The widespread availability of generative tools has weakened a long-standing assumption in computing education: that the production of working code can serve as a proxy for student competence. In resource-constrained settings, these tensions are compounded by intermittent power, high data costs, and emergent institutional governance. We report a two-site qualitative study of Nigerian computing departments (n = 20), drawing on semi-structured interviews with students and academic staff and analysing the corpus through thematic analysis to characterise assessment practice under policy-light conditions. Our findings describe a persistent detection trap, where staff rely on inconclusive software or subjective judgement, and institutional silence, where expectations for acceptable use are unevenly specified and applied. We contribute the Scaffolded AI-Verification Framework (SAVF), presented as a traceable design pattern catalogue of handset-first, low-data feasible teaching moves derived from these stakeholder accounts. SAVF comprises (i) permitted-help statements with disclosure, (ii) process-evidence bundles that foreground explanation and testing, and (iii) course-anchored prompts that require adaptation to local materials and constraints. We provide three pattern specifications, a traceability table linking themes to requirements and patterns, and adoption guidance for low-bandwidth implementation, positioning SAVF as a stakeholder-informed design contribution with a testable evaluation plan for future in-situ study rather than as an evaluated intervention.

In this Experience Report, we describe a semester-long course in which university students develop original software products to teach K--12 learners ideas in artificial intelligence (AI) and machine learning (ML). The university students develop their knowledge of AI/ML, build expertise in software development, and gain skills in education research. We test the educational software tools with K--12 learners at ''AI Expos'' held at partner public schools. We teach our university students about human-subjects research and collaboratively obtain IRB approval for the school-based work. We develop pre/post surveys and conversation questions for the K--12 participants. Our students instrument their software tools to gather live interaction data from the K--12 student use. The university students complete final course papers which describe their tool design and present evidence of student understanding of AI/ML concepts based on the data they have gathered. Students of the course often continue their scholarship beyond the semester, successfully submitting their work to conferences. We are developing a growing collection of software tools to teach AI/ML that are available for use by curriculum developers. For many students, the course provides their first opportunity to create a working software product used by others; seeing others use one's system is deeply satisfying and motivational. The course also provides a full-cycle research experience, including experimental research design, data collection, analysis, and writeup; nearly all students gain their first introduction to educational research in the course. This paper presents the university course design and our insights from teaching four iterations of the course.

Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. This working group will consider the ethical and societal impacts of GenAI in higher computing education. In this paper, we outline the goals, methodology and expected deliverables of the working group. In particular, we will carry out a systematic literature review to address a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and social impacts, we will provide an evaluation of university policies on the adoption and guidelines for use of GenAI for computing education and develop a framework to outline the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for educational and professional codes of ethics.

This poster presents Teacher Education for Data Science (TEDS), a study conducted with a cohort of teachers who developed ways to incorporate new data fluency approaches to teaching and learning in multiple middle school subjects. In a four-session online professional development sequence, teachers learned how to use a web-based collaborative data visualization platform; developed a data-intensive unit for their existing middle school curriculum; implemented this unit with their students; and shared reflections with fellow teachers and the researchers. We describe how we supported the teachers in a successful co-design process. Data gathered included a pre/post survey; individual 30-minute post-interviews with each teacher; and teacher instructional artifacts. Teachers reported on student agency as they entering their data into the collaborative platform and how students found real-world import in their data. To accomplish more student fluency with data in the middle school, we recommend more such technology-supported curriculum integration approaches.

In today's digital era, a diverse and proficient cohort in the computing field is essential for nations to operate, advance, and stay competitive. However, challenges persist in introducing newcomers to computing, particularly in the first programming course (CS1). By exploring the factors affecting student performance in CS1, we can gain valuable insight into the variables influencing student learning and outcomes. This study explored the impact of visual art background on the programming achievements of first-year college CS1 students. Data were collected from two cohorts of CS1 students who participated in quasi-experimental studies conducted at two polytechnics in north-central Nigeria. These students were the treatment groups exposed to Scratch programming according to constructionist pedagogy. The findings indicate that students with visual arts backgrounds consistently outperformed their peers, regardless of gender. Furthermore, the study examined how age and academic achievement levels interact with visual arts proficiency to predict programming success, and found that visual arts proficiency plays a critical role in determining programming achievement, regardless of these demographic factors. Considering these findings, this study offers valuable insights for programming educators to develop inclusive and engaging introductory programming classes. Incorporating engaging visual art concepts and activities into CS0 or pre-college CS curricula could benefit all students, especially those new to computing. Visual art proficiency may also predict programming success, guiding educators to provide additional support.

