and Looi. Productive sources are ACM International Conference Proceedings Series and journals like International Journal Of Mobile Learning And Organization. Institutions like National Taiwan University and countries like ChinaUnited States, and Taiwan lead in productivity. Top keywords include “mobile learning,” “e-learning,” and “students,” offering insights for future research planning

Mobile learning is the use of technology in the learning process using tablets, PCs or smartphones. Online based mobile learning provides opportunities for students to study anytime and anywhere. The use of a website as a learning media gives users broad access without downloading additional applications that are burdensome to the device. Therefore, the perception of physics education students is needed on the use of mobile learning in basic physics practicum II at Universitas Jambi. This study aims to introduce new learning media that are more effective and flexible in the 4.0 digital era. Based on the results of the study, the perceptions of physics education study program students are in a good category with an average of 74.01 and a median of 75.00 and a mode of 75.00. Distribution of perception scores of physics education students has a minimum value of 52.00 and a maximum value of 91.00

Mobile learning technologies have the potential to change higher education teaching, participation and learning now and in the future. This chapter investigates contributing factors and how these are positioned and considered within higher education in Lao PDR and Cambodian universities. It investigates the cases of two university-based agriculture courses. In particular, it explores the issues under consideration by educators as they develop degree programmes that enhance flexibility and learning outcomes through the incorporation of mobile technologies. Literacies, access, barriers and participation associated with adopting mobile learning technologies in higher education in these two countries are explored from the perspectives of the student and the lecturer. The chapter considers the functional aspects of mobile learning relevant to participation and learning including dissemination and access to information and communications, enabling person-to-person connections in education and connecting and intersecting learning content with the situated contexts of the learner. Access issues and barriers to the use of the technology within programmes are considered. Key insights associated with mobile learning adoption within these universities are highlighted at each of pedagogical, technical and organisational levels.

Mobile devices are becoming increasingly pervasive within the higher education student community and correspondingly mobile learning applications are increasingly popular as part of the learning process. The purpose of this study was to investigate engagement with, and effectiveness of, different avatar interface types (text, image, audio, video and cartoon) when used within a mobile learning context, and specifically, when used to teach the English language courses mandatory within Saudi degree programmes. One hundred fifty six students from Albaha University participated in this study which involved students undertaking mini lectures delivered onto their mobile devices via each of the five avatar types. The students completed pre/post questionnaires about their perceptions and experiences of mobile learning and undertook a short test to assess their performance and retention of information after the mini-lectures. Results revealed that the majority of students rated the convenience of using m-learning as being very important and showed that they possessed the required resources and underlying technical experience required to adopt m-learning. Results also showed that whilst female students preferred to learn from video or cartoon interfaces they performed best with cartoon and audio interfaces, whilst male students preferred the video interface yet performed better with the cartoon interface.

Mobile learning (m-learning) is transforming higher education, reshaping traditional teaching methods with unprecedented flexibility and accessibility. This study delves into the dynamic realm of m-learning research, exploring its evolution, challenges, and future prospects through a comprehensive bibliometric analysis guided by PRISMA guidelines. By examining publications, citations, keywords, and global trends from 2007 to 2023, our research highlights the United States, Australia, and the United Kingdom as leaders in mobile learning, supported by their significant citation rates. Key themes such as mobile devices, higher education, and blended learning emerge as pivotal in shaping this educational frontier. Innovative methodologies such as augmented reality (AR), gamification, and integrating social media content and learning management systems (LMS) underscore the diverse landscape of m-learning. Beyond enhancing educational practices and student engagement, m-learning promotes interactivity, lifelong learning, and student motivation. However, challenges persist, including the need to strengthen internet infrastructure, improve content quality, foster digital literacy, and ensure privacy in digitally immersive learning environments. This study highlights the transformative power of m-learning and calls for continuous innovation to fully harness its potential in advancing global education in the digital era.

Technological advancements in mobile devices coupled with their increased use in informal and formal learning at an individual level have forced academicians to implement full-scale mobile learning in higher education. However, its full-scale implementation can be achieved if the perception and attitude towards its acceptance are studied from the viewpoint of higher education teachers. This exploratory study begins with the identification of potential determinants of mobile learning acceptance. Thereafter, Teachers Mobile Learning Acceptance Scale was developed based on the explored factors. There is no available instrument so far that encompasses important factors for determining acceptance of mobile learning by higher education teachers in ‘one go'. This instrument serves to be a comprehensive measure of teacher's acceptance of mobile learning. Two new factors ‘teacher authority and privacy' and ‘motivational factors for students' have been added. Although these factors have not been addressed in the past research, yet they play a significant role when considered from teachers' perspective. This instrument depicts a high level of content validity and reliability.

