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33 SES 08 B: Exploring Gendering Processes Through Innovative Methodologies: Strengths and Challenges
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33. Gender and Education
Paper Gender Biases in Artificial Intelligence Models and Humans 1: Aristotle University of Thessaloniki, Greece; 2: University of Ioannina, Greece Presenting Author:In recent years, remarkable progress has occurred in the development of large language models (LLMs), driving artificial intelligence (AI) into increasingly sophisticated realms of human-computer interaction. Leading examples include OpenAI’s ChatGPT and Google’s Gemini AI, both of which demonstrate impressive capabilities in natural language understanding, text generation, translation, and other language-driven applications (Rane, Choudhary, & Rane, 2024). As AI technologies continue to advance and data usage expands, it becomes increasingly crucial to comprehend their broader societal implications, particularly within the fields of education and training. Educators and school leaders must possess at least a foundational understanding of AI and data practices to engage with these technologies positively, critically, and ethically, thereby leveraging their full potential (European Commission, 2022, p. 4). When applied appropriately, AI has the potential to enhance teaching, learning, and assessment, improve educational outcomes, and increase institutional efficiency. However, the misuse or poor design of AI systems may lead to negative or even harmful consequences. It is therefore essential for educators to evaluate whether the AI tools they employ are reliable, fair, safe, and trustworthy, and ensure that educational data is managed securely, respects privacy, and serves the public good. The concept of “Ethical AI” underscores the importance of developing and deploying AI systems that adhere to ethical standards, guiding principles, and fundamental human values (European Commission, 2022, p. 11). Gender biases in artificial intelligence models concern the academic community, especially in the field of education. The integration of AI into the educational process is a directive of the educational policies of the European Commission. Thus, more research is needed on making those AI models unbiased and ethical as far as gender issues are concerned, so that the transition can be smooth, taking the available resources in education into account. The purpose of this research is the comparative study of the results provided by two artificial intelligence models compared with the results obtained by male and female students who were trained in gender issues in education, at a higher educational institution in the country. The method for the texts written by the students is content analysis and was used to identify gender patterns in texts. For the texts produced by the AI models a script in Python was written to do context and statistical analysis with the help of python dictionaries. Through a feminist approach, this paper seeks to understand whether Artificial Intelligence perpetuates existing stereotypes or not, when compared with the texts produced by students. The findings showed that Gemini was the AI model that gave better results than the ChatGPT and the students, in terms of the breadth of the gender dimension in the texts they were trained to create. The students who attended the course on gender issues showed a small understanding of their perceptions about gender equality. The only notable observation is that the students included some LGBTQI issues in their answers. Two fundamental issues arise from the foregoing research that warrant further exploration: Methodology, Methods, Research Instruments or Sources Used The research was conducted within a higher education department where the researcher taught the course “Education and Gender” during the 2025–26 academic year. Thirty male and female students attended lectures on theories and concepts related to gender relations and participated in group activities and experiential workshops designed to foster a deeper understanding of gender stereotypes. At the end of the semester, students were provided with a set of materials to which they were asked to respond. The same exercises were then administered to ChatGPT and Gemini with the aim of comparing (a) human responses with those generated by AI systems, and (b) the responses of the two AI systems with each other. While similar studies have been conducted abroad, this research constitutes the first study within higher education to compare the responses of students, who received formal instruction on gender issues and subsequently engaged in creative writing and sentence completion tasks, with those generated by AI systems trained on the same prompts and asked to complete the same exercises. A feminist content analysis of the students’ stories and sentence completions revealed a weak correlation between the material taught during the academic semester and their final responses. This indicates that, despite exposure to feminist theory and gender studies, their answers remained markedly stereotypical. From the AI perspective of the research, the same set of prompts given to students was entered into ChatGPT and Gemini, supplemented by the instructions: “Make the people in the stories for A and B more human. Give the character a name, a profession, or any distinctive trait” and “Respond as a person aged 18–30”, all written in Greek. Without these additional prompts, the models tended to respond in a definitional or abstract manner, lacking the narrative richness necessary for meaningful interpretation. Accordingly, two separate chat sessions, one per AI model, were trained to respond as if they were “different students.” Subsequently, a Python script was developed to perform contextual and statistical analysis using Python dictionaries, associating