Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
|
Daily Overview |
| Session | ||
33 SES 13 B: Innovative Methodologies in Gendered Research: AI, Online Images, Policy Analysis and Quantative Data Analysis.
Paper Session | ||
    | ||
33. Gender and Education
Paper Sex, Gender and Intersectionality in Research Content: Training Researchers for Stronger Objectivity Université Paris-Est Creteil, France Presenting Author:How do we know what we know? Feminist epistemologies have long demonstrated that scientific knowledge is socially situated and that claims to neutrality often obscure structural biases embedded in research questions, methods, and interpretations. Rather than undermining objectivity, the systematic integration of sex, gender and intersectional perspectives has been shown to strengthen it, contributing to what Harding (1991) conceptualised as “strong objectivity”. Over the past two decades, initiatives such as Gendered Innovations (Schiebinger, 2008) have provided compelling evidence that incorporating sex and gender analysis enhances research quality across science, engineering, medicine, as well as the humanities and social sciences (HSS). Despite these advances, and notwithstanding its status as a mandatory requirement in EU-funded research, the integration of gender as a research dimension remains one of the least developed areas of gender equality in the research sector (ERAC, 2020).
This paper addresses the persistent gap between policy requirements, conceptual consensus, and research practice by focusing on the training of researchers. It argues that sustainable change in research content depends on embedding gender and intersectional approaches at the formative stages of academic careers, when research habits, disciplinary norms and methodological reflexes are still being shaped. Drawing on feminist theories of knowledge production, as well as recent training experiences and institutional initiatives, the paper explores how and why sex, gender and intersectionality should be incorporated throughout the research cycle.
The contribution combines conceptual reflection with empirical and practice-based insights. First, it revisits the social foundations of scientific knowledge, highlighting how androcentrism, gender blindness and the exclusion of marginalised perspectives continue to structure research canons, objects of study and evaluative standards. Using examples from the history of science and other HSS disciplines, the paper illustrates where gender and intersectionality can intervene in the selection of research topics, the construction of concepts, the choice of sources, methodological design, and the interpretation and dissemination of results. These reflections demonstrate the transferability of gender-sensitive approaches across disciplines and their relevance beyond HSS.
Second, the paper presents training initiatives aimed at researchers as a key lever for change. Building on observations from VOICES training school in 2023 and NEXT GENDER capacity-building workshop in 2026, it shows that many researchers have received little or no formal education in sex- and gender-sensitive research, despite being expected to address these dimensions in funding applications and publications. Interactive, peer-based training environments—where participants engage directly with their own research projects and exchange with experienced scholars—are shown to foster reflexivity, methodological innovation and the formulation of more inclusive research questions.
Third, the paper discusses the added value of co-creative and community-based approaches, such as communities of practice, in supporting the long-term adoption of gender and intersectionality methodologies. Unlike fragmented or short-lived resources, these collective spaces allow for ongoing learning, disciplinary translation and mutual support across institutional roles, including researchers, evaluators and trainers. By articulating training workshops with sustainable communities of practice and open-access toolkits, such initiatives address the diverse needs and constraints of stakeholders within the research ecosystem.
