Exploring Gender in the Digital Divide and Artificial Intelligence uses: Sharing Feminist Voice

• In Event: The Discourse of Education Innovation, Global Learning and Teaching, and ICTs

Proposal

“If I do not use my classroom to proactively resist the overrepresentation of males in our dominant discourse, I will condemn another generation of students to sit in classes empty of women’s lives, voices, and vision” wrote Ursula Wolfe-Rocca, a Rethinking Schools editor. This call for the equal representation and voice of girls and women in the classroom can be equally applied to the call to provide equal access and use of online learning with information and communication technologies (ICT) and artificial intelligence. A gender digital divide exists in both developing and developed countries of the world that feminists decry as yet another obstacle to more equal educational systems. Artificial intelligence (AI) uses come with both opportunities and challenges in regard to findings on gender. A feminist perspective requires that we research gender equality and justice in use of these digital tools for learning.
From our earliest documented theories of learning from Socrates and Plato, we have understood that the goals of education for boys and girls have differed. Girls and women were not expected to fully participate and take their place as leaders of society. If discussed at all in ideas around the essential questions of why educate, who should be educated, should the state provide for education, and should education be the same for all students, girls were not often relegated to roles that limited their participation to the domestic sphere, the nurturing of boys and men, and other supportive roles to aid men. In some countries, these roles for women have unfortunately not much changed.
Feminist theory and pedagogy embrace and demand equity, respect diversity and strive for girls and women to gain visibility and inclusion (McCusker, 2017). Feminist theory and pedagogy are not guided by an essentialist approach but rather embrace a set of epistemological theories, teacher-student relationships, content approaches, and classroom and online approaches. Liberation from injustice and oppressions, especially gender injustice, are emphasized and space for critical engagement and empowerment in institutions such as education are promoted. Developing political consciousness and working toward social justice and social change such as in schools are encouraged (Naples & Bojar, 2002).
Reports and literature reviews about the gender digital divide (GDD) over the last decade have helped us to understand the divide (Acilar & Saebo, 2023) and sometimes focus on a particular geographical area such as Latin America (Ancheta-Arrabal, Pulido-Montes, & Carvajal-Mardones, 2021). Key characteristics in the divide emphasize access and use, yet even when equal access exists, it often does not result in equal use or quality of use. Findings point to gender differences in access to ICT existing especially in developing countries where gender inequality is still a major challenge, how sociocultural factors play a role, and that implementable policies to bridge the divide are needed. (Acilar &Saebo, 2023). In the Latin America literature review, the authors found it problematic that the pedagogical perspectives of women and feminists were missing and must be included. They noted “Inequality in education represents a major contributor to the gender digital divide, therefore women’s and girls’ digital adoption and use is frequently limited by lower levels of digital literacy, and a lack of confidence (Ancheta-Arrabal et al., 2021).
Ideas from just a few of the many recent research studies around AI that include variables of gender demonstrate that both opportunities and challenges exist regarding gender equity and justice. Some studies focused on students at university level and secondary levels have indicated that comparable learning occurs in AI literacy for both genders. Findings by Kong, Cheung, and Zang (2021) indicated that “the participants of diverse study backgrounds, and of both genders, could understand the concepts of machine learning, supervised learning, regression, classification, unsupervised learning, and clustering.” In a study of Nigerian secondary schools, the authors emphasized that contextual and cultural values be considered, that teamwork is needed. They found no significant differences across gender and school type.
Yet example studies also demonstrated the challenges of AI for gender equality. When gender differences in students’ motivation to learn AI were explored at the primary level, male students scored higher on motivation factors and challenges (Lin, Chai, Jong, Dai, Guo and Oin, 2021). In exploring bias in generative AI and its implications, especially the objectification of individuals, particularly women, Kuck (2023) found that filters that use generative AI can exhibit gender and racial biases while sexualizing users. The author found inadequate mitigation efforts to avoid bias and recommends a framework to help remove bias.
I have provided an introduction to the need to explore the gender digital divide and the motivations in use of AI in different contexts and the gender biases found in some AI use. More structured learning of AI literacy in schools appears to provide more success for both genders, especially with older students. The fuller paper will explore more studies and feminist analysis to open the discourse about attaining gender equity.

Author

• Sandra L Stacki, Hofstra University