Generative Artificial Intelligence (GenAI) has come to stay, and educators are exploring its usage in diverse contexts. One pertinent question begging for an answer is how educators integrating GenAI tools can equitably assess students' learning outcomes. This study explores the mixed-method approach, consisting of a rapid literature review and an analysis of experts' perspectives to address this question. We analyze data from the Scopus and Web of Science databases from the rapid review to understand how the use of GenAI is penetrating the STEM field. On the other hand, the thematic analysis of data generated from a ten-week-long group learning circle discussion among STEM professors regarding assessment in the era of the GenAI was used to gain understanding of educators' perspectives regarding how students' learning could be assessed in a classroom where GenAI tools are used. Our findings provide insights regarding how, where, and when to integrate GenAI in STEM classes and potential assessment strategies that could foster trust and transparency between educators and students. This study contributes to the growing body of literature on GenAI in STEM education. It offers implications from the perspective of contextual adoption of assessment strategy in the era of GenAI rather than the traditional approach of one-size-fits-all.

Extending computer science (CS) education to address inclusion, diversity, and equity in all settings can broaden the participation of underrepresented groups including the adult education. Recently, studies have examined CS education at elementary and college levels, however, little is known in the context of adult education. This study systematically investigates past studies on computing education research in adult education (formal or informal) through the lenses of a literature review. The study sought to understand: (i) how research in this domain has evolved over the years; and (ii) what impact - in terms of learning outcomes - has been reported in the literature. Data were collected from three databases including the ACM digital library, Scopus, and Web of Science. Findings from this study show that despite CS in adult education had started since the 1980s, there is little scholarly progress and advancement witnessed in this domain. In addition, indicators measuring the impact of broadening participation in CS education among adults appear insignificant. Further, the learning outcomes reported in CS education research for adults includes motivation, increased interest, self-confidence, and computing knowledge. This study revealed several gaps and draws scholar's attention to broadening participation in Adults' CS education, highlighted study implications and future research agenda.

Today's technological advancements in mobile technologies and the growing number of mobile devices are extremely beneficial in the mobile learning process. This study is a work in progress that discusses the possibilities of integrating Augmented Reality (AR) and computer vision (CV) into science education which uses deep learning to detect animals in real-time and teach children to classify animals, and learn about their habitat, sound, and important facts. In this study, the Design Science Research (DSR) is used which is a pragmatic approach to creating substantial knowledge for problem-solving through the development of artifacts. The mobile application - AnimalCircle - was developed following the DSR method, and initial users' study was conducted to investigate the efficacy of the use of AR and CV in mobile learning on children's science education and if it can enhance children learning experience. Semi-structured interviews were conducted with children studying at primary level class in Kathmandu district of Nepal between the age groups of 6–12. The findings show that children are positive toward usage of AnimalCircle app in their learning process because they find it beneficial and effective in their learning. However, most children also found it difficult and complained of getting confused during their initial usage. Therefore, significant efforts are required to improve the usage of these technologies in the mobile app and to provide a child-friendly learning experience.

Artificial Intelligence (AI) for K-12 education has been considered a global initiative. However, evidence of Africa’s inclusion in globalization across schools is lacking in the literature. Besides, resources, including materials and content, are developed across Hong Kong, Japan, Europe, and the USA. These suggest that contextualized resources are effective for AI implementation in schools. Since appropriate pedagogical approaches, sound instructional methods, materials, tools, and activities familiar to the student for instruction lead to effective learning, we embark on a literature survey to unravel the approaches and kind of AI resources utilized across contexts. A systematic literature review methodology was used in this paper to understand the trends of teaching AI at the K-12 educational level. Scientific databases such as IEEE, ACM, Web of Science, and Scopus were searched to gather relevant literature in tandem with our research aim. Out of the 451 articles that were retrieved, only 54 fit well into the inclusion criteria and were reviewed for further analysis. This study revealed several existing approaches and resources used to teach AI in schools.

