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Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

The societal relevance of artificial intelligence is growing rapidly. Advances are primarily driven by machine learning techniques. Recently, many educational tools for teaching AI have been introduced, allowing the user to implement AI features within pedagogical programming environments. However, many of these existing approaches share a common trait: the implementation of the underlying AI framework remains a black box, where external API calls or servers handle the actual computing. For the user, this typically means there is no chance to "see inside" the implementation. As a result, users often cannot gain a deeper understanding of how the "computer is learning". In this paper, we propose design principles for a framework in order to break open that black box. These design principles are implemented in the first part of SnAIp, a project aimed at enabling Machine Learning within Snap!. The focus of this part is using Reinforcement Learning within Snap! games. The corresponding framework enables constructionist learning and is implemented entirely in Snap!, which allows for a high degree of transparency and tangibility. Furthermore, we present a curriculum for Reinforcement learning using the SnAIp framework. With this, we outline a way to address ML in the classroom using block-based languages, while enabling the allimportant "look behind the scenes".

Debugging code is a central skill for students but also a considerable challenge when learning to program: helplessness and, in consequence, frustration when confronted with errors is a common phenomenon in the K12 classroom. Debugging is distinct from general programming abilities, therefore it should be taught explicitly. Despite this, debugging is an underrepresented topic in the classroom as well as in computer science education research, as only few studies, materials and concepts discuss the explicit teaching of debugging. Consequently, novices are often left on their own in developing debugging skills. This paper analyses the effectiveness of explicitly teaching a systematic debugging process, especially with regard to the students' self-efficacy and the resulting debugging performance. To this end, we developed an intervention, piloted it and then examined it in a pre-post-control-group-test-design: Both experimental and control groups were surveyed using a questionnaire and given debugging exercises as a pre-test. Afterward, the intervention was carried out in the experimental group, while the control group continued to work on debugging exercises. During the post-test, the students once more worked on debugging exercises and were surveyed. The results show a significant increase in both self-efficacy expectations and debugging performance in the experimental group in contrast to the control group. Therefore, our study provides empirical arguments for explicitly teaching debugging and simultaneously offers a hands-on approach for the classroom.

Purpose To advance the learning of professional practices in teacher education and medical education, this conceptual paper aims to introduce the idea of representational scaffolding for digital simulations in higher education. Design/methodology/approach This study outlines the ideas of core practices in two important fields of higher education, namely, teacher and medical education. To facilitate future professionals’ learning of relevant practices, using digital simulations for the approximation of practice offers multiple options for selecting and adjusting representations of practice situations. Adjusting the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics can be characterized as representational scaffolding. Building on research on problem-solving and scientific reasoning, this article identifies leverage points for employing representational scaffolding. Findings The four suggested sets of representational scaffolds that target relevant features of practice situations in simulations are: informational complexity, typicality, required agency and situation dynamics. Representational scaffolds might be implemented in a strategy for approximating practice that involves the media design, sequencing and adaptation of representational scaffolding. Originality/value The outlined conceptualization of representational scaffolding can systematize the design and adaptation of digital simulations in higher education and might contribute to the advancement of future professionals’ learning to further engage in professional practices. This conceptual paper offers a necessary foundation and terminology for approaching related future research.