In conclusion, the paper argues that integrating sex, gender and intersectionality into research content is not merely a compliance exercise but a transformative epistemic practice. Training researchers plays a crucial role in normalising these approaches, expanding research imaginaries and enhancing scientific quality. By combining conceptual grounding, practical tools and collaborative learning formats, the proposed approaches contribute to more inclusive, reflexive and robust knowledge production across disciplines. Methodology, Methods, Research Instruments or Sources Used This contribution adopts a qualitative and reflexive methodological approach combining literature review, conceptual analysis and participant observation. The first, conceptual part of the paper is based on an extensive review of interdisciplinary literature on intersectionality, feminist epistemology and European policy frameworks, complemented by conceptual thinking aimed at critically examining how intersectionality is defined, translated and operationalised in continental European contexts. The second and third parts of the paper combine literature review with empirical insights drawn from participant observation. The empirical material is collected through direct involvement in the co-creative capacity-building workshop organised by the NEXT-GENDER project in April 2026, as well as through ongoing observation of the Communities of Practice (CoPs) launched within the same project in February 2026. Participant observation allows for the analysis of how sex, gender and intersectionality is discussed, negotiated and translated into practice by researchers across disciplines and institutional contexts. Attention is paid to interactions, collective problem-solving processes, and the emergence of shared understandings and points of tension. This mixed approach makes it possible to link conceptual debates with situated practices, highlighting how intersectionality is learned, adapted and enacted within European training and policy-oriented settings. Conclusions, Expected Outcomes or Findings This paper aims to demonstrate the heuristic value of sex, gender, and intersectional methodologies in research, highlighting their potential to generate new insights and inform more inclusive knowledge production. Practically, it offers co-creative capacity-building workshops, communities of practice, methodological toolkits, case studies, and structured training programmes, enabling researchers to collaboratively develop inclusive and reflexive approaches while fostering innovation and shared learning across disciplines and institutions. References Theoretical Foundations •Crenshaw, K. W. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299. •Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Cornell University Press. •Rocchi, J.-P. (2021). L’intersectionnalité ou la conceptualisation de la chose vécue. In M. Boussahba et al. (Eds.), Qu’est-ce que l’intersectionnalité (pp. 35–62). Éditions Payot & Rivages. •Wylie, A. (2002). Thinking from things: Essays in the philosophy of archaeology. University of California Press. Policy, Governance, and European Institutional Frameworks •Müller, J., & Humbert, A. L. (2025). Data collection and analysis for inclusive GEPs: Intersectional challenges and solutions for R&I institutions. In She Figures 2024: Policy report (pp. 39–76). Publications Office of the European Union. https://data.europa.eu/doi/10.2777/934401 •Woodward, A. E. (2003). Building velvet triangles: Gender and informal governance. In T. Christiansen & S. Piattoni (Eds.), Informal governance in the European Union (pp. 76–93). Edward Elgar Publishing. •European Research Area and Innovation Committee (ERAC). (2020). Position paper on the future gender equality priority in the European Research Area (ERAC 1204/20). Council of the European Union. https://data.consilium.europa.eu/doc/document/ST-1204-2020-INIT/en/pdf Gender as Research Content and Knowledge Production •Gendered Innovations. (2025). Guidelines for intersectional analysis in science and technology: Implementation and checklist development. Stanford University. https://genderedinnovations.stanford.edu/methods-sex-and-gender-analysis.html?utm •Schiebinger, L. (2014). Gendered innovations: Harnessing the creative power of sex and gender analysis to discover new ideas and develop new technologies. Triple Helix, 1, Article 9. https://doi.org/10.1186/s40604-014-0009-7 •Schiebinger, L., & Klinge, I. (Eds.). (2020). Gendered innovations 2: How inclusive analysis contributes to research and innovation. Publications Office of the European Union. https://doi.org/10.2777/316197 •Schiebinger, L., & Schraudner, M. (2011). Interdisciplinary approaches to achieving gendered innovations in science, medicine, and engineering. Interdisciplinary Science Reviews, 36(2), 154–167. https://doi.org/10.1179/030801811X13013181961450 •Schiebinger, L., Klinge, I., Sánchez de Madariaga, I., Paik, H.