This systematic review synthesizes 64 empirical studies to examine how Generative AI (GenAI) shapes learning in Computer Science Education (CSE), particularly in programming, debugging, algorithmic reasoning, and computational problem-solving contexts. Grounded in Constructivist, Sociocultural, Cognitive Load, Adaptive Learning, and Metacognitive Learning theories, the review adopts an integrative perspective to analyze how GenAI-driven adaptivity, AI output qualities, hallucination dynamics, and cognitive–affective regulation influence learners' interpretation, cognitive processing, and learning outcomes. Findings reveal a dual impact of GenAI in CSE. On the negative side, hallucinated or misleading outputs can increase extraneous cognitive load during programming and debugging and promote over-reliance on system-generated content. They may also perpetuate inequities due to limited access in low-resource settings or insufficient support for culturally and linguistically diverse learners. These effects can disrupt error detection, self-monitoring, and problem-solving, leading to impaired learning performance and widened educational disparities. On the positive side, when embedded within structured, equitable, and pedagogically grounded environments, GenAI supports reflective programming practice by promoting self-monitoring, verification, and strategic adjustment, thereby enhancing problem-solving skills, engagement, and personalized learning outcomes. By framing learning performance, hallucination dynamics, and problem-solving as interconnected dimensions of GenAI-supported computing education, this review provides a theoretically coherent and pedagogically grounded lens for understanding how GenAI reshapes learning in CSE. The review's novelty lies in its integrative conceptual framework, offering actionable insights for designing equitable, cognitively balanced, and instructionally effective GenAI-supported learning environments.

This pilot study investigated the potential of integrating Augmented Reality (AR) and Generative Artificial Intelligence (GenAI) to support engagement and task management for university students diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). Using a Design-Based Research (DBR) approach, we evaluated ADHDvance LearnAR across three within-subject conditions (baseline tools, AR-enhanced, and AR+GenAI) with n = 15 undergraduate participants. Quantitative measures showed directional improvements in engagement duration, interaction frequency, and task completion across conditions; however, differences did not reach statistical significance, consistent with the study’s exploratory scale. Qualitative analysis provided explanatory depth, identifying themes related to (i) increased task salience through spatial AR visualisation, (ii) improved task initiation and planning support, and (iii) reduced perceived cognitive load through just-in-time GenAI scaffolding. Findings suggest AR+GenAI may offer promising affordances for inclusive learning design, warranting longitudinal evaluation with larger samples and stronger causal designs.

This systematic literature review investigates the Google Cloud Healthcare API's role in transforming healthcare delivery through advanced analytics, machine learning, and cloud-based solutions. The study examines current features of cloud-based healthcare platforms in managing heterogeneous healthcare data formats, analyzes the effectiveness of cloud solutions in enhancing clinical outcomes, and compares Google Cloud Healthcare API with alternative platforms. The findings reveal that Google Cloud Healthcare API demonstrates notable advantages through its fully managed, serverless architecture, native support for healthcare standards (e.g., FHIR, HL7v2, DICOM), and seamless integration with advanced AI/ML services. Cloud-based predictive analytics platforms have proven effective in reducing hospital readmissions, addressing physician burnout, and enabling scalable telemedicine solutions. However, significant challenges persist including data privacy concerns, regulatory compliance complexities, infrastructure dependencies, and potential vendor lock-in risks. The research demonstrates that healthcare organizations implementing comprehensive cloud-based solutions achieve measurable improvements in patient outcomes, operational efficiency, and care delivery models. While technical challenges around latency in medical imaging and interoperability remain, the evidence strongly supports cloud adoption for healthcare transformation, provided organizations address security, compliance, and implementation challenges through strategic planning and comprehensive change management approaches.

In today's era of technological evolution, programming education is crucial for shaping the future workforce and fostering innovation. However, access to quality computer science education remains a significant challenge with Sub-Saharan Africa nations experiencing a pronounced digital divide. Despite growing interest in technology, these countries struggle with unequal access to educational resources. AI-driven tools like ChatGPT, Codey, and GitHub Copilot offer personalized learning experiences that could democratize access to knowledge and reshape programming education. This quantitative study examines the impact of these AI tools on fostering inclusive education in Kenya, Nigeria, and South Africa. It involves 322 university students, using purposive sampling and online questionnaires. Various quantitative analyzes, including descriptive statistics, country-wise comparisons, one-way ANOVA, Kruskal–Wallis tests, and correlation analysis, were conducted. The study reveals students’ motivations for programming, their attitudes towards AI-driven educational tools, and the perceived impact on equity, diversity, and inclusion. Significant variations were found in attitudes based on educational level and country of residence, highlighting the need for tailored strategies to enhance the inclusivity and effectiveness of AI-driven programming education tools.

This research delves into the dynamics of active school transport (AST) by utilizing a two-stage co-design process and leveraging persuasive technology within a game for promoting AST called Battleship-AST. The primary aim of this research is to thoroughly investigate the two-stage game co-design process employed in creating a Battleship-AST game. Moreover, our research aims to evaluate participants’ perceptions regarding the motivating and engaging potential of the persuasive technology and gamification features embedded within the final iteration of the game. This evaluation aims to understand how these features influence participants’ motivation to increase their usage of AST through gameplay. In pursuit of these objectives, the research builds upon the existing Battleship-AST prototype and actively engages school children in a collaborative two-stage co-design process. Their valuable insights and preferences were harnessed in refining the game, which was subsequently tested during a tech event in Skellefteå, Sweden. The findings shed light on various aspects of the game’s impact, from its reception to the gamification features integrated within. Notably, the research highlights the positive impact of the co-design process, with increased motivation and engagement observed among the participants. Their involvement in shaping the game’s design resulted in a more engaging and enjoyable experience. The persuasive technology features, encompassing competition, collaboration, auditory cues, a virtual reward system, and an emphasis on similarity, played a pivotal role in sustaining engagement and motivating players. Elements such as rewards, leaderboard progression, and badges proved highly effective in encouraging continued participation and fostering a positive feedback loop. However, the study also identifies areas for potential improvement, including the need to measure real-life progress and refine the game’s levelling system. The research indicates that refining feedback mechanisms and tailoring game content to individual preferences could create an even more engaging experience. Additionally, long-term playtesting is proposed to assess the game’s extended impact. The findings offer promising avenues for enhancing motivation and engagement in AST, which can contribute to the promotion of healthier and more sustainable transportation choices among school children.

