Generative artificial intelligence (GenAI) is used in programming education; however, its adoption can introduce pedagogical misalignment, shallow cognitive engagement, and ethical risks that threaten the sustenance of programming skills of students. This study evaluated the GenAI programming education framework’s ability to sustain higher-order thinking skills (HOTS) and programming logic while mitigating pedagogical, cognitive, and ethical barriers in Java programming. A between-group mixed-methods experiment was conducted amongst 124 undergraduate students (62 in the control group and 62 in the experimental group) over 7 weeks. Learning outcomes were assessed using pretests and posttests, analyzed with baseline-adjusted ANCOVA and MANCOVA, and supplemented with trace-based learning analytics from GenAI logs collected at time points (Weeks 3 and 7). The experimental group showed a baseline-adjusted advantage on HOTS (adjusted mean difference = 0.29; p < 0.001; adjusted Hedges’ g = 0.80) and a smaller but significant improvement in programming logic (adjusted mean difference = 0.21; p = 0.047; adjusted Hedges’ g = 0.36), alongside a multivariate group effect across domains. Log-derived indices also showed larger gains in pedagogical alignment and cognitive engagement, reflected in more frequent task decomposition and debugging behaviors. Ethical engagement has also increased, indicating consistent hallucination and data sensitivity awareness. Path modelling indicated that the intervention increased changes in pedagogical, cognitive, and ethical engagement. Pedagogical alignment and cognitive engagement were positively associated with post-test HOTS and programming logic, whereas ethical engagement was negatively associated with HOTS but not significantly associated with programming logic. Overall, the findings suggest that GenAI becomes more educationally beneficial in programming when guided by a structured approach.

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.

The need to integrate the teaching and learning of computational thinking (CT) in K-12 education has been on the rise since it was identified as a skill for solving 21st-century problems. The co-design pedagogical approach has shown great potential in promoting effective communication of CT to both university and K-12 students with the support of different educational tools in different contexts. To ensure Nigerian secondary school (K-12) students develop CT skills, a four-day co-design CT activities workshop was organized. Co-design pedagogy and constructivism theory were deployed in this study with students co-designing COVID-19 disease spread game for learning CT. A mixed method was adopted to investigate student’s interest, attitudes, understanding of CT, and their learning experience from implementing CT-based prototype using Scratch. This study recruited 40 students from two different secondary schools in Nigeria as participants. The result revealed that student’s interest in learning CT was aroused through the use of co-design pedagogy and Scratch (μ = 4.55, σ = 0.815). Similarly, students attitude toward CT after the intervention study shows positive (μ = 4.50, σ = 0.716). This study paved way for student’s skills development in teamwork and collaborative learning, communication, idea sharing, personal skill development, game design, and understanding of programming. This study instigates thinking ideation, inspires the application of CT concepts in daily life activities, and improves problem-solving skills. This study promotes and advocates for the application of co-design pedagogy to foster the teaching and learning of CT in a Nigerian context. This study contributes to knowledge by promoting the use of Scratch as a tool for co-designing in learning CT, proposing a four-phase co-design application flow for the integration of co-design pedagogy with Scratch for learning CT in the Nigerian K-12 context and suggesting ways to implement the teaching and learning of CT in K-12 education.

Women's informal saving groups (ISGs) in Tanzania play a vital role in financial inclusion particularly to the low-income earners. However, limited financial management skills for effective financial decision-making hinder their profitabiliy and sustainability. Traditional training and operaional practice methods often fail to meet their needs due to costs, social, and mobility constraints. This study employed design science research and design thinking methodologies to co-create WanawakeApp, a mobile prototype aimed at supporting financial management and decisionmaking. The findings indicate that the app's enhanced financial record-keeping, loan planning, and fund management are for informed financial decision-making. Women's ISGs expressed satisfaction with the App intuitive interface and context-specific content. The study also revealed that device compatibility, sociocultural factors, and user demographics influenced skill development and app's adoption. Conversely, challenges such as connectivity, digital illiteracy, and high costs posed barriers to effective use. WanawakeApp presents a promising model for strengthening community well-being by mitigating negative impacts of financial mismanagement and created a new pathway for research and innovation using design science research (DSR) in Infomation and communication technology for develpmeng (ICT4D) to empower ISGs and informal workers.