-Y., Schraudner, M., & Stefanick, M. (2011). Gendered innovations in science, health & medicine, engineering, and environment. Interdisciplinary Science Reviews, 36(2), 154–167. https://doi.org/10.1179/030801811X13013181961450 •Tannenbaum, C., Ellis, R. P., Eyssel, F., Zou, J., & Schiebinger, L. (2019). Sex and gender analysis improves science and engineering. Nature, 575(7781), 137–146. https://doi.org/10.1038/s41586-019-1657-6 Communities of Practice and Co-Creative Methodologies •Palmén, R., & Müller, J. (Eds.). (2024). A community of practice approach to improving gender equality in research. Routledge. https://doi.org/10.4324/9781003225546 •Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5–18. https://doi.org/10.1080/15710880701875068 33. Gender and Education
Paper Validation of a Matrix for Image Analysis from a Gender Perspective Universidad de Sevilla, Spain Presenting Author:This work is funded by the R&D&I Project PID2023-149084OB-I00 financed by the Ministry of Science, Innovation and Universities, entitled: Female labour inclusion in STEAM professions: analysis of discourses on women in the STEAM field in school textbooks in democratic Spain. In today's society, characterised by the dizzying pace of constant change, schools operate in an environment of digital stimuli that can hinder deep reflection. For this reason, humanistic education geared towards critical thinking is becoming increasingly important (Moreno-Pinado, & Tejeda, 2017). To this end, we must review the teaching resources that are commonly used in education, such as school textbooks. School textbooks remain a central resource in teaching, and images in particular are a highly prominent component that contributes to constructing professional references, roles and expectations. In the field of Science, Technology, Engineering, Arts and Mathematics (STEAM), the visual representation of women and men can reinforce or challenge gender stereotypes linked to epistemic authority, innovation, leadership and professional training. The comparative European study by Jehle et al. (2024) indicates that, even in contexts with regulatory frameworks favourable to equality, biases persist in textbooks in terms of gender presence and stereotyping. This coeducational approach requires going beyond equal representation and addressing the professional training and qualifications of women, thereby increasing their visibility as scientists within school knowledge (Subirats, 2017). From a European and international perspective, this work is linked to SDG 4 (quality education) and SDG 5 (gender equality) and to the monitoring of equality in research and innovation through comparable indicators (European Commission, Directorate-General for Research and Innovation - ECDRI, 2025). This approach is in line with UNESCO's guidelines on the future of education and with the 2030 Agenda (UNESCO, 2021). In the Spanish context, recent analyses document the persistence of gender gaps in STEAM pathways throughout the educational cycle and into the labour market (Cobreros et al., 2024). These gaps are connected to the processes of identity construction and professional aspirations in young women and are related to biases in the attribution of fields of contribution when women are represented (Vázquez-Cupeiro, 2022), reinforcing the need to identify and correct curricular messages that operate as early references. Therefore, this contribution poses the following research question: to what extent does an image analysis matrix allow us to identify patterns of female representation and gender stereotypes in images from STEAM textbooks? To answer this question, the following objectives are established: O1) to design an analysis matrix focused on the person represented; O2) to analyse content validity through expert judgement; O3) to estimate reliability through a pilot test with independent coding. Methodology, Methods, Research Instruments or Sources Used A design for the development and validation of the instrument for visual content analysis is adopted. The pilot corpus will focus on Spanish secondary education textbooks published after the curriculum reform of Organic Law 3/2020 (LOMLOE, 2020). The starting point is based on school textbooks and previous proposals for analysing the representation of women in textbooks (Moreno-Crespo et al., 2022). However, it incorporates an adaptation with regard to the unit of analysis, which shifts from the ‘complete image’ to the ‘character/person’ represented, allowing for the description of the attributes and narratives associated with each figure analysed. For each record, the matrix includes the assigned book code, as well as the page and location in the book. The iconographic analysis incorporates various variables such as: image mode; type of drawing; character characteristics (gender, age, ethnicity); character type; character name (if