stereotypical terms with social groups and calculating their frequency of occurrence within each category. Conclusions, Expected Outcomes or Findings The research was conducted within a university department of secondary education teacher training. The findings indicate that professional development on gender-related issues is essential for educators. The study compares the dataset (a) between human and AI-generated outputs, and (b) between ChatGPT and Gemini models. In the narratives created by male and female students about a “professional,” most responses reflected traditional and gender-stereotypical occupations. When asked to write stories about a person’s ambitions, students predominantly depicted men as individuals who strive, aim, and aspire to succeed, often as freelancers or scientists. In contrast, female characters were portrayed as “dreaming” of success, with goals oriented toward helping others or protecting the environment. In the sentence-completion exercises, both male and female students provided rather conventional responses. For instance, female students were described as “trying hard, studying, not missing classes,” whereas male students were characterized as “missing classes, failing exams, playing videogames to the detriment of their studies.” Descriptions of mothers included “taking care, cooking, organizing, working hard, offering moral support, showing love,” while fathers were depicted as “working all day, providing material goods, loving and supporting silently.” The representations of grandparents were also heavily stereotyped: grandmothers were said to “grumble, stay at home, cook for their grandchildren, and help their daughters with housework,” while grandfathers “go to cafés, sit and watch TV and give money to their grandchildren.” Regarding the AI-generated responses, ChatGPT tended to produce a range of topics containing stereotypical elements, whereas Gemini offered more diverse and non-stereotypical content characterized by a positive tone across all sentence themes. Continuous training on gender awareness should be provided throughout teachers’ professional careers, as deeply internalized gender stereotypes, manifested in both thought and behavior, are often difficult to recognize and even harder to change. References Armutat, S., Wattenberg, M. & Mauritz, N. (2024). Artificial Intelligence: Gender-Specific Differences in Perception, Understanding, and Training Interest. Proceedings of the 7th International Conference on Gender Research, (pp. 36-43). Academic Conferences International Ltd. https://doi.org/10.34190/icgr.7.1.2163 Consuegra-Ayala, J. P., Martínez-Murillo, I., Lloret, E., Moreda, P. & Palomar, M. (2024). A multifaceted approach to detect gender biases in Natural Language Generation. Knowledge-Based Systems, 303, 112367 https://doi.org/10.1016/j.knosys.2024.11236 Spillner, L. (2024). Unexpected Gender Stereotypes in AI-generated Stories: Hairdressers Are Female, but so Are Doctors. In: R. Campos, A. Jorge, A. Jatowt, S. Bhatia, M. Litvak (eds.): Proceedings of the Text2Story’24 Workshop, Glasgow (Scotland), 24-March-2024. https://ceur-ws.org/Vol-3671/paper10.pdf Thomson, E., Chapman, R., Cooper G. & Cooper, Z. (2026). Exploring bias in artificial intelligence: stereotypes and gendered narratives in digital imagery of early childhood educators, Gender and Education, 38, (1), 53-72. DOI: 10.1080/09540253.2025.2546048 Valerio-Ureña, G., Sevilla-Campoverde, G., Ortúzar, S. & Lazcano, C. (2024). Gender Stereotypes in the Creation of Educational Cases with ChatGPT. International Symposium on Multimedia (ISM) IEEE. DOI: 10.1109/ISM63611.2024.00059 Voutyrakou, D. A., & Skordoulis, C. (2025). Using AI Tools to Enhance Educational Robotics to Bridge the Gender Gap in STEM. Education Sciences, 15(6), 711. https://doi.org/10.3390/educsci15060711 Yim, Iris Heung Yue (2024). Artificial intelligence literacy in primary education: An arts-based approach to overcoming age and gender barriers. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100321 33. Gender and Education
Paper Constructing the “Knowing Subject” in the Context of Intersectionality: Tracing Epistemic Injustice of Pre-Service Teachers’ Educational Biographies 1: Eskisehir Osmangazi University, Turkey (Türkiye); 2: Bahcesehir University, Turkey (Türkiye); 3: Uludag University, Turkey (Türkiye) Presenting Author:Pedagogical institutions, particularly schools, should not be understood merely as physical sites where knowledge and skills are formally transmitted through curricula. Rather, they operate as social and institutional environments in which values, norms, and beliefs are conveyed to successive generations through both explicit instruction and the hidden curriculum. From a feminist perspective, the hidden curriculum and routine pedagogical practices convey unwritten rules concerning gendered expectations and socially sanctioned roles. These norms are reinforced through existing power structures and become normalized within society. Empirical research demonstrates persistent gender-based disadvantages across educational contexts. Studies indicate that male students speak 1.6 times more frequently than female students, interrupt at rates up to fifteen times higher, and tend to use assertive language, whereas women often employ hesitant and apologetic speech (Lee & McCabe, 2020). Moreover, educational materials similarly reproduce gendered representations; for example, medical training texts often portray women as caregivers and nurses and men as doctors and administrators (Arsever et al., 2023), while in engineering, women report experiences of social isolation, condescension and sexist discourse masked as humour (Harris, 2025). As a result of all these studies, it has been emphasized that women face negative outcomes such as professional inadequacy, silencing, decreased performance, and a weakened sense of belonging.