Integrating generative artificial intelligence tools like ChatGPT and GitHub Copilot into programming education presents notable opportunities to enhance learning but also raises critical challenges in balancing innovation with preserving foundational programming skills. Through a systematic review of 40 empirical studies guided by PRISMA 2020 and Kitchenham’s methodologies, this study evaluates how effectively GenAI was incorporated into programming education and its impact on preserving higher-order thinking skills and foundational programming logic. The Findings reveal that successful integration hinges on intentional teaching strategies, thoughtfully designed assessments, and structured integration processes. However, barriers such as GenAI tools’ limited accessibility features, insufficient bias mitigation, and a tendency to prioritize tool availability over curriculum alignment often disrupt seamless adoption. Additionally, the potential for students to become over-reliant on AI risks diminishing higher-order thinking skills and programming logic. To address these gaps, this paper proposes the GenAI-Ped framework, a structured approach combining self-regulated learning, universal design principles, and iterative feedback to harmonize GenAI-driven support with skill development. The findings emphasized the necessity of strategic implementation, educator training, and inclusive practices to maximize GenAI’s potential in programming education.

Despite the importance of social network platforms (SNPs) in collaborative learning, their integration into academic curricula faces various challenges, including accreditation requirements, faculty resistance, and infrastructural issues. Using institutional theory, this study examines how coercive, normative, and mimetic pressures affect SNP integration to enhance collaborative accounting learning. A structured questionnaire, grounded in a conceptual framework, was completed by 116 accounting students and faculty members. The data collected include demographic information and responses to 39 statements rated on a Likert scale related to the five structural constructs in the framework. The data were analyzed with partial least squares structural equation modeling (PLS-SEM) via SmartPLS 3, which provides a rigorous assessment of both measurement and structural models. The findings show that coercive and mimetic pressures significantly influence SNP integration, whereas normative pressures are weak due to deep-rooted pedagogical traditions. On this basis, we recommend policy reforms, faculty development, and mobile-centric infrastructure to facilitate SNP integration. These findings contribute to the ICMET discourse by offering practical strategies to transform accounting education into a more collaborative, inclusive, and future-oriented discipline.

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.

Financial management skills are important for the performance of informal saving groups (ISGs) for making informed financial decision-making. Digital training solutions present a convenient, ubiquitous, and relatively affordable skills development mechanism. Despite the importance of digital financial management skills to ISGs, knowledge is lacking on how to design and develop digital learning solutions relevant to the ecosystem of ISGs. This qualitative study combined a design science research framework and cognitive fit theory to map the digital financial management skills relevant to women's ISGs. The results indicate women's ISGs need mobile applications with usability features tailored for financial recording, tracking, and coordination. The results further show that financial planning, management, and recording are desired financial management skills in such ISGs. The study also found that while aligning tasks and mental representations improves performance, it is crucial to consider contextual factors to ensure the relevance of these tasks and solutions. The research implies there is a need for contextualized and localized digital solutions that meet the needs and requirements of ISGs and marginalized people. The study also extends the application of cognitive fit theory within design science research and underlines its implications for information and communication technology for development (ICT4D).

As initiatives on AI education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students' engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this article, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven 2-hour sessions. The summer camp consisted of 2-hour sessions over 4 consecutive days, with 18 students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants' dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students' personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.

Background and contextResearchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K-12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context.ObjectiveThe purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings.MethodUsing a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3-13 years constructed their interactions with a machine-based technology (Google's Teachable Machine).FindingsThis study provides empirical support for the claim that Google's Teachable Machine contributes to the development of data literacy and conceptual understanding across K-12 irrespective of the learners' backgrounds. The results also confirmed children's ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning.ImplicationsThe results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K-12 context.

The recent popularity of computational thinking (CT) and the desire to apply CT in our daily lives have prompted the need for a successful pedagogical technique for learning CT in K-12 education. The application of co-design pedagogical techniques has the potential to improve students’ CT learning through knowledge sharing and the creation of ideas to solve problems and develop an artifact. However, there is a limited understanding of how co-design pedagogical techniques have been explored to foster CT learning, which could hamper the successful use of co-design as a pragmatic teaching approach. This study examined the ways in which co-design pedagogical techniques have been applied in CT education by implementing a systematic literature review (PRISMA protocol) to document the review analysis. A total of 26 articles that met the inclusion criteria for this study were reviewed. Findings in this study revealed that workshops are the most utilized co-design learning setting and, as expected, the collaborative technique is the co-design pedagogical technique most frequently adopted for implementing CT in K-12 education. NetLogo is the most frequently used co-design tool for teaching and learning CT in K-12 education, and an interdependence exists between NetLogo and the Common Online Data Analysis Platform. Co-design also helps teachers develop the ability to use co-design pedagogical techniques to learn, create content, and integrate CT into their various subjects. This study contributes to practical knowledge by unraveling and advocating the use of dialogical, prompting, framing, and game-based techniques as co-design pedagogical techniques for K-12 teachers and also helps teachers identify useful co-design tools for learning CT.