identifiable); and whether it is a cultural reference. For the occupational and contextual dimension, the main activity performed is coded; the description; the field; the profession; the type of activity; the sector and the professional classification, according to CIUO-08 (general and specific). The validation process to ensure the consistency of the instrument involves expert judgement in gender and education. To this end, a qualitative review and quantitative estimation (CVI/CVR) have been used, taking into account recent guidelines and protocols (Mokkink et al., 2025) and comparative evidence on content validity indices. Subsequently, for the pilot test and reliability calculation, the matrix is applied to a purposive sample of STEAM textbooks for secondary education (2023–2024 editions). Three researchers perform independent coding after training. Inter-coder reliability is estimated using Krippendorff's alpha, and training and data cleaning decisions are documented, following recent standards (Oschatz et al., 2023) and current tools for calculating the coefficient (Marzi et al., 2024). Conclusions, Expected Outcomes or Findings The aim is to consolidate a robust tool that allows for the description, comparison and auditing of female representation in images in STEAM textbooks from a gender perspective. The tool provides twofold added value. On the one hand, it allows for the quantification of presence/absence and, at the same time, captures the ‘quality’ of that presence through narrative attributes. On the other hand, it makes it possible to translate the findings into operational guidelines for publishers and teaching teams, so that materials can be adjusted in terms of equity and the promotion of scientific vocations without bias. The expected results aim to verify whether, despite a favourable regulatory framework and possible improvements in numerical representation, subtle inequalities persist: secondary roles, traditional scenarios, less visual centrality, or recurring associations of women with milestones of the past, rather than with current leadership and technological innovation (Jehle et al., 2024). In a context of rapid change at the global and local levels, the study is aligned with the commitment to SDGs 4 and 5 of the 2030 Agenda, contributing to quality and inclusive education that offers diverse models and does not limit the professional potential of half the population. As an international contribution, the work proposes a transparent, replicable and transferable procedure for auditing curriculum materials consistent with European equality agendas in R&D&I (ECDRI, 2025), in a scenario in which education systems seek to balance deep learning and social justice (UNESCO, 2023; OECD, 2024). The end result, therefore, is not only a validated matrix, but also an applicable tool for improving textbooks and teaching practices aimed at equity. References Cobreros, L., Galindo, J., & Raigada, T. (2024). Mujeres en STEM Desde la educación básica hasta la carrera laboral. Obtenido de EsadeEcPol-Center for Economic Policy. https://bit.ly/49fFq5i European Commission: Directorate-General for Research and Innovation – ECDRI (2025). She figures 2024 – Gender in research and innovation – Statistics and indicators. Publications Office of the European Union. https://doi.org/10.2777/592260 Jehle, A. M., Groeneveld, M. G., van de Rozenberg, T. M., & Mesman, J. (2024). The hidden lessons in textbooks: Gender representation and stereotypes in European mathematics and language books. European Journal of Education, 59(4), e12716. https://doi.org/10.1111/ejed.12716 Ley Orgánica 3/2020, de 29 de diciembre, por la que se modifica la Ley Orgánica 2/2006, de 3 de mayo, de Educación (LOMLOE). (2020). Boletín Oficial del Estado (BOE), núm. 340. https://www.boe.es/buscar/doc.php?id=BOE-A-2020-17264 Marzi, G., Balzano, M., & Marchiori, D. (2024). K-Alpha calculator–krippendorff's alpha calculator: a user-friendly tool for computing krippendorff's alpha inter-rater reliability coefficient. MethodsX, 12, 102545. https://doi.org/10.1016/j.mex.2023.102545 Mokkink, L., Herbelet, S., Tuinman, P., & Terwee, C. (2025). Content validity: judging the relevance, comprehensiveness and comprehensibility of an outcome measurement instrument–a COSMIN perspective. Journal of Clinical Epidemiology, 111879. https://doi.org/10.1016/j.jclinepi.2025.111879 Moreno-Crespo, P., Ferreras-Listán, M., & Moreno-Fernández, O. (2022). Representación femenina en la manualística española del siglo XXI: Educación Secundaria. In Mujer, educación e inclusión laboral: Una visión desde la manualística escolar (1975-2020) (pp. 167-183). Octaedro. Moreno-Pinado, W. E., & Tejeda, M. E. V. (2017). Estrategia didáctica para desarrollar el pensamiento crítico. REICE. Revista iberoamericana sobre calidad, eficacia y cambio en educación, 15(2), 53-73. Nussbaum, M. C. (2010). Sin fines de lucro. Por qué la democracia necesita de las humanidades. Katz editores. OECD (2024). Students, digital devices and success (OECD Education Policy Perspectives, No. 102). OECD Publishing. https://doi.org/10.1787/9e4c0624-en Oschatz, C., Sältzer, M., & Stier, S. (2023). Establishing standards for human-annotated samples applied in supervised machine learning-evidence from a Monte Carlo simulation. Studies in Communication and Media: SCM, 12(4), 289-304. https://doi.org/10.5771/2192-4007-2023-4-289 Subirats, M. (2017). Coeducación, apuesta por la libertad. Octaedro. UNESCO. (2021). Reimagining our futures together: A new social contract for education: Executive summary. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000379381 UNESCO. (2023). Global education monitoring report, 2023: Technology in education: A tool on whose terms?. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000385723 Vázquez-Cupeiro, S. (2022). Women’s networking in Spanish academia: a ‘catch-all’strategy or strategic sisterhood?. Critical Studies in Education, 63(4), 485-500. https://doi.org/10.1080/17508487.2021.1911820 33. Gender and Education
Paper The Global Representation of Women School Leaders in Generative AI Models: A Critical Discourse Analysis 1: Hakkari University; 2: Selahaddin Eyyubi Secondary School Presenting Author:Although the teaching profession is largely chosen by women worldwide, the insufficient representation of women in leadership and administrative positions in education constitutes a striking paradox. Across OECD countries, while nearly 69% of teachers are women, only 45% of school principals are female, revealing a sharp decline in leadership representation (OECD, 2017). In Turkey, the percentage of female rectors was 5.3% in 2007 and increased to 9% in 2023. Meanwhile, the ratio of female vice rectors was 4% and female deans was 17% in 2023 (Turkish Higher Education Management Information System, 2023). According to the 2023 report by the Ministry of Family and Social Services, only 3 out of 81 Provincial Directors of National Education are women, representing 3.7% of these positions. Butler (1990) argues in her book, Gender Trouble that gender is “performative”; In other words, gender is not an innate characteristic, but a process constantly reproduced through culturally determined actions. People perform these performances unconsciously, and their identities are shaped by these repetitive actions. Butler emphasizes that gender is constructed through linguistic and cultural discourses, and that no meaning related to the body predates social structure. Therefore, she denies that the biological body or the fetus exists with "natural" meanings. In short, according to Butler, gender is not something a person "is," but something they "do." The rationale for this study is to understand how artificial intelligence models create language and discourse about female school leaders. Generative AI systems reproduce patterns they learn from data sources. Therefore, their responses to female leadership may be influenced by social perceptions, cultural norms, and common expressions. Examining AI's representations in this field can contribute to both how gender perceptions are constructed in digital environments and the forms in which potential biases emerge. The purpose of this study is to examine how female school leaders are represented in generative AI algorithms on a global scale. In this context, the research aims to analyze how different generative AI models generate discourses about female school leadership and the gender norms these discourses implicitly or explicitly reproduce or transform. Research problems: According to artificial intelligence algorithms, how are women school leaders defined, and what is the representation of women leaders in educational institutions globally? According to artificial intelligence algorithms, what are the main cultural, structural, and institutional factors contributing to the underrepresentation of women in school leadership roles in certain countries? According to artificial intelligence algorithms, what are the effects of the low representation of women school leaders on education systems, school performance, and various school-related variables? According to artificial intelligence algorithms, what positive outcomes might an increase in women school leaders generate for educational organizations, what leadership style trends are observed, and what differences exist between women and men leaders? According to artificial intelligence algorithms, how is the role of women school leaders expected to evolve in the future, what policy and structural transformations are needed to strengthen women’s leadership, and how might AI reshape the representation of women’s leadership in education in the digital age? Methodology, Methods, Research Instruments or Sources Used Qualitative research methods will be preferred in the research. Qualitative research is a type