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.

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.

The paper presents a study were drivers and barriers for increased use of Technology Enhanced Learning in higher education were identified. The method included focus groups with Faculty Pedagogical Developers at KTH Royal Institute of Technology, followed by a Force Field Analysis. Ten drivers and ten barriers were identified, and are presented in this paper. The most significant drivers found were: collegial discussions, increased automatization, Technology enhanced learning support for the teachers (to assist exploration), tech savvy students and engagement among faculty. The most significant barriers identified were: unclear return on time investment, insufficient funding for purchases and lack of central decisions. The analysis also revealed that some drivers and barriers could act both ways. One example is locally developed systems which are understood to be drivers when it comes to solving (local) problems and encouraging experimentation with IT systems, but when these local systems are cancelled due to lack of funding, or for example replaced by centralized systems, they discourage use and development. The findings constitute a foundation for future discussions about change processes to increase utilization of technology enhanced learning in higher education.

The potential of technology enhanced learning (TEL) can have both pedagogical and administrative benefits. In a previous study, we investigated the drivers and barriers for TEL in higher education using Force Field Analysis (FFA). In this follow-up study, we collected new data through a questionnaire to a group of pedagogical developers and at a presentation at a university internal conference for teachers. A Kruskal Wallis test was carried out to test if the groups filling out questionnaire deviated from each other in their ranking. A comparison was also done to the scores in the previous study. As a result of this triangulation, deviations were found between ratings for seven of the 20 identified forces. While the assessments of strengths in FFA is debated, we argue that each group’s view is an important component to understand the situation, and triangulation of data is helpful in understanding the different views.