Feminist perspectives grounded in Marxist analyses argue that knowledge production is not neutral but is deeply embedded in relations of power, privilege, and politics (Tuana, 2017). Fricker (2007) distinguishes between between (i)testimonial injustice, which occurs when a speaker’s credibility is unfairly undermined due to prejudice, and (ii)hermeneutical injustice, which arises when individuals lack the conceptual resources necessary to interpret and articulate their experiences. In educational contexts, these forms of injustice shape whose knowledge is recognized and whose experiences are rendered unintelligible or invisible. Analysing education through the framework of epistemic injustice enables critical examination of authority relations in teacher–student interactions, the role of curricular choices in reproducing inequality, and the pedagogical consequences of prejudice in classrooms. It also raises ethical questions regarding how educators can protect students’ epistemic agency while maintaining academic standards (Kotzee, 2017).
Accountability-driven evaluation practices within education systems further exacerbate these injustices by marginalizing teachers who successfully engage with historically disadvantaged students in low socioeconomic contexts. Over time, this exclusion produces cumulative harm conceptualized as “slow violence” that undermines the professional identities and leadership aspirations of women and minorities, thereby limiting diversity within the teaching profession (Bernard et al., 2023). Consequently, understanding how pre-service teachers’ past experiences of testimonial and hermeneutical injustice shape their professional identity formation and perceptions of educational leadership is critically important. The literature emphasizes the need for more studies that examine epistemic injustice in teacher education within local contexts (Babu et al., 2025; Bernard et al., 2023; Kotzee, 2017) and through an intersectional framework (Anyango et al., 2026; Tuana, 2017). Addressing this gap, the present study adopts an intersectional framework focusing on gender, disability, and socioeconomic status. Specifically, the study critically examines the antecedents and consequences of epistemic injustice experienced by pre-service teachers throughout their educational trajectories, from primary education to higher education, from a feminist and intersectional perspective. The study seeks to answer the following sub-questions:
Q1. Which curricular contents (formal, hidden, and null curricula) and pedagogical practices encountered in pre-service teachers’ prior educational experiences hindered their capacity to act as knowledge producers? Q2. How do the intersection of disability status, and social status besides gender shape pre-service teachers’ contributions to knowledge production? Q3. How do experiences of epistemic injustice influence pre-service teachers’ personal, academic and professional development? Q4. What kinds of resistance strategies have pre-service teachers developed in response to epistemic injustice pressures?
Methodology, Methods, Research Instruments or Sources Used Given the study’s focus on meaning-making of pre-service teachers’ experiences and on revealing their connections to structural power relations, a qualitative feminist biographical narrative research design was employed. Within this design, participants’ educational life stories were examined through temporal and contextual lenses. Purposeful sampling was used, specifically the maximum variation strategy. Crenshaw (1992), who introduced the concept of intersectionality, argues that single-axis analyses of gender or race are insufficient for explaining experienced inequalities and that their combined consideration enables the identification of distinct dynamics. To ensure intersectionality, twelve Turkish students from different grade levels were selected, representing diversity in gender (women and men), disability status (visual and physical disabilities), and social class (e.g., poor and affluent backgrounds, geographically disadvantaged and advantaged regions). Data were collected over the course of one academic term, allowing sufficient time to establish trust-based relationships with participants. In the first phase of data collection, in-depth individual interviews were conducted to gather participants’ educational life stories. Participants were asked questions such as: “Can you share an instance in which you were not taken seriously by your teachers or peers regarding something you claimed to know?” and “Have you ever felt that textbooks lacked representations of your lifestyle, culture, or gender? How did this make you feel?” These questions aimed to identify critical moments in participants’ educational trajectories. Another data collection method involved ‘reflective journals’ kept by participants. They were asked to document moments during the research period in which they felt unheard, ignored, or not taken seriously in class, as well as moments in which they felt valued and empowered. Finally, focus group interviews were conducted to explore the structural dimensions underlying individual narratives. Conducting focus groups with participants who shared similar identities functioned not only as a data collection strategy but also as a collective meaning-making process that encouraged participants to interpret injustices as systemic and structural rather than overly personalized experiences. Data were analyzed using Reflexive Thematic Analysis (Braun & Clarke, 2019). The dataset was read in detail, codes were generated, categories were formed, and themes were developed. These themes were interpreted in relation to the framework of epistemic injustice, with intersectional considerations such as being both disabled and female, or being poor and male (particularly when not conforming to hegemonic masculinity expectations), taken into account. Conclusions, Expected Outcomes or Findings The findings indicate that teachers who adhere to traditional educational approaches emphasize their authoritative position in the classroom and systematically disregard or devalue the knowledge production capacities of students from disadvantaged groups. The findings further demonstrate that teachers who do not assume responsibility for promoting gender equality actively contribute to the formation of a “chilly climate” within classrooms. Gender stereotypes are reproduced through teacher–student interaction patterns, the selection of instructional materials, and the structuring of curricular content. The systematic underrepresentation or exclusion of women and people with disabilities in course materials and classroom discourse constrains students’ opportunities to engage in knowledge production and meaning-making processes. Moreover, the exclusion of marginalized students’ cultural and local knowledge reduces epistemic diversity, while the hidden curriculum reinforces normalized behaviors and marginalizes those who deviate from dominant norms. From an intersectional perspective, the findings reveal that students who occupy multiple marginalized positions such as being both female and disabled experience distinct and compounded forms of epistemic injustice. Educational systems that fail to recognize and respond to these voices perpetuate hermeneutical injustice by denying individuals access to the interpretive resources necessary to understand and articulate their experiences. A study conducted with medical students (Blalock & Leal, 2022) highlights how participants resisted injustice by redefining their presence in the field, critiquing the curriculum, raising their voices, and supporting one another. Overall, the findings suggest that fostering awareness of epistemic injustice within teacher education programs particularly through collaborative and dialogical learning practices supports the development of resistance strategies and enables pre-service teachers to reconstruct their professional identities as “knowing subjects.” Such approaches contribute to challenging dominant power relations in knowledge production and promoting more inclusive educational leadership practices. References Anyango, C., Goicolea, I., Lövgren, V., & Namatovu, F. (2025). ‘The feeling that I’m unimportant’: epistemic injustice lens on formal support for women with disabilities survivors of intimate partner violence. Journal of Gender-Based Violence, 1-18. Arsever, S., Broers, B., Cerutti, B., Wiesner, J., & Dao, M. D. (2023). A gender biased hidden curriculum of clinical vignettes in undergraduate medical training. Patient Education and Counselling, 116, 107934. https://doi.org/10.1016/j.pec.2023.107934 Babu, Y., Mishra, P., Kumar, A., Pandey, C. S., & Pandey, S. (2025). Epistemic injustices andcurriculum: Strategizing for justice. Social Sciences & Humanities Open, 11, 101220.https://doi.org/10.1016/j.ssaho.2024.101220 Bernard, C. F., Kaufman, D., Kohan, M., & Mitoma, G. (2023). edTPA implications for teachereducation policy and practice: Representations of epistemic injustice and slowviolence. Education Policy Analysis Archives, 31, 1-27. Blalock, A. E., & Leal, D. R. (2022). Redressing injustices: how women students enact agencyin undergraduate medical education. Advances in Health Sciences Education (2023 28: 741–758 https://doi.org/10.1007/s10459-022-10183-x Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Researchin Sport, Exercise and Health, 11(4), 589-597.https://doi.org/10.1080/2159676X.2019.1628806 Crenshaw, K. (1992). Race, gender, and sexual harassment. Southern California Law Review, 65, 1467-1476. Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford universitypress. Harris, D. (2025). The hidden curriculum: gender, bias and banter in first-year engineeringeducation. European Journal of Engineering Education, 1-19.https://doi.org/10.1080/03043797.2025.2586131 Kotzee, B. (2017). Education and epistemic injustice. In The Routledge handbook of epistemicinjustice (Eds. I.J. Kidd, J. Medina, & G. Pohlhaus, Jr.) (pp. 324-335). Routledge. Lee, J. J., & Mccabe, J. M. (2021). Who speaks and who listens: Revisiting the chilly climatein college classrooms. Gender & society, 35(1),32-60. DOI:10.1177/0891243220977141 Tuana, N. (2017). Feminist epistemology: The subject of knowledge. In The Routledgehandbook of epistemic injustice (Eds. I.J. Kidd, J. Medina, & G. Pohlhaus, Jr.) (pp. 125138). Routledge. 33. Gender and Education