of inquiry that employs methods such as observation, interviews, and document analysis to address a research problem (Seale, 1999). In data analysis, the critical discourse analysis method will be used. Critical discourse analysis is an approach that examines the effects of language on social structure and power relations. This approach analyzes how concepts such as ideology, power, and identity are shaped and reproduced through discourse and texts. It also supports social change by revealing injustices and social inequalities (Van Dijk, 2001). In this study, questions will be posed to the ChatGPT, DeepSeek, Gemini, Mistral, Claude, Kumru, Qwen, and Alice AI models. These AI models were selected due to their cultural diversity, technical differences, and dataset diversity. This diversity can provide the opportunity to examine how discourses regarding representations of women in educational leadership differ, what ideological influences they carry, what prejudices they reproduce, and which models produce more egalitarian discourse. In this context, the questions to be posed to artificial intelligence models are as follows: How do you define women school leaders? What is the representation of women leaders in educational organizations worldwide? What are the reasons for the underrepresentation of women in school leadership roles in some countries? What are the potential impacts of the low number of women school leaders on education systems in some countries? What positive outcomes could an increase in women school leaders create in these systems? What might be the salient professional and personal characteristics of women school leaders? What are the common trends regarding the leadership styles of women school leaders? What differences do you observe between women and men school leaders? What might be the impact of women school leaders on school variables? How do you assess the future evolution of the role of women school leaders in education? What policies or structural transformations might be necessary to strengthen women school leadership? What are your thoughts on AI transforming representations of women's leadership in education in the digital age? According to Ravitch and Carl (2019), validity and reliability procedures will be conducted in the study. For credibility, expert opinions will be taken. Confirmability, findings based on algorithms' statements will be emphasized. Transferability refers to the applicability of the findings to similar contexts. Conclusions, Expected Outcomes or Findings AI models are expected to mostly portray female school leaders as individuals with strong communication skills, empathy, openness to collaboration, and strong capacity for community building. While these representations are positive, they can sometimes reproduce normative gender-based roles. Models may tend to indicate that the proportion of female school leaders worldwide is lower than that of men. They are also expected to explain this situation with structural factors such as cultural norms, gender roles, patriarchal institutional structures, and career-family incompatibility. AI models are expected to highlight the negative consequences of a low number of female leaders, such as a lack of diversity and inclusiveness, a weakening of school climate and communication quality, and uniformity in decision-making processes. Models focusing on the positive effects of increased female leadership are expected to highlight the following positive outcomes: a more democratic and inclusive school culture, increased teacher motivation, enhanced student well-being and school engagement, and a role model role in gender equality. Some discourses within the models may create the risk of unwittingly framing female leadership as "emotional" and male leadership as "rational." This may indicate the presence of implicit gender stereotypes in algorithmic discourses. AI models are expected to generate discourses generally optimistic about the strengthening of female leadership in the future, such as policies improving and public awareness rising. The following recommendations can be developed within the scope of the study: More in-depth discourse analyses can be conducted to reveal implicit biases in representations of female leadership in generative AI models. Gender theories can be rethought through digital discourses, and conceptual frameworks can be developed to understand how AI reimagines gender. Mentoring and leadership development programs can be strengthened to increase the visibility of female school leaders. Regulations focused on gender equality can be developed for leadership appointments in education. References Butler, J. (1990). Gender Trouble: Feminism and the Subversion of Identity. New York: Routledge. OECD. (2017). Education at a Glance 2017: OECD Indicators. OECD Publishing. Ravitch, S. M., & Carl, N. M. (2019). Qualitative research: Bridging the conceptual, theoretical, and methodological. Sage Publications. Seale, C. (1999). Quality in qualitative research. Qualitative Inquiry, 5(4), 465-478. Turkish Higher Education Management Information System (2023). https://istatistik.yok.gov.tr Van Dijk, T. A. (2001). Multidisciplinary CDA: A plea for diversity. In R. Wodak & M. Meyer (Eds.), Methods of critical discourse analysis (pp. 95-120). http://dx.doi.org/10.4135/9780857028020.d7 33. Gender and Education