Researchers' efforts to build a knowledge base of how middle school students learn about machine learning (ML) is limited, particularly, considering the African context. Hence, we conducted an experimental classroom study (N = 32) within the context of extracurricular activities in a Nigerian middle school to discern how students engaged with ML activities. Furthermore, we explored whether participation in our intervention program elicit changes in students' ML comprehension, and perceptions. Using multiple qualitative data collection techniques including interviews, pre-post open-ended surveys and written assessments, we uncover evidence that indicated evolution of students’ ML understanding, ethical awareness, and societal implication of ML. In addition, our findings showed that a middle school student can learn and understand ML, even when one had no prior knowledge or interest in science related careers. The findings have implication for pedagogical design of AI instruction in middle school context. We discuss the implication of our results for researchers and relevant stakeholders, highlight the limitations and chart future work paths.

The students’ assessment regarding collaborative learning and workgroups is being reported as one of the main concerns in higher education. The increased technological evolution leads to adapting novel self and peer assessment methods to e-assessment. This paper reports the results of a study to evaluate students’ opinion about their experience with an e-assessment tool, WebAVALIA, and its assessment criteria. The results indicate that students (N = 359) consider the tool fair, and it increases the productivity regarding work development. Kruskal-Wallis tests show that the students recently considered WebAVALIA fairer and more straightforward. Quickness and anonymity are also identified as tool advantages.

This case study outlines the development and utilization of a Mobile Augmented Reality (MAR) application to teach an asynchronous online lesson on e-commerce. The MAR technology was leveraged primarily to enhance the presentation of online learning materials, add interactivity to the learning process, and enable students to access the lesson from anywhere via their ubiquitous mobile devices. Two groups, comprising a total of 105 business students, participated in the online lesson during the COVID pandemic. Students' experiences of the implementation were captured through an online survey to find out how the MAR technology was perceived, and what were the benefits and challenges of using the technology. This study contributes to the research on the use of augmented reality in education and offers practical recommendations for teachers to consider when designing and implementing MAR online lessons, including the importance of learner-centered design, careful guidance on technology use, and encouragement of student interaction. The study concludes that augmented reality is a useful tool for improving learning materials and asynchronous online learning practices and MAR applications can be effective for learning with compact materials and micro-credentials.

Machine learning (ML) literacy has recently been identified as one of critical skills students need to succeed as future creators and innovators. While the significance of introducing ML basics at kindergarten to twelfth grade (K-12) levels is increasingly acknowledged, there is limited research that focuses specifically on collaborative design of ML applications with middle school students. We posit that engaging young children to co-invent and make concrete prototypes improves their ideas, encourages them to become active participants, and allows them to establish the implications of the technology in their everyday lives. In order to lay the foundation for middle school ML education, we collaboratively designed and prototyped ML applications with 43 eighth grade students (ages 11 to 14) in a Nigerian school. The ideas generated by the students indicate that they began to identify the applicability of ML to their daily lives and as a solution to a plethora of societal challenges. This study provides learners’ input and preliminary insights into approaches that could be adopted to promote ML within the compulsory level of education in an African setting. The research contributes to the limited body of knowledge available on effectively teaching ML to young learners using design-oriented pedagogy, especially in the context of an emerging country.

Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students’ understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students’ CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students’ CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

A critical aspect of designing and running online study programs is the identification of factors and elements that could potentially threaten the continuation of studies. In this study, we first identified a set of critical events that occurred in the running of a Finnish online doctoral study program over 16 years. Next, we analyzed the events using a four-pillar sustainability model, which consisted of the economic, social, environmental, and ethical pillars. We detected several contextually relevant and dynamic pivotal factors related to each of the pillars, which had effects on the sustainability of the program at the time of the critical events. The analysis revealed that positive pivotal factors in one sustainability pillar can be used to compensate for negative pivotal factors in the other pillars. Two aspects that were crucial for the sustainability of the online doctoral study program were the resilience and shared commitment of the community involved in its activities, which helped in overcoming any challenges encountered. Based on this study, we recommended that future research should design novel solutions that help online study programs to proactively identify potential critical events and related pivotal factors. Furthermore, studies should find creative approaches for constructively coping with critical events that have been identified.