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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.

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Dealing with representations is a crucial skill for students and such representational competence is essential for learning science. This study analysed the relationship between representational competence and content knowledge, student perceptions of teaching practices concerning the use of different representations, and their impact on students' outcome over a teaching unit.Participants were 931 students in 51 secondary school classes. Representational competence and content knowledge were interactively related. Representational aspects were only moderately included in teaching and students did not develop rich representational competence although content knowledge increased significantly. Multilevel regression showed that student perceptions of interpreting and constructing visual-graphical representations and active social construction of knowledge predicted students' outcome at class level, whereas the individually perceived amount of terms and use of symbolic representations influenced the students' achievement at individual level. Methodological and practical implications of these findings are discussed in relation to the development of representational competence in classrooms.

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