Despite significant advances in educational technology and design methodologies, current educational games demonstrate a fundamental limitation: educators are unable to modify content after the games are deployed, limiting curriculum alignment and pedagogical customization. This paper introduces Imikathen-VR, a solution built upon a text-to-animation system, supporting K-1 and K-2 teachers to create minigames for their students to practice basic writing skills. Our implementation extends an existing animation pipeline by integrating a fine-tuned T5 model for sentence simplification, achieving 95% F1 BERT score and 76% ROUGE-L score in maintaining semantic and lexical fidelity. We improve visual reasoning by transforming the task of identifying missing visual details into a Masked Language Modeling problem. Preliminary results demonstrate the system's effectiveness in generating curriculum-aligned VR exercises, though comprehensive classroom testing remains pending. This work advances the integration of customizable VR technology in early education, providing teachers with enhanced control over educational content.

This paper investigates the feasibility of using Large Language Models (LLMs) to automate the grading of Unified Modeling Language (UML) class diagrams in a software design course. Our method involves carefully designing case studies with constraints that guide students’ design choices, converting visual diagrams to textual descriptions, and leveraging LLMs’ natural language processing capabilities to evaluate submissions. We evaluated our approach using 92 student submissions, comparing grades assigned by three teaching assistants with those generated by three LLMs (Llama, GPT o1-mini, and Claude). Our results show that GPT o1-mini and Claude Sonnet achieved strong alignment with human graders, reaching correlation coefficients above 0.76 and Mean Absolute Errors below 4 points on a 40-point scale. The findings suggest that LLM-based grading can provide consistent, scalable assessment of UML diagrams while matching the grading quality of human assessors. This approach offers a promising solution for managing growing student numbers while ensuring fair and timely feedback.

This study explores the role of teaching assistants (TAs) as assessors in a university’s computer science program. It examines the challenges and implications of TAs in grading, with a focus on their expertise and grading consistency. The paper analyzes grading experiences in various exam settings and investigates the impact on assessment quality. We adopt an empirical methodology and answer the research question by analyzing the data from two exams. The chosen exams have similar learning objectives but they differ in how TAs graded them, thus providing an opportunity to reflect on different grading styles. It concludes with recommendations for enhancing TA grading effectiveness, emphasizing the need for detailed rubrics, training, and monitoring to ensure fair and reliable assessment in higher education.

In our rapidly evolving technological landscape, AI tools have gained substantial power and integration across various domains. Through interviews and surveys conducted at a University in the Netherlands, we investigated students’ perceptions of AI tools. Our results show that students generally have a positive attitude towards the adoption of AI technologies and feel that it enhances their learning experience. Furthermore, this research project examines the capabilities of AI-powered tools, namely GitHub Copilot and ChatGPT, in solving a variety of university-level assignments. By empirically evaluating the capabilities of these AI tools and offering insights to educators, this research project aims to assist them in designing programming exercises that encompass essential learning processes while accounting for students’ utilization of AI tools. The findings indicate that a majority of the exercises currently utilized by the examined university could be solved partially or entirely with the aid of these tools. This project highlights the importance of educators understanding the capabilities of AI tools, as well as students’ attitudes towards them, to effectively adapt their teaching methods and promote essential learning goals.

In this paper we report on the outcome of a controlled experiment using one of the widely available and used online proctoring systems, Proctorio. The system uses an AI-based algorithm to automatically flag suspicious behaviour, which can then be checked by a human agent. The experiment involved 30 students, 6 of which were asked to cheat in various ways, while 5 others were asked to behave nervously but make the test honestly. This took place in the context of a Computer Science programme, so the technical competence of the students in using and abusing the system can be considered far above average. The most important findings were that none of the cheating students were flagged by Proctorio, whereas only one (out of 6) was caught out by an independent check by a human agent. The sensitivity of Proctorio, based on this experience, should therefore be put at very close to zero. On the positive side, the students found (on the whole) the system easy to set up and work with, and belie ved (in the majority) that the use of online proctoring per se would act as a deterrent to cheating. The use of online proctoring is therefore best compared to taking a placebo: it has some positive influence, not because it works but because people believe that it works, or that it might work. In practice however, before adopting this solution, policy makers would do well to balance the cost of deploying it (which can be considerable) against the marginal benefits of this placebo effect.

This study is a work in progress that aims to design and implement a smart learning environment based on virtual reality technology to aid the teaching and learning of programming concepts. The paper followed the approach of designing and modelling of requirement specification for the intended smart platform. This modelling approach is desirable in satisfying activities that engender the design and prototyping of the smart learning environment based on the design science research method. The study discusses the proposed architecture of the system, modelled the system with UML, presents a scenario-based model for teaching and learning of programming concepts, and connect the outcome to the future research.