It’s possible to assume that women who study STEM topics with a low proportion of females have successfully overcome barriers in school and the family, making them less prone to stereotypic views and influences. The present study focuses on these kinds of factors and analyzes to which degree family factors, school-related factors, and individual stereotypes may influence a woman’s academic self-concept. The following presents a latent regression model which is based on a survey of 296 women from different German universities, all of whom are part of STEM programs of study that have less than 30% females. It was investigated to which degree individual stereotypes, support in school, and family support contribute to the self-concept in STEM. Gender stereotypes were negatively related to students’ STEM-specific self-concept in the selected sample. This study also reveals negative family-related influences that lower a woman’s self-concept. Positive predictors on the other hand included school aspects that are found in the students’ favorite subjects at school. The results of the study provide important aspects for STEM education. Even though the students participating in the study presumably had good grades in STEM, stereotypes still corrupted their self-concept. One of the reasons for this might lie in stereotypes that attribute girls’ achievements to diligence instead of talent. The results also point out that direct support, particularly by parents, can have a negative impact on female students’ self-concept. Activities that are meant to support pupils directly may actually backfire and transport stereotypes instead. This stresses the need for indirect support during socialization, e.g. by providing opportunities for children to have positive experiences or by giving them the chance to meet role models that are enthusiastic about their STEM professions. These kinds of measures have the potential to spur students’ interest in STEM subjects – something that in the present study proved to be especially beneficial for women’s positive self-concept when studying STEM topics.

Advancements in the generative AI field have enabled the development of powerful educational avatars. These avatars embody a human and can, for instance, listen to users’ spoken input, generate an answer utilizing a large-language model, and reply by speaking with a synthetic voice. A theoretical introduction summarizes essential steps in developing AI-based educational avatars and explains how they differ from previously available educational technologies. Moreover, we introduce GPTAvatar, an open-source, state-of-the-art AI-based avatar. We then discuss the benefits of using AI-based educational avatars, which include, among other things, individualized and contextualized instruction. Afterward, we highlight the challenges of using AI-based educational avatars. Major problems concern incorrect and inaccurate information provided, as well as insufficient data protection. In the discussion, we provide an outlook by addressing advances in educational content and educational technology and identifying three crucial open questions for research and practice.

Similarity search in multimedia databases requires an efficient support of nearest neighbor search on a large set of high dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest neighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for high dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e. it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in the X tree (up to a factor of 4).

• Evidence about differences in digital competencies (digital divide) is contextualized with other core competencies, esp. literacy and numeracy. • The generation a person is born to/in is a factor for digital divide for all subgroups. • The effect of education on digital competency is larger than the effect of generation. • Rather mitigating digital divide as its impact on digital competencies is smaller compared to the impact of literacy and numeracy. • Migration background has a higher impact on digital divide for persons without computer use at the workplace digital divide than for those who use.