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This study explores the presence and impact of female role models in Science, Engineering, Technology and Mathematics (STEM) education due to the underrepresentation of girls in STEM fields. The objective is to assess girls' role models, particularly the role they play in their interest and in a career choice in STEM. A survey was distributed through an online panel and 546 valid answers from girls aged 14-20 in Germany were received. The analysis links to social cognitive theory and revealed that immediate family members (50,1%), musicians, actors and artists (12,4%), and media influencers (9,6%) were the top role model categories. Male role models dominated in teachers, entrepreneurs, and friends, while female role models were prominent in the family category. Female scientists and entrepreneurs who could serve as STEM role models, were found to be underrepresented. The study also showed that girls with STEM occupational role models in their immediate surroundings were more likely to have an interest in STEM subjects. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) tools are increasingly being used in education for various purposes. In particular, AI chatbots such as ChatGPT, with their user-friendly interfaces are being explored in education to co-create teaching materials, provide advice and guidance to educators, simulate classroom scenarios, and offer personalized recommendations to students on how to study and approach subjects. With all the enthusiasm for these new opportunities, one should be aware of the risks due to potential biases in the generated content or the responses. These biases can be associated with factors such as gender, race, religion, or political orientation. As a consequence, educators who are using AI chatbots to (co-)create teaching materials need to have the knowledge and the strategies to mitigate such biases. This paper focuses on one particular type of bias, namely gender bias, and on specific disciplines, namely Science, Technology, Engineering and Mathematics (STEM). Gender bias in STEM education is particularly problematic because it may reinforce existing stereotypes about girls and women in STEM and contribute to their underrepresentation in STEM fields. To raise awareness of these risks of gender bias in AI-co-created STEM teaching materials, this paper identifies risks of gender bias by analysing potential usage patterns of AI chatbots by educators when creating teaching materials. An example of such a risk is if the AI chatbot generates educational materials that primarily portray men as STEM professionals and underrepresent women. This would exacerbate the lack of female role models in STEM. Therefore, strategies are developed that educators can apply to mitigate these risks. These strategies will be demonstrated using practical examples. This will allow them to break the vicious cycle of perpetuating stereotypes in STEM education. In addition, these examples demonstrate how AI chatbots can be used to make STEM education more inclusive, which may include co-creating educational materials tailored to individual interests and learning styles. [ABSTRACT FROM AUTHOR]

Science, Technology, Engineering and Math (STEM) subjects are extremely important to any nation's economy. For society to flourish sustainably there should be equal involvement of men and women in these professions. Even though the number of women in STEM areas has increased over the past several decades, there is still a sizable underrepresentation of women overall and in key positions. In the winter term 2020/21, among all the 1,090,804 students in STEM subjects at German universities, only 347,197 were female students (31,8%) (Statistisches Bundesamt, 2022). According to research by Corbett and Hill (2015), the following factors limit girls and women from majoring, for example, in computer science: Cultural prejudices, gender biases, and microinequities (subtle discriminatory actions) all contribute to a lack of sense of belonging in these sectors. Overall, this study aims to better understand the key situations that affect girls' and young women's decision for or against a career in STEM. In particular, the research is interested in finding out about subtle or obvious discriminatory but also facilitating actions which influence attitude towards STEM subjects. An online questionnaire was distributed to a student panel via a market research institute, which was answered by 777 German female students aged 16-20 years in May 2022. The questionnaire collected female students’ memorable positive and negative critical incidents which had an impact on their attitude towards STEM subjects. On the one hand, female students reported a learning environment in school leading to fear, frustration or anxiety which result into them questioning their math or science competence. On the other hand, female students also reported very positive, memorable experiences and appraisal situations in and offsite the classroom which increase their positive attitude about STEM subjects and might lead to a future career choice in a STEM subject. The authors hope to spark a conversation about how institutions might better tailor their offers to a female audience. [ABSTRACT FROM AUTHOR]