BACKGROUND AND PURPOSE

One way to gain a new perspective and inspiration for one's teaching practice is to go on a teaching sabbatical and teach or co-teach in a new context at another institution. Awarding or sending faculty on sabbaticals is an old practice and, at some institutions, a well-integrated part of faculty development (Kang & Miller, 1999).

WORK DONE

All authors have been fortunate to go on teaching sabbaticals funded by The Swedish Foundation for International Cooperation in Research and Higher Education (STINT,2025). Emma Riese was at Arizona State University 2024; Ric Glassey at the National University of Singapore 2023; Tomas Ekholm at Williams College 2019; Olle Bälter at Williams College 2008; Viggo Kann at Amherst College 2006; all in the fall semester.

LESSONS LEARNED

Going on a teaching sabbatical gave us all new perspectives and time to reflect. While moving to another country requires planning and determination, we all agree that it was definitely worth it! The experiences have broadened our perspectives, shaped us, and influenced our practices. Below are short descriptions of our journeys:

Viggo started to do research in Computer Science Education, and implemented several changes at KTH inspired by Amherst College. For example he changed the KTH cultureof not erasing the blackboard after each lecture, and he switched to holding one-hourinstead of two-hour lectures (Kann, 2010).

Olle changed his research area to Technology Enhanced Learning. Together with Viggo,they founded Cerise 1 , the CS Education research group. The sabbatical was a determining factor for the visiting scholarship at Stanford Graduate School of Education 2015-16. There he picked up the ideas on Question-Based Learning, later improved together with Ric to pure Question-Based Learning (Bälter et al., 2024).

Ric used the sabbatical to dive into learning science and the desirable and undesirable difficulties in learning. He was also able to reflect on other approaches to managing scale and quality against the rise of Gen-AI. This has led to a series of studies on how KTH might leverage AI to enhance our learning environment (e.g. Fayaz et al., 2025).

Emma’s biggest takeaway was how working as a teaching team created a supportive work environment for instructors and teaching assistants while ensuring students across all course sections got a similar student experience. She also had the opportunity to collaborate on training for teaching assistants (ASU, 2025).

Besides teaching a new course, Tomas took the opportunity to sit in on several courses with different teachers. It was a privilege to have time for this, while also having time to reflect.

TAKE-HOME MESSAGE

(1) Go on exchanges and teach! If you can, bring the rest of the family; it is a wonderful adventure. (2) There is much more to a teaching sabbatical than teaching! Reach out to the local pedagogical developers and engage to help further develop your own pedagogy. (3) It is an opportunity to say ‘yes’; to all the serendipitous meetings, seminars, and workshops that are a ‘no’ under the normal workload at home. (4) Absence makes the heart grow fonder! The grass may not be greener on the other side; however, having some distance from your typical environment can make you appreciate what you have and renew your efforts.

ACKNOWLEDGEMENTS Thank you to The Swedish Foundation for International Cooperation in Research and Higher Education, STINT, for funding our teaching sabbaticals!

REFERENCES

Arizona State University, Ira Fulton Schools of Engineering, Learning and Teaching Hub,Teaching Assistants onboarding and beyond, URL: https://lth.engineering.asu.edu/referenceguide/teaching-assistants-onboarding-and-beyond/

Bälter, O., Glassey, R., Jemstedt, A., & Bosk, D. (2024). Pure Question-Based Learning.Education Sciences, 14(8). https://doi.org/10.3390/educsci14080882

Kann, V. (2010). Kan kvalitet på ett elitcollege föras över till svenska förhållanden? NU 2010.URL: https://suhf.se/static/2010/2010/konferensbidrag/Pass5_Kan_kvalitet_pa_ett_elitcollege_foras_over_till_svenska_forhallanden.pdf

Kang, B., & Miller, M. T. (1999). An Overview of the Sabbatical Leave in Higher Education: A Synopsis of the Literature Base.

The Swedish Foundation for International Cooperation in Research and Higher Education, STINT, Teaching sabbatical, URL: https://www.stint.se/en/program/teaching-sabbatical

Avid Fayaz, Richard Glassey and Alexander Baltatzis. 2025. Generating Personalized Assignments with Students in the Loop. In Proceedings of the 2025 on Innovation and Technology in Computer Science Education (ITiCSE 2025).

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

We present a system that enables students to generate their own programming assignments that are both personalized to their interests and aligned with the course objectives. A group of twelve undergraduate students randomly selected from an introductory programming course (n approximate to 200) received training and access to the system for six weeks. Students were free to use the system to generate assignments or take regular assignments instead. Weekly student feedback was used to refine the system through three iterations, and a focus group with three students was held after the course to gather opinions on the experience. Our findings are complicated and cautionary: students appreciated generating their own personalized assignments with feedback on demand. However, the cognitive load of having to generate and decide which assignment to complete, along with the variation of difficulty and alignment with learning objectives were noted as problematic and discouraging. Perhaps the most negative but heartwarming result is that students missed the community aspect of independently solving and collectively discussing solutions to a common assignment.

