Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be “lying” in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where assessments have long been challenging due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, a study was conducted where potential opportunities and threats of ChatGPT for programming education were investigated. The question to answer was: What will the consequences be for programming education? 

The study applied a methodological approach inspired by action research and analytic autoethnography to investigate, experiment and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. 

Findings of the study include several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be lying in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT will have a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. 

In a survey of the CS-education community, we find a range of beliefs about the "geek gene" theory. We suggest an alternative term, the "geekiness quotient (GQ)". The GQ, grounded in Gardner's work on multiple intelligences, is a hypothetical measure of the student's current CS ability. The GQ supports a moderate view of the geek gene: that students arrive in our classrooms with a range of CS abilities, whether acquired through background or innate talent, and can improve their abilities through effort.

In a survey of the computing education community, many respondents suggested "active learning" as a teaching approach that would increase the likelihood of student success. In light of these responses, we analyze the way in which active learning is described in the computing-education literature. We find a strong consensus that active learning is good, but a lack of precision in how the term is used, often without definition, to describe instructional techniques, rather than student learning. In addition, active learning techniques are often discussed as if they were all equally effective. We suggest that making clear distinctions, both between teaching techniques and active learning and among the teaching techniques, would be fruitful for both instructors and researchers. Finally, we propose some dimensions along which distinctions among techniques could usefully be made.

In previous work we proposed the idea of ‘threshold skills’ as a complement to threshold concepts. The definition of threshold concepts assumes that theoretical knowledge is paramount: gaining the understanding of particular concepts irreversibly transforms the learners. We noted, however, that mastering computing, like many disciplines, requires learning a combination of concepts and skills, and we suggested that this required further investigation. In this paper we examine the activity of designing software as a possible example of such a threshold skill. We looked at 35 software designs collected from students nearing graduation in computing courses, and see many of the characteristics of threshold skill and also of students being in liminal space. A close examination of the students’ designs leads to some useful implications for teaching this fundamental activity.

In this article, we address the question of why computing students choose to learn computing topics on their own. A better understanding of why some students choose to learn on their own may help us to motivate other students to develop this important skill. In addition, it may help in curriculum design; if we need to leave some topics out of our expanding curriculum, a good choice might be those topics that students readily learn on their own.

Based on a thematic analysis of 17 semistructured interviews, we found that computing students’ motivations for self-directed learning fall into four general themes: projects, social and peer interactions, joy of learning, and fear. Under these, we describe several more specific subthemes, illustrated in the words of the students.

The project-related and social motivations are quite prominent. Although these motivations appear in theliterature, they received greater emphasis from our interviewees. Perhaps most characteristic of computingis the motivation to learn to complete some project, both projects done for fun and projects required for schoolor work.

We propose ‘threshold skills’ as a complement to threshold concepts. The definition of threshold concepts assumes that theoretical knowledge is paramount: gaining the understanding of particular concepts irreversibly transforms the learners.

Mastering computing, like many disciplines, however, requires learning a combination of concepts and skills. Mathematicians learn to do proofs, musicians learn to play their instruments, and martial artists learn to make moves by doing these activities, not just intellectually understanding them. We propose some characteristics for threshold skills and outline implications for teaching and for future work.

Threshold concepts can be used to both organize disciplinaryknowledge and explain why students have diculties at cer-tain points in the curriculum. Threshold concepts transforma student's view of the discipline; before being learned, theycan block a student's progress.In this paper, we propose that in computing, skills, inaddition to concepts, can sometimes be thresholds. Somestudents report nding skills more dicult than concepts.We discuss some computing skills that may be thresholdsand compare threshold skills and threshold concepts.

We report preliminary results from an ongoing investigation of how computing professionals perceive and value selfdirected learning, based on a qualitative analysis of interviews with ten computing professionals. The professionals expect that future colleagues will have a well-developed ability to learn on their own. They indicate that professionals use a range of resources, strategies, and collaborators to help them learn. They find their work-related self-directed learning enjoyable, expressing a sense of confidence and pride; yet they often also find it to be a stressful never-ending process.