One of the advanced technologies in e-learning deals with the systems’ ability to fit the students’ preferences. It emerged based upon the common conception that every person has different learning style. However, despite the many options of learning style models toward using personalized e-learning, there are considerable challenges to assess the usability degree of the e-learning. The aim of this study is the evaluation of usability of personalized adaptive e-learning system that has been developed based on students’ learning style and initial knowledge level. The study involved 62 Computer Network students in one of the public vocational secondary schools in Yogyakarta, Indonesia. To measure the usability, the USE Questionnaire, which consists of four indicators (usefulness, ease of use, ease of learning, and satisfaction) represented by 30 questions with four possible Likert scale options, was distributed to the students. The research finding indicates at first the usability of the adaptive e-learning system for the students was well accepted in all aspects of usability. Next, the multiple linear regression result showed that the variables usefulness, ease of use, and ease of learning simultaneously influence satisfaction. Lastly, the regression results also revealed that the variables usefulness and ease of use partially influence satisfaction, while the variable ease of learning does not.

This article aims to sketch a conceptual design for an information and knowledge management system in sustainability research projects. The suitable frameworks to implement knowledge transfer models constitute social communities, because the mutual exchange and learning processes among all stakeholders promote key sustainable developments through the coproduction of knowledge. A main finding in theory is that integrative information and knowledge management fulfills the necessary requirements to realize the coproduction of knowledge. On the other hand, the important finding in practice is that knowledge is best transferred during face-to-face meetings.

In today's world, the ability to think computationally is essential. The skillset expected of a computer scientist is no longer solely based on the old stereotype but also a crucial skill for adapting to the future. This perspective presents a new educational challenge for society. Everyone must have a positive attitude toward understanding and using these skills daily. One thousand two hundred seven documents about computational thinking (CT) may be found while searching the Scopus database from 1987 to 2023. Data from Scopus were analyzed using VOSviewer software. This study educates academics by delving into the fundamentals of what is known about the CT of visual and quantitative research skills. This approach allows for a more in-depth look at the literature and a better understanding of the research gap in CT. This bibliometrics analysis demonstrates that (1) research on CT is common to all sciences and will develop in the future; (2) the majority of articles on CT are published in journals in the fields of education, engineering, science and technology, computing and the social sciences; (3) the United States is the most dominant country in CT publications with a variety of collaborations; (4) keywords that often appear are CT, engineering, education, and mathematics, and (5) research on CT has developed significantly since 2013. Our investigation reveals the beginnings and progression of the academic field of research into CT. Furthermore, it offers a road map indicating how this study area will expand in the coming years.

In the Big Data era, people can access vast amounts of information, but often lack the time, strategies and tools to efficiently extract the necessary knowledge from it. Research and innovation staff needs to effectively obtain an overview of publications, patents, funding opportunities, etc., to derive an innovation strategy. The MOVING platform enables its users to improve their information literacy by training how to exploit data mining methods in their daily research tasks. Through a novel integrated working and training environment, the platform supports the education of data-savvy information professionals and enables them to deal with the challenges of Big Data and open innovation.

The primary purpose of this study is to evaluate the web-based adaptive e-learning application based on the expert-based assessment. There are two aspects of assessment considered in this study, the first one will evaluate the e-learning system in terms of the learning content and its structure, and the second one will focus on the media aspect. The process of evaluation was started by developing the instruments of evaluation by taking into account some related literature. Then, the content validity of the instruments was checked by scientific experts. After that, the assessment was conducted by two groups of experts in a paper-and-pencil format by marking one out of 4 points Likert scale. The result was then analyzed through some justification approaches and indicated that the adaptive e-learning application was categorized acceptable to use in the learning process.

While educational gamification successfully drives student engagement, it faces persistent criticism for fostering extrinsic reward dependency, superficial achievement (“fast leveling”), and inequitable learning experiences. This study posits Artificial Intelligence (AI) as a critical corrective mechanism to these structural limitations, repositioning static game mechanics within dynamic, adaptive learning ecosystems. Adopting an Integrative Review methodology based on Whittemore and Knafl’s framework, this study synthesizes 61 empirical and theoretical studies (2003–2025) identified through systematic two-way snowballing. Complementary bibliometric data from Scopus and ScienceDirect reveals an exponential “J-curve” growth in the field, marking a decisive disciplinary shift from computer science architectures to pedagogical applications in Social Sciences. The findings indicate that AI integration mitigates traditional gamification pitfalls by (1) personalizing difficulty through adaptive algorithms, (2) replacing superficial rewards with intelligent, real-time feedback, and (3) enhancing inclusivity for diverse learner profiles. Crucially, this review proposes the “AI Corrective Role Framework,” a conceptual model grounded in convergent evidence that operationalizes how AI acts as a learner-centered function to deepen cognitive retention and as a decision-making instrument for institutional strategy. These insights offer researchers and policymakers a robust roadmap for implementing sustainable, evidence-based, and equitable gamified learning environments in the era of Generative AI.