The integration of Artificial Intelligence (AI) with mobile learning platforms has revolutionized personalized education by dynamically adapting content to individual learner preferences. This paper presents the design and implementation of Smart Ed, an AI-driven mobile learning framework that leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to customize educational experiences based on user behavior, learning patterns, and performance metrics. The proposed system consists of multiple components, including a personalized content recommendation engine, an adaptive assessment module, a content management system (CMS), cloud-based storage, and a real-time feedback mechanism. To enhance the learning process, the AI-powered recommendation engine continuously analyzes learner interactions, assessments, and engagement levels to provide customized study plans, interactive quizzes, and AI-assisted tutoring. The system integrates multi-factor authentication (MFA) and role-based access control (RBAC) to ensure secure access and data privacy. Furthermore, a real-time analytics dashboard enables educators to track student progress and optimize content delivery. Cloud-based storage and API integrations facilitate seamless accessibility across multiple devices. The framework is evaluated using key performance metrics such as content recommendation accuracy, student engagement levels, and learning efficiency. Results indicate that Smart Ed significantly improves student retention, comprehension, and overall learning outcomes compared to traditional e-learning models. By incorporating state-of-the-art AI techniques and adaptive learning methodologies, this project aims to bridge the gap between conventional education and digital learning, ensuring a personalized, scalable, and effective mobile learning experience.

Mobile learning (M-Learning) has become a popular and effective method of education that leverages the ubiquity of mobile devices. M-Learning has digitally transformed the process of teaching and learning. It has tremendous potential to empower all sections of society through education and training. This study presents a systematic literature review of M-Learning. The articles were retrieved from Scopus and Web of Science databases. After applying inclusion and exclusion criteria, a final selection of 161 articles published between 2016 and 2022 was included in the review. To analyze the articles, the researchers employed the TCCM (Theory, Context, Characteristics, Methods) framework, which facilitated addressing the research questions. This review identified various theories, such as behaviorism, constructivism, cognitivism, situated learning, problem-based learning, context awareness learning, socio-cultural theory, collaborative learning, conversational learning, lifelong learning, informal learning, activity theory, connectivism, navigation, and location-based learning, that are used to support and guide the implementation of M-Learning. In terms of context, developing countries contributed to 70.8% of the studies, while developed countries contributed to 29.1%. Further, a majority of the studies, 93%, involved students followed by faculty members and only two studies involved staff from higher education management. A total of 19 unique characteristic factors have been identified, such as personal, intention, attitude, usage, utility, ease of use, learnability, social, technological, pedagogical, anxiety, enjoyment, accessibility, knowledge, experience, trust, price, and habit. A quantitative research design was used in 90% of the studies, followed by mixed methods research design in 7% of the studies, and qualitative research design in only 3% of the studies. Further, this article synthesizes previous research findings and highlights gaps for future research. Overall, this review contributes to the understanding and advancement of M-Learning as a valuable educational platform.

Mobile instant messaging support for teaching and learning in higher education

Since their inception, social media technologies have been reinventing the communal and cultural traditions of our society. They have repeatedly shown their ability to uproot deeply seeded customs and replace them with new methods of interaction. Businesses and corporate institutions are quickly turning to virtual learning in order to train new staff and educate corporate elites. Individuals adopted ‘digital personas’ which serve as an online representation of their physical being. Accordingly, educational methodologies are drastically affected by the rapid growth of social media technologies. Leading this andragogical revolution is Second Life, “an online, three dimensional virtual environment in which users take on the form of avatars, a representation of themselves, and then interact with other users in the synthetic environment” (Inman, Wright, & Hartman, 2010, p. 45). Within this simulated environment, users are able to actively collaborate and interact with surrounding objects, people, and the environment. If harnessed correctly, Second Life has the potential to house complex virtual educational institutions whereby users are able to dynamically engage with academic material, interact with peers, and ultimately complete university credits, all over an online virtual platform. Thus, the realm of Second Life is an extraordinary example of how andragogical strategies can be shifted to develop new educational practices in terms of an immersive online world. This approach to social media in education is one that will change the face of pedagogy and andragogy in the future.