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Risk management strategy for generative AI in computing education: how to handle the strengths, weaknesses, opportunities, and threats?
The idea of Artificial intelligence (AI) has a long history in both research and fiction and has been applied in educational settings since the 1970s. However, the topic of AI underwent a huge increase of interest with the release of ChatGPT in late 2022, and more people were talking about generative AI (GenAI or GAI). According to some estimates, the number of publications on generative AI increased with 2269.49% between 2022 and 2023, and the increase was even higher when related to computing education. The aim of this study is to investigate the potential strengths, weaknesses, opportunities, and threats of generative AI in computing education, as highlighted by research published after the release of ChatGPT. The study applied a scoping literature review approach with a three-step process for identifying and including a total of 129 relevant research papers, published in 2023 and 2024, through the Web of Science and Scopus databases. Included papers were then analyzed with a theoretical thematic analysis, supported by the SWOT analysis framework, to identify themes of strengths, weaknesses, opportunities, and threats with generative AI for computing education. A total of 19 themes were identified through the analysis. Findings of the study have both theoretical and practical implications for computing education specifically, and higher education in general. Findings highlights several challenges posed by generative AI, such as potential biases, overreliance, and loss of skills; but also several possibilities, such as increasing motivation, educational transformation, and supporting teaching and learning. The study expands the traditional SWOT analysis, by providing a risk management strategy for handling the strengths, weaknesses, opportunities, and threats of generative AI.
Journal Article
Digital skills : unlocking the information society
\"Digital Skills systematically discusses the skills or literacies needed in the use of digital media, primarily computers and the Internet. Following the work of van Dijk's, The Deepening Divide: Inequality in the Information Society, it uses conceptual analysis and empirical observations to show what digital skills are, how they are distributed, how skill inequalities develop, and how these inequalities can be remedied by designers, educators, policymakers, and different types of Internet users\"-- Provided by publisher.
Book
Culturally Responsive Debugging: a Method to Support Cultural Experts’ Early Engagement with Code
Despite the value that cultural experts bring to efforts to broaden the participation of racially minoritized youth in US computer science, there has been little research on supporting their knowledge of computing. This is a missed opportunity to explore the diffusion of computing knowledge across local community contexts where underrepresented youth of color spend time. To address this gap, we present one strategy for promoting cultural experts’ early engagement with code, culturally responsive debugging: using culturally situated expertise and knowledge to debug code. We analyzed qualitative data from a professional development workshop for cultural experts to evaluate this strategy. Our findings have implications for broadening participation efforts and supporting non-programmers’ knowledge of code.
Journal Article
Computational thinking in computer science teacher training courses in Brazil: A survey and a research roadmap
The adoption of computational thinking in the classroom has been growing in the last years. Its use needs to be supported by the correct digital technologies and teaching methods, and for this, is required, capable teachers. This work aims to understand how computational thinking is addressed by Computer Science Teacher Education courses in Brazil, and which digital technologies and teaching methods are used to foster it. A survey was conducted, and a roadmap was built. Main obtained results are: Common and accessible technologies, used in everyday life, can help promote computational thinking; Researchers and teachers can explore the list of technologies surveyed and categorized to promote computational thinking; Teachers can analyze the teaching methods used and understand how these methods are applied in the teaching process; The teachers and researchers can use and explore the best technologies identified in the paper, to foster each computational thinking characteristic. Moreover, it is essential to enhance the knowledge about computational thinking, to apply the correct digital technologies and teaching methods in its promotion.
Journal Article
An Ethnomethodological Study of Abductive Reasoning While Tinkering
Tinkering is often viewed as arbitrary practice that should be avoided. However, tinkering can be performed as part of a sound reasoning process. In this ethnomethodological study, we investigated tinkering as a reasoning process that construes logical inferences. This is a new asset-based approach that can be applied in computer science education. We analyzed artifact-based interviews, video observations, reflections, and scaffolding entries from three pairs of early childhood teacher candidates to document how they engaged in reasoning while tinkering. Abductive reasoning observed during tinkering is discussed in detail.
Journal Article
Education and technology : key issues and debates
\"Will technology replace the school and university? Will technology replace the teacher? What do we really know about technology and learning? Does technology make education more individualized? What does the future hold for technology and education? What can be learnt from the history of technology use in education? In a thoroughly revised edition of this successful book, Neil Selwyn takes a critical look at some of the major current debates and controversies concerning digital technologies and education. Focusing on the social as well as the technical aspects of these issues, Selwyn addresses fundamental but often unvoiced questions about education and technology. Over the course of eight chapters, the book gives careful thought to the people, practices, processes and structures behind the rapidly increasing use of technologies in education, with an emphasis on the implications of digital technologies for individuals and institutions. The book focuses attention on the connections between recent technology developments and broader changes in education practice, education policy and education theory over the past 10 years. It also challenges us to reflect on future directions and controversies for education in the (post)digital age. Expanded study questions, annotated further reading and a new glossary of key terms are included to support readers. An updated companion website links to case study examples, two bonus chapters and much more for students and lecturers.\"-- Provided by publisher.
Book
A review of AI teaching and learning from 2000 to 2020
In recent years, with the popularity of AI technologies in our everyday life, researchers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical models, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the university level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collaborative project-based learning approaches, featuring activities like software development, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaffold students’ AI understandings. With suitable teaching tools and pedagogical support in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the latest educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.
Journal Article