Paper No Evidence for Polarization or a Gender-Equality Paradox in Youth Value Gaps Across 33 European Countries in the 21st Century 1: University of Helsinki, Finland; 2: ESADE Business School, Spain Presenting Author:There has recently been extensive media coverage suggesting that young men and women have become increasingly polarized, drifting apart in their worldviews, values, attitudes, and political ideologies (Burn-Murdoch, 2024; Chung, 2025). However, relatively little empirical research has examined whether this purported division among youth has in fact changed over time. Media coverage often lack attention to historical trends, and even data-driven journalistic efforts may prioritize audience engagement over careful empirical description of the phenomenon. At the same time, a related concept has gained prominence in both media and academic discourse: the so-called “gender-equality paradox” (Stoet & Geary, 2018). This framework posits that gender differences are larger in more egalitarian societies, for example in domains such as science attitudes measured in PISA and in several other individual characteristics. If gender equality has indeed increased over time, this paradox would also imply within-country growth in differences between young men and women. This study tests whether this purportedly paradoxical pattern is evident in the personal values of European youth, examined from 2002 to 2020 using ten rounds of European Social Survey data across 33 European countries (total N = 63,402). Methodology, Methods, Research Instruments or Sources Used Following recent developments in statistical methodologies for examining gender differences and their correlates, this study employed a multivariate, value-based gender-typicality measure to assess the overall distance between young men and women aged 18 to 29 (Ilmarinen et al., 2023). To examine associations with time and country-level gender equality, a deconstructed analytical approach was used, addressing methodological limitations in many studies that report evidence for a “gender-equality paradox” across educational, economic, personality, and value domains (Ilmarinen & Lönnqvist, 2024). Rather than relying on difference scores, which can introduce statistical artifacts in regression and correlation analyses, the deconstructed approach separately examines changes in the average profiles of young men and young women over time and as a function of gender equality. This strategy allowed for clearer inference about whether observed patterns reflect shifts in one or both groups. Multilevel models with individuals nested within countries were employed to account for the hierarchical structure of the data. Personal values were measured using the Human Values Scale. Country-level gender equality was measure using the Gender Equality Index (a reversed form of the Gender Inequality Index) and the Global Gender Gap Index. Research hypotheses and analysis plans were preregistered (https://osf.io/7cags/?view_only=f3e97a78271e46bfafb9e20ac8d35bb1). Data, code, and materials are available at https://osf.io/e3947/overview?view_only=6bc07ea0d0034f0f93c826fee1f1daa3. Conclusions, Expected Outcomes or Findings There was no evidence of general polarization or increasing division in value-based gender gaps across the ten rounds of European Social Survey data collected between 2002 and 2020. Model estimates indicate that the average multivariate gender difference among European youth in 2002 (ESS Round 1) was D = 0.42, 95% CI [0.37, 0.48], p < .001, whereas by 2020 (ESS Round 10) it had declined to D = 0.32, 95% CI [0.26, 0.38], p < .001. These results suggest that young men and women in Europe are not growing apart in their values over time; if anything, they have become more similar and less male-typical on average. Country-specific time trends for young women and men further illustrate the absence of increasing gender division in 21st-century Europe. None of the 33 countries exhibited diverging trends. Instead, value-based male-typicality declined on average and showed decreases in 19 countries, while increases were observed in only five. Interestingly, several Eastern European countries displayed both increases in value-based male-typicality and evidence of convergence, with young men and women becoming more similar to each other over time. These findings suggest that interpretations of a gender-equality paradox in values and other individual differences reflect “inflated crud” rather than a coherent substantive pattern. Following Orben and Lakens (2020), this “crud” refers to small true-positive correlations arising from the complexity of social and behavioral data. The apparent paradox may therefore largely result from analytical choices rather than a theoretically meaningful phenomenon. Addressing this requires examining associations separately for men and women, using effect size metrics that do not rely on mean-level correlations (e.g., Cohen’s q), and preregistering theoretically meaningful minimum effect sizes. References Burn-Murdoch, J. (2024, January 26). A new global gender divide is emerging. Financial Times. https://www.ft.com/content/29fd9b5c-2f35-41bf-9d4c-994db4e12998 Chung, H. (2025, March 5). Gen Z men and women most divided on gender equality, global study shows. King’s College London. https://www.kcl.ac.uk/news/gen-z-men-and-women-most-divided-on-gender-equality-global-study-shows Ilmarinen, V. J., & Lönnqvist, J. E. (2024). Deconstructing the gender-equality paradox. Journal of Personality and Social Psychology, 127(1), 217–237. https://doi.org/10.1037/pspp0000508 Ilmarinen, V. J., Vainikainen, M. P., & Lönnqvist, J. E. (2023). Is there a g-factor of genderedness? Using a continuous measure of genderedness to assess sex differences in personality, values, cognitive ability, school grades, and educational track. European Journal of Personality, 37(3), 313–337. https://doi.org/10.1177/08902070221088155 Orben, A., & Lakens, D. (2020). Crud (re)defined. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920917961 Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29(4), 581–593. https://doi.org/10.1177/0956797617741719 | ||