Paper With a Woman's Name, The Female Presence in Secondary School Textbooks Universidad de Sevilla, Spain Presenting Author:This work is funded by the R&D&I Project PID2023-149084OB-I00 financed by the Ministry of Science, Innovation and Universities, entitled: Female labour inclusion in STEAM professions: analysis of discourses on women in the STEAM field in school textbooks in democratic Spain. The research presented here addresses the visibility of women's contributions in secondary school textbooks. It is based on a historical bias in educational materials that tends to omit or distort the role of women in human progress, particularly in STEM disciplines (Aldrin, 2025; Bataineh, et al., 2024; López-Navajas, 2014). It should be noted that several studies emphasise the role of school textbooks as a tool for cultural legitimisation that shapes students' identities and expectations (Ferreras-Listán, M. & Jiménez-Pérez, R. 2013). This study connects the production and use of educational knowledge with its social effects (EERA, 2026) and aligns with Sustainable Development Goals (SDGs) 4 (quality education) and 5 (gender equality) of the 2030 Agenda. In this way, it seeks to respond to the international urgency to transform curricular materials into instruments of equity that eliminate gender bias and promote inclusive quality education (UNESCO, 2021). On the other hand, it addresses the guidelines of the European Education Area and the Gender Equality Strategy. On the other hand, it follows the guidelines of the European Education Area and the Gender Equality Strategy, emphasising that the recovery of female genealogies in textbooks is not only a historical debt, but also a requirement for advancing towards effective equality (European Commission, 2020). In line with the above, this study poses the following research question: What is the frequency, quality and narrative of female presence in textbooks? To this end, it analyses which attributes or stereotypes are associated with women and how they are included in school textbooks. In short, the aim is to elucidate whether women are recognised for individual achievements or whether their representation continues to be restricted to secondary, episodic or merely anecdotal roles. Thus, the overall objective of the study is to analyse the representation of women in school textbooks, not only by quantifying their presence, but also by evaluating the reason for their inclusion. The aim is to identify the extent to which the pedagogical narrative reproduces or dismantles gender stereotypes, with the purpose of providing credible role models that allow female students to project their professional identity on equal terms. From a contextual perspective, we find ourselves in a scenario of uncertainty in which education should reactivate humanistic and ethical training (Nussbaum, 2010; UNESCO, 2021). Far from being a neutral artefact, school textbooks select which knowledge, discourses and figures deserve to be preserved in school memory, relegating others to oblivion through processes of omission. Finally, contributions from theories on the representativeness gap (Frabotta, 2021; Rivas-Sepúlveda & Lamas-Huerta, 2024) are incorporated to explain how the absence of female role models in science and technology not only distorts the historical understanding of knowledge but also limits the aspirations of young women by reinforcing persistent biases that disconnect women from the production of technical knowledge. Methodology, Methods, Research Instruments or Sources Used This paper presents the results of an analysis of Spanish textbooks for compulsory secondary education published in 2023 and 2024, following the latest reform of educational content in Organic Law 3/2020 (LOMLOE, 2020). This is an in-depth pilot study with a methodological approach based on content analysis, aimed at identifying named characters from different fields (society, culture, politics, arts, science, mythology, among others). The quantifiable data were recorded and processed in a relational database designed ad hoc, using a matrix for the collection and processing of information (Moreno-Crespo et al., 2022). The total sample for this study consists of eight textbooks corresponding to four subjects related to STEM: Physics and Chemistry (2), Biology and Geology (2), Technology (2) and Mathematics (2). A total of 458 named entries were analysed. Conclusions, Expected Outcomes or Findings The research confirms an