According to current research into trends in information technology and its impact on the global economy, it has been realized that the upward rise and adoption of digital technologies continue to contribute to the use of cloud computing technologies, with more concern placed on the energy consumed by these cloud infrastructures. In this paper, as much as the global concern is the energy consumption of different worldwide systems, the focus is directed to the energy consumption of cloud computing infrastructures. Hence, we look into the energy consumption levels of several chosen virtualization technologies and their underlying operating systems in the context of cloud computing, with a close look into their greenhouse gas (GHG) emissions. This work examines how cloud computing components affect energy usage in the global ICT ecosystem. The methodology used in the work was divided into two categories: The macro methodology that emphasized life-cycle analysis, and the micro methodology which used both experimental setup and inferential statistics to confirm details of the result. The findings of the study showed that the Microsoft Hyper-V consumed the least energy, and it is expected that this finding will improve cloud computing practitioners’ and policymakers’ understanding of virtualization tools’ energy consumption patterns alongside GHG emissions, helping them make sustainable environmental decisions.

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

This study investigates how integrating generative AI (GenAI) with instructional scaffolding and prompt engineering supports higher-order thinking skills (HOTS) and programming logic. A mixed-methods design was used, combining quantitative and qualitative data. The intervention followed a one-group pretest-post-test structure over seven weeks with 25 computer science students with no prior C++ experience. The GenAI-Ped framework guided the design. It combines Bloom's taxonomy, Seelf-Regulated Learning, Universal Design for Learning, and Vygotsky's Zone of Proximal Development. Students received scaffolded support across six instructional phases, including prompt training and guided GenAI use. Quantitative results showed significant gains in problem-solving (applying constructs: t = 2.38, p = 0.013, d = 0.475), critical thinking (conditional reasoning: t = 2.53, p = 0.018, d = 0.506), creativity (applying new ideas: t = 2.28, p = 0.032, d = 0.456), and programming logic (loops: t = 2.78, p = 0.010, d = 0.555). However, smaller gains were observed in code optimization (t = 1.693, p = 0.103, d = 0.339) and evaluating solutions (t = 1.732, p = 0.096). Qualitative data, including feedback and GenAI chat logs, showed that prompt specificity and scaffolded feedback improved engagement, HOTS, and programming logic. The novelty of the study lies in its demonstration that the integration of GenAI into programming education using GenAI-Ped framework can sustain HOTS and programming logic while mitigating overreliance. These findings offer a practical model for integrating GenAI into programming education.

The study employs design-based research (DBR) to design the mathematics teaching and learning activity model (M-TLAM) for blended learning instruction in a Tanzanian higher education context. This model utilises contextual factors, including ICT tool usage, collaborative learning with metacognitive activities, and local culture, to determine how optimising these factors boosts math students' motivation and achievement. Two DBR phases were conducted, of which Phase 1 lasted for two weeks and Phase 2 an entire semester. To evaluate M-TLAM in real-life learning settings, experiments were conducted with 225 first-year undergraduate students and seven lecturers at the  College of Business Education in Tanzania. Experimental data were collected from pre-and post-tests, interviews, and questionnaires administered to students and lecturers. The study examined the perceptions and motivation of the participating lecturers towards using the M-TLAM in mathematics education and the factors influencing student motivation, academic achievement, and experience towards business mathematics courses. The evaluation results are promising and show that the M-TLAM implementation can potentially improve students' motivation and academic achievement. In addition, the pedagogical experiences of students were primarily positive, and student's attitudes towards the business mathematics course through M-TLAM were more favourable than those of students who studied the course via traditional methods. Cultural, technological, and instructional Factors contribute to students' improved motivation. Lectures demonstrated a cheerful disposition towards the potential of the M-TLAM for enhancing teaching and learning activities. The contributions of this study are highlighted through the implementation of the M-TLAM for blended learning and through the design principles and guidelines provided towards its effective implementation.

A computer-based simulation is a viable approach for integrating the basics of science and technology in Junior Secondary. This study examined the impact of PhET on students' academic performance as well as students' motivation toward Basic Science and Technology. The study also investigates how PhET influence students' attitudes toward Basic Science and Technology. The study adopted the pre-test, post-test, and non-equivalent control group design. We randomly selected the study population from junior secondary two (JSII) students in private and public schools who are studying Basic Science and Technology. Quantitative data were collected and analyzed using a statistical approach such as the mean, standard deviation, and t-test. Students' post-test academic performance improved significantly (t160 = 36.28, p < 0.05) as a result of teaching Basic Science and Technology with PhET. According to the findings PhET had a substantial effect on the motivation of Basic Science and Technology students (t160 = 29.32, p < 0.05). Furthermore, the results demonstrated that PhET affected students' attitudes toward Basic Science and Technology (t160 = 65.36, p < 0.05). This study contributes to the body of knowledge by providing empirical evidence to support the integration of PhET in the teaching of Basic Science and Technology in Nigeria and other similar contexts. The findings suggest that PhET can be an effective pedagogical tool for improving learning outcomes in science and technology education, particularly in developing countries where resources and infrastructure may be limited.