This ITiCSE Working Group (WG) has two goals: first, to increase awareness of computing education research in the African countries, and second, to create and strengthen connections between computing education researchers in African countries and those in the larger computing education research community.

There is a worrying lack of published work in international venues about computer science education (CSE) in the Global South, especially in Africa. Despite the increasing number of African institutions offering various computer science degree programs to meet the growing demand for computing skills and practices, there is limited scholarly evidence in the CSE context. As a result, it is important to understand why there is low representation of the continent in computing education research (CER) space. In this paper, we provide insights into the factors hindering the growth of CER in Africa, including strategies that could be employed to increase participation. This study employed a qualitative approach through the use of interviews. This study gathered the thoughts of ten computing education researchers across different countries and regions of Africa. The findings uncovered African researchers' motivation for CER and challenges such as limited awareness, financial constraints, and inadequate institutional support. Proposed solutions include establishing collaborative networks, raising awareness, and providing funding opportunities to boost CER output in the region. This research provides valuable insights for CSE, contributing to inclusion, equity, and broadening participation in computing.

Artificial intelligence (AI) and machine learning (ML) have a deepening impact in our world. For empowered citizenship and career readiness, elementary and middle school students need to understand these technologies. This poster reports on five original interactive AI and ML software exhibits tested by 125 elementary and middle school students aged 7 to 14 years. Four themes emerged: Students recognized that AI and ML systems can process data from cameras (perception); they saw that these systems responded to their training input (trust); they appreciated the practical import of AI/ML systems (affective and cognitive attitudes); and students were introduced to models and modes (specialization).

Machine learning (ML) systems are increasingly in use in society. For young learners to be informed citizens and have full career potential it is important for them to understand these concepts. To support this learning, we created "ChemAIstry,'' an interactive software tool for children which demonstrates training and classification in machine learning. Students select which everyday items are safe to bring into a chemistry lab (e.g., a lab coat is safe; pizza is not). These selections serve as training input for a decision tree classifier. After training, students see how the trained model performs in classifying new objects. ChemAIstry was tested with 40 students aged 7 to 14 years at a public K?8 school. The software captured student selections during training. We analyzed these interactions to yield a "Correspondence Score,'' a measure of student understanding of the classification task. We screen-recorded student use of the software and audio-recorded our conversations with them during this use. Our analysis of these data indicates that students were able to understand the concept of model training, including that items were subsequently classified based on their training input. More than half of the student trials indicated that students correctly understood the task. This suggests ChemAIstry was effective in introducing students to these ideas in machine learning. We recommend continued development of related tools for curriculum integration of AI in K-8 education.

While there are multiple standards bodies that define characteristics of high-quality, there are limited guidelines on conducting equity-enabling research, particularly in the context of high quality and in computing education. As part of an ACM ITiCSE Working Group in 2023, we engaged in a concept analysis and structured literature review to identify high-impact practices for conducting both high-quality and equity-enabling education research. As a result of this work, we produced a set of guidelines across each major phase of research that integrates characteristics of high-quality education research with those that are necessary for producing research that is designed to honor and meet the needs of various subgroups of learners. Special emphasis is given to the role that the researcher plays in shaping the research based upon how the researcher's lived experiences, perspectives, and training influences their work. During this special session, we will review each set of guidelines and engage attendees in reflection and discussion of them and how they can use the guidelines to enhance their education research.

To investigate and identify promising practices in equitable K-12 computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being conducted and the lack of equity-focused research.

Data science and machine learning should not only be research areas for scientists and researchers but should also be accessible and understandable to the general audience. Enabling students to understand the details behind the technology will support them in becoming aware consumers and encourage them to become active participants. In this paper, we present instructional materials developed for introducing students to two key machine learning algorithms: decision trees and k-nearest neighbors. The materials were tested in a middle school's afterschool artificial intelligence program with four participating students aged 12 to 13. A combination of hands-on activities, innovative technology, and intuitive examples facilitated student learning. With hand-drawn decision trees and penguin species classifications, students used the algorithms to solve problems and anticipate other possible applications. We present the technology used, curriculum materials developed, and classroom structure. Following the guidelines from AI4K12 and introducing foundational machine learning algorithms, we hope to foster student interest in STEM fields.