emerging trend towards numerical equity in the iconography of STEM textbooks. However, this progress is interpreted as a form of numerical inclusion. Although the number of female figures is increasing, their presence tends to be more representative than substantive. Similarly, the fundamental concepts of the subjects analysed continue to be presented from an androcentric perspective (e.g., Thales' theorem, Newton's law, Archimedes' principle), which reinforces a narrative in which abstract thinking and the formulation of natural laws are associated as inherently masculine attributes (Frabotta, 2021). In this context, while men appear as ‘authors,’ women tend to be portrayed as ‘collaborators,’ linked to domestic tasks or referred to by generic terms. This limits the recognition of women's authorship and dilutes the intellectual property of their findings in the minds of students (Rodríguez-Aparicio, 2025; Zambrano et al., 2025). For future society to move towards effective equality, 21st-century textbooks must go beyond the mere visual incorporation of women in laboratory settings. It is essential to construct a scientific genealogy in which women do not appear as added presences, but as identifiable protagonists with their own names. Only in this way can invisibility be broken down, encouraging new generations to recognise women as producers of culture and science (Rivas-Sepúlveda & Lamas-Huerta, 2024). Overall, the results suggest that current textbooks do not sufficiently promote a critical attitude; Therefore, the following recommendations are made: (1) balance roles and levels of prominence between genders; (2) incorporate a critical perspective that allows for the identification of stereotypes linked to gender inequalities; (3) highlight women's contributions to promote their recognition; and (4) strengthen their presence as role models for students by showing omitted or masculinised career paths as viable options. References European Commission. (2020, March 5). A Union of Equality: Gender Equality Strategy 2020–2025 (COM/2020) 152 final). EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0152 European Educational Research Association (EERA). (2026). Call for proposals: European Conference on Educational Research (ECER) 2026 & Emerging Researchers’ Conference (ERC) https://eera-ecer.de/fileadmin/user_upload/Documents/ECER_Documents/ECER_2026_Call_for_Proposals.pdf Ferreras-Listán, M., & Pérez, R. J. (2013). ¿ Cómo se conceptualiza el patrimonio en los libros de texto de Educación Primaria?. Revista de educación, (361), 591-618. Jiménez-Andújar, E. M., Monforte-García, E., & Alcalá-Ibáñez, M. L. (2023). Currículum Oculto de Género desde la Mirada Docente: Los Libros de Texto. Revista Internacional de Educación para la Justicia Social, 12(2), 25-44. http://dx.doi.org/10.15366/riejs2023.12.2.002 Ley Orgánica 3/2020, de 29 de diciembre, por la que se modifica la Ley Orgánica 2/2006, de 3 de mayo, de Educación (LOMLOE). (2020). Boletín Oficial del Estado (BOE), núm. 340. https://www.boe.es/buscar/doc.php?id=BOE-A-2020-17264 López-Navajas, A. (2014). Análisis de la ausencia de las mujeres en los manuales de la ESO: una genealogía de conocimiento ocultada. Revista de Educación, (363), 282-308. https://doi.org/10.4438/1988-592X-RE-2012-363-188 Frabotta, S. (2021). La ausencia de mujeres como referentes culturales en los manuales de italiano L2/LS. Cuestiones de género: de la igualdad y la diferencia, (16), 668-687. http://dx.doi.org/10.18002/cg.v0i16.6971 Moreno-Crespo, P., Ferreras-Listán, M., & Moreno-Fernández, O. (2022). Representación femenina en la manualística española del siglo XXI: Educación Secundaria. In Mujer, educación e inclusión laboral: Una visión desde la manualística escolar (1975-2020) (pp. 167-183). Octaedro. Nussbaum, M. C. (2010). Sin fines de lucro. Por qué la democracia necesita de las humanidades. Katz editores. Rivas-Sepúlveda, E., & Lamas-Huerta, P. A. (2024). Más allá de los estereotipos: participación de mujeres en carreras tecnológicas y STEM. Revista Educación Superior y Sociedad (ESS), 36(2), 247-271. https://doi.org/10.54674/ess.v36i2.903 Rodríguez-Aparicio, E. R. (2025). Referentes Teóricos y Epistemológicos de STEAM en la Educación en Ciencias. Revista Latinoamericana de Educación Científica, Crítica y Emancipadora, 4(1), 92-108. Rodríguez-Rodríguez, J., i Delgado, C. M., & Amo, C. D. (2023). Discusiones actuales alrededor del libro de texto escolar. Octaedro. UNESCO. (2021). Reimagining our futures together: A new social contract for education: Executive summary. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000379381 Zambrano, D. V., Rodríguez, H. M., & Rodríguez, E. (2025). Formación en prácticas inclusivas de género para la orientación vocacional hacia perfiles STEAM (Ciencia, Tecnología, Ingeniería, Arte y Matemáticas). Información tecnológica, 36(1), 15-26. http://dx.doi.org/10.4067/s0718-07642025000100015 | ||