Characterising code quality is a challenge that was addressed by a previous ITiCSE Working Group (Börstler et al., 2017). As emerged from that study, educators, developers, and students have different perceptions of the aspects involved. The perception of code quality by CS1 students develops from the feedback they receive when submitting practical work. As a consequence of increasingly large classes and the widespread use of autograders, student code is predominantly assessed based on functional correctness, emphasising a machine-oriented perspective with scarce or no feedback given about human-oriented aspects of code quality. Such limited perception of code quality may negatively impact how students understand, create, and interact with code artefacts. Although Börstler et al. concluded that "code quality should be discussed more thoroughly in educational programs", the lack of materials and time constraints have slowed down progress in that regard. The goal of this Working Group is to support CS1 instructors who want to introduce a broader perspective on code quality in their classroom, by providing a curated list of examples and activities suitable for novices. In order to achieve this goal, we have extracted from the CS education literature a range of examples and activities, which have then been analysed and organised in terms of code quality dimensions. We have also mapped the topics covered in those materials to existing taxonomies relevant to code quality in CS1. Based on this work, we provide: (1) a catalogue of examples that illustrates the range of quality defects that could be addressed at CS1 level; and (2) a sample set of activities devised to introduce code quality to CS1 students. These materials have the potential to help educators address the subject in more depth.

Recent research has shown the great potential of automatic feedback in education. This paper presents SOBO, a bot we designed to automatically provide feedback on code quality to undergraduate students. SOBO has been deployed in a course at the KTH Royal Institute of Technology in Sweden with 130+ students. Overall, SOBO has analyzed 1687 GitHub repositories and produced 8443 tailored code quality feedback messages to students. Unlike traditional tools embedded in CI pipelines, SOBO is designed to interact with students in a way that promotes personalized learning without imposing additional teaching burdens. The quantitative and qualitative results indicate that SOBO effectively nudges students into adopting code quality best practices, without interfering with pedagogical objectives. From this experience, we provide guidelines on how to design and deploy teaching bots in programming courses.

Technology-enhanced learning (TEL) is a research field occupied with how teaching and human learning can be supported through the help of digital tools for increased efficiency, effectiveness, learnability, and pedagogical values by applying verified learning theories supported by analyses of the data generated by the students’ activities. Research in TEL is closely related to a social mandate that is becoming eminent in education nowadays: digitalizing education in an accessible, ethical and sustainable way. Most literature on TEL has focused on technological aspects, pedagogical approaches, ethical considerations or accessibility concerns in isolation, often within different research communities. Also, with generative AI's broad and unpredictable impact, these gaps could widen further. This commentary paper aims to bridge these gaps by offering an integrated perspective addressing all three aspects—technology, pedagogy, and accessibility—while examining intersections and implications from multiple viewpoints. From a historical perspective, various educational technologies have facilitated the scaling of different pedagogies and contributed to students' understanding by enhancing personalized learning, expanding visualization possibilities, and improving access to learning materials. While television and radio enabled remote learning, technological advancements in recent decades have significantly increased accessibility, such as radio and TV learning programs, to the emergence of e-learning platforms, adaptive learning systems, and artificial intelligence-driven educational tools. However, it is essential to acknowledge that, despite these advancements, technology-supported educational tools often remain more accessible to learners from developed countries or those with a high socio-economic background who can afford the costs and possess the necessary skills for effective use of these tools.

The aim of this work is to describe a simple and cost effective way to integrate generative AI into GitHub to support course specific scenarios. We are motivated by helping teachers realise their creative AI use cases in spite of technical barriers and also to ensure that students have a blessed and fair way to access AI services without needing to sign-up, prompt or pay. First we will describe a scenario that we have implemented for our own CS1 course, then we will describe the technical requirements for implementation. We finish off with our early thoughts on where these types of scenarios might be heading in terms of supporting computing education.

Characterising code quality is a challenge that was addressed by Börstler et al.’s working group in 2017. As emerged from their study, educators, developers and students have different perceptions of the manifold aspects involved, and a major conclusion of that WG was that “code quality should be discussed more thoroughly in educational programs” [2, p. 70]. However, the lack of materials and the time constraints have slowed down progress in that regard. The goal of this working group is to propose manageable ways to address code quality in the CS1 classroom, with a particular focus on activities that help students become aware of and improve the quality of their code. To achieve this goal, we will (a) extract from the literature a comprehensive set of quality issues which will then be classified according to the appropriate strategies to fix them; and (b) circulate a survey to explore the instructors’ views on code quality issues and the way they deal with (or ignore) them. Based on this work we aim to produce: (1) a taxonomy of code quality issues with associated examples, as well as (2) a sample set of teaching materials to introduce those issues to CS1 students.