Collaboration is a crucial part of computing education. The increase Khan [28] has proposed an inspiring vision of how AI could in AI capabilities over the last couple of years is bound to profoundly help realize personalized individual tutors for every learner. Complementing affect all aspects of systems and software engineering, including this, an expert panel from 2020 [49] draws a scenario collaboration. In this position paper, we consider a scenario where where "AI supports orchestration of the multiple types of activities, AI agents would be able to take on any role in collaborative processes learning partners, and interaction patterns that can enrich a classroom". in computing education. We outline these roles, the activities We believe the possibilities are even broader, and to help and group dynamics that software development currently include, think about them, we propose a thought experiment that not only and discuss if and in what way AI could facilitate these roles and accommodates emerging practices and visions but also suggests activities. The goal of our work is to envision and critically examine new use cases in education that (to the best of our knowledge) have potential futures. We present scenarios suggesting how AI not yet been explored.

This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required.

This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of GenAI use on their coding experience, learning, and self-efficacy. Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics. The analysis of the learning aspects is distinguished by the application of learning and pedagogy informed lenses to discuss the data. We propose a preliminary design space for GenAI-based programming learning tools highlighting the importance of considering the roles that GenAI can play during the learning process, the varying support-ability patterns that can be applied to each role, and the importance of supporting transparency in GenAI for team members and students in addition to educators.

Over the past few decades, computing has emerged as a transformative discipline, significantly reshaping the world. Virtually all knowledge areas now use computing to enhance their applicability, and even everyday activities often require some computing knowledge. Recent advancements in artificial intelligence (AI) have highlighted the profound impact computing has on our lives to everyone. Therefore, it is desirable that students of all levels have age-appropriate familiarity with computing.5,6 Several international efforts, including frameworks and reports, have emerged to address the development of early computational skills.

Last year, 18 percent of Stanford University seniors graduated with a degree in computer science, more than double the proportion of just a decade earlier. Over the same period at MIT, that rate went up from 23 percent to 42 percent. These increases are common everywhere: The average number of undergraduate CS majors at universities in the U.S. and Canada tripled in the decade after 2005, and it keeps growing. Students' interest in CS is intellectual--culture moves through computation these days--but it is also professional. Young people hope to access the wealth, power, and influence of the technology sector. That ambition has created both enormous administrative strain and a competition for prestige.

This study investigates the application of large language models, specifically GPT-4, to enhance programming education. The research outlines the design of a web application that uses GPT-4 to provide feedback on programming tasks, without giving away the solution. A web application for working on programming tasks was developed for the study and evaluated with 51 students over the course of one semester. The results show that most of the feedback generated by GPT-4 effectively addressed code errors. However, challenges with incorrect suggestions and hallucinated issues indicate the need for further improvements.

Across the African continent, students grapple with various educational challenges, including limited access to essential resources such as computers, internet connectivity, reliable electricity, and a shortage of qualified teachers. Despite these challenges, recent advances in AI such as BERT, and GPT-4 have demonstrated their potential for advancing education. Yet, these AI tools tend to be deployed and evaluated predominantly within the context of Western educational settings, with limited attention directed towards the unique needs and challenges faced by students in Africa. In this book chapter, we describe our works developing and deploying AI in Education tools in Africa: (1) SuaCode, an AI-powered app that enables Africans to learn to code using their smartphones, (2) AutoGrad, an automated grading, and feedback tool for graphical and interactive coding assignments, (3) a tool for code plagiarism detection that shows visual evidence of plagiarism, (4) Kwame, a bilingual AI teaching assistant for coding courses, (5) Kwame for Science, a web-based AI teaching assistant that provides instant answers to students' science questions and (6) Brilla AI, an AI contestant for the National Science and Maths Quiz competition. We discuss challenges and potential opportunities to use AI to advance science and computing education across Africa.

Challenges and opportunities faced by computing educators and students adapting to LLMs capable of generating accurate source code from natural-language problem descriptions.

It takes a village: Supporting inquiry- and equity-oriented computer science pedagogy through a professional learning community.

The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning. Very recent advances in artificial intelligence have resulted in code generation models that can produce source code from natural language problem descriptions -- with impressive accuracy in many cases. The wide availability of these models and their ease of use has raised concerns about potential impacts on many aspects of society, including the future of computing education. In this paper, we discuss the challenges and opportunities such models present to computing educators, with a focus on introductory programming classrooms. We summarize the results of two recent articles, the first evaluating the performance of code generation models on typical introductory-level programming problems, and the second exploring the quality and novelty of learning resources generated by these models. We consider likely impacts of such models upon pedagogical practice in the context of the most recent advances at the time of writing.