This research paper examined theories, pedagogics, and contents explored by studies focusing on promoting computational thinking (CT) in higher education institutions (HEIs). CT has become a fundamental approach to building problem-solving skills, which requires a thought process. The field of CT is generally still maturing, and the use of CT as an approach to introduce freshmen to introductory programming courses in HEIs has been gaining scholars' interest in the recent past. To appreciate the strategies explored to promote teaching and learning of CT in HEIs; evaluate scholarly discussions, contributions, and potential impact of studies in this field, there is a need to ground the theoretical constructs that build the foundation for the field. A literature review methodology was adopted in this study. The data collected from the Web of Science, the Scopus, ACM, and ProQuest databases were analyzed to provide answers to the research questions. The findings from this study suggest that constructionism and constructivism are the prevailing learning theories explored by scholars in this field to deploy CT in HEI. Additionally, the study revealed that activity-based learning, problem-based learning, automatic assessment-based learning, and self-regulated or self-reflective learning are the prominent pedagogies used by educators. These findings provided a strong foundation for research in this growing field. Besides, the findings also create an opportunity for positioning CT in HEI's educational curriculum regarding how CT should be taught in that context.

The educational community continuously seeks ways to improve the learner-centered learning process through new approaches like Learning analytics and its dashboard, which is helpful to enhance the teaching and the learning process. It involves a process whose final goal is presenting results to support decision-making about improving the learning process. However, a descriptive Learning analytics interface for analyzing learning data of students, including the disadvantaged, where to view and interpret learners' historical data is -in general- missing in this research domain. Hence, more research is still required to establish the philosophy of learning analytics on inclusion with an interface for the stakeholders to understand learning and teaching in an inclusive learning environment. This paper fills this gap by providing an inclusive educational learning analytics dashboard to support teachers and students. This study aimed to present a learning analytics implementation in the context of a smart ecosystem for learning and inclusion. We gave the inclusive educational needs and discussed the workflow followed during the descriptive learning analytics dashboard development. Therefore, the study improved existing learning analytics dashboards with a descriptive approach and inclusiveness of students with disabilities. Owing to the software development nature of this study, agile methodology based on five stages was applied: requirement elicitation; data gathering; design and prototyping; implementation; and testing and integration. We performed an initial evaluation, which indicated that the dashboard is suitable for understanding teachers' and students' needs and expectations. Besides, the visualization of inclusive learning characteristics improves engagement and attainment of learning goals.

The benefits of teaching machine learning to K-12 pupils include building foundational skills, useful mental models and inspire the next generation of AI researchers and software developers. However, introducing machine learning in schools has been a challenge even though several initiatives, curriculum design, platforms, projects, and tools exist to demystify the concept. The existing resources are scattered and sometimes overlap. Thereby selecting the appropriate tools to adopt in teaching becomes an arduous task for the teachers and other practitioners. More so, despite the increasing number of papers published in this field, there are still gaps in identifying specific tools and resources for teaching machine learning in K-12 settings. This study presents a literature review on machine learning in K-12 by selecting articles published from 2010 to 2021. Therefore, this paper presents a resource catalog and surveys of tools to help teachers find suitable teaching paths and make the decision to introduce activities that help students understand the basic concepts of machine learning. Based on the research objective, we utilized six databases to extract relevant information, while thirty-nine peer-reviewed articles were collected based on a systematic literature search and were analyzed. This study identified resources, tools, and instructional methods as the main categories of pedagogical items needed to ensure impactful teaching of machine learning in K-12 settings. Besides, the mode of operation, benefits and the challenges of the pedagogical tools for teaching machine learning in K-12 settings were unraveled. The findings also show the increased number of initiatives resulting in tools development to support machine learning teaching. Finally, this study provides recommendations for future research directions to help researchers, policymakers, and practitioners in the education sector identify and apply various resources to aid decision-making in practice and to democratize machine learning practices in schools.

This study is a work in progress that aims to design and implement a smart learning environment based on virtual reality technology to aid the teaching and learning of programming concepts. The paper followed the approach of designing and modelling of requirement specification for the intended smart platform. This modelling approach is desirable in satisfying activities that engender the design and prototyping of the smart learning environment based on the design science research method. The study discusses the proposed architecture of the system, modelled the system with UML, presents a scenario-based model for teaching and learning of programming concepts, and connect the outcome to the future research.

This paper investigates the impact of puzzle-based learning technique (PbLT) for teaching introductory programming in the context of Nigeria. The participants in the study were computer science students at Usmanu Danfodiyo University, Sokoto, Nigeria. The study adopted the quantitative research method. The study shows that PbLT has a positive impact on the students' understanding of introductory programming. In addition, the students confirmed that MobileEdu-Puzzle learning application was supportive and easy to use. Nonetheless, the result revealed that information technology infrastructure especially internet connectivity is a major challenge that may hinder the use of mobile learning technologies such as MobileEdu-puzzle. The overall outcome shows that PbLT holds a lot of promise towards enhancing students learning experiences.