In this poster we present "DoodleIt,'' an interactive web application that performs sketch recognition and an afterschool curriculum that teaches students the key concepts of convolutional neural network (CNN). With DoodleIt, students make simple line drawings on a canvas area and a previously-trained CNN identifies the object drawn. The application visualizes the different layers that are involved in the process of CNN, including a display of kernels, the resulting feature maps, and the percentage of match at output neurons. We used DoodleIt as a part of 18-hour curriculum to introduce students to artificial intelligence, machine learning, and data science. Our findings indicate that students were able to understand the functionality of the kernels and feature maps involved in the CNN to perform rudimentary image recognition.

It is undeniable that technological advancements have taken significant changes in the nature of work, resulting in the loss of many jobs and the emergence of new ones. As a result, information technology (IT) job seekers and professionals must be aware of changes required in their respective fields. This study identified and ranked the most-used technologies, such as programming languages, databases, operating systems (OSs), and collaborative tools, specific to twenty-three (23) different IT roles. Furthermore, this study advocates that individuals focus on critical skills required in various domains to achieve career success. Similarly, individuals with a set of valued technical skills may decide whether to change jobs or roles if they are interested in a position that requires their current competence. The findings of this study can be helpful to career counselors, instructors, students, professionals, recruiters, curriculum designers, and decision-makers.

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.

As artificial intelligence education (AI) continues to be integrated into the mainstream educational system across countries, cultural competence and ethical considerations should be emphasized to ensure effective AI learning. Literatures has established that integrating elements of cultural competence within technology mediums has helped students understand difficult topics from computer science concepts learned in class. It is also argued that student with an ethical orientation of AI education is more likely to learn more about impacts and implications of AI. Hence, this study was conducted to understand how students’ cultural competence, and ethics combine to influence AI content. We surveyed Nigerian high school students after an experimental teaching session. A total of 596 students provided useful responses for the analysis that was done using WarpLS software. We performed structural equation modelling to understand the relationship among the variables utilized in the study. The result shows that cultural competence and ethics significantly influence AI content. This study’s results further shows that the association between ethics of AI and AI content has the highest predictive value which emphases the vital role of ethics in AI learning. This study also tested school location differences in the research model and discovered that urban students’ perception is higher than their rural counterpart on the adopted variables in relation to AI content. Overall, the results suggest that stakeholders and educators should emphasize cultural elements and humanistic thinking as well as ethical considerations in the design of AI content and instructional materials. We discuss the findings and propose future directions.

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 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 research is interested in how to teach machine learning concepts to K-12 learners. There is limited evidence to support the teaching, learning, and usefulness of machine learning in K-12 settings, hence addressing the evident gap. This research aims to specifically identify pedagogical approaches with the underlying theories and methods in teaching K-12 machine learning as well as design, assess, and determine the impact of machine learning on student outcomes in K-12.

This research Full paper presents the pedagogies of machine learning in K-12. The new learning pedagogies and technologies are introduced with the aim of enhancing student engagement, experience and learning outcome. This study examined how machine learning has been taught in the recent past and further explores the ways and suitable approaches for K-12 context. Literatures on pedagogies associated with machine learning were reviewed to understand the dynamics and suitability of these pedagogies to support machine learning teaching. Though studies have explored pedagogies for machine learning in higher education context, few studies explored pedagogical strategies for teaching machine learning in K-12. In all, the pedagogies employed in teaching and learning of machine learning has not witnessed much research in literature. The pedagogical strategies revealed in the literature are mostly adopted in the higher education institutions to enable the of teaching machine learning concepts. The literature survey revealed several pedagogical strategies such as problem-based learning, project-based learning and collaborative learning used in higher education institutions. The revealed pedagogies suggest learners-centered approaches such as active learning, inquiry-based, participatory learning, design-oriented learning among others will be suitable for teaching machine learning in K-12 settings.

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.