Gender Differences in AI Literacy Workshop Outcomes and Deepfake Engagement
- URL: http://arxiv.org/abs/2606.14718v1
- Date: Tue, 19 May 2026 22:21:08 GMT
- Title: Gender Differences in AI Literacy Workshop Outcomes and Deepfake Engagement
- Abstract summary: This study examines gender differences in AI literacy, safety awareness, and STEM career aspirations among Australian secondary students.<n>Using statistical regression methods, we found that pre- workshop, male students reported significantly higher STEM career interest.<n>Female students were significantly more likely to use AI for schoolwork and to seek advice from AI tools.
- Score: 39.984067260070844
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As Artificial Intelligence (AI) literacy initiatives expand in K-12 settings, understanding how gender shapes student baseline perceptions, tool-use, and responsiveness to interventions is essential for equitable curriculum design. This study examines gender differences in AI literacy, safety awareness, and STEM career aspirations among Australian secondary students (Years 7, 8, and 10; N(pre) = 199, n(post) = 136) from two co-educational government schools who participated in a one-day AI literacy workshop. Using statistical regression methods controlling for year level and school, we found that pre-workshop, male students reported significantly higher STEM career interest across all three domains (AI, computer science, and engineering), while female students were significantly more likely to use AI for schoolwork and to seek advice from AI tools. Gender-differentiated patterns also emerged in deepfake behaviours: males were significantly more likely to have created or shared deepfake content. Both genders improved in AI knowledge post-intervention, yet females showed a richer profile of gains: wider conceptual understanding, greater confidence, and meaningful increases in AI and CS career interest that partially narrowed the gender STEM gap. These findings highlight the need for gender-responsive AI curricula, particularly deepfake safety education for male students, and demonstrate that even single-day workshops can narrow gender gaps in STEM aspirations and AI confidence.
Related papers
- A Critical Discourse Analysis of Gender Representation in Software Engineering Education Videos on YouTube [54.52098098905923]
This study presents a critical discourse analysis of 200 manually analysed English and German software engineering tutorials on YouTube.<n>Our results show that male characters and masculine linguistic defaults dominate the tutorials.<n>We identified an agency gap, in which technical and decision-making roles are almost exclusively assigned to male actors.
arXiv Detail & Related papers (2026-06-16T19:18:37Z) - Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
arXiv Detail & Related papers (2026-05-02T05:26:24Z) - AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventions [38.350232667249095]
Deepfakes and other forms of synthetic media pose growing safety risks for adolescents.<n>This study evaluates the impact of Day of AI Australia's workshop-based intervention on Australian secondary students.
arXiv Detail & Related papers (2026-01-30T02:55:53Z) - Women Worry, Men Adopt: How Gendered Perceptions Shape the Use of Generative AI [0.0]
Generative artificial intelligence (GenAI) is diffusing rapidly, yet its adoption is strikingly unequal.<n>We show that women adopt GenAI substantially less often than men because they perceive its societal risks differently.<n>We construct a composite index capturing concerns about mental health, privacy, climate impact, and labor market disruption.
arXiv Detail & Related papers (2026-01-07T12:47:39Z) - AI's assigned gender affects human-AI cooperation [0.0]
This study investigates how human cooperation varies based on gender labels assigned to AI agents.<n>In the Prisoner's Dilemma game, 402 participants interacted with partners labelled as AI (bot) or humans.<n>Results revealed participants tended to exploit female-labelled and distrust male-labelled AI agents more than their human counterparts.
arXiv Detail & Related papers (2024-12-06T17:46:35Z) - Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
We study gender bias in 22 popular image-to-text vision-language assistants (VLAs)<n>Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances.<n>To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance.
arXiv Detail & Related papers (2024-10-25T05:59:44Z) - "My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Law [0.0]
This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations.
By incorporating cognitive psychology and feminist legal theory, we examine how data used for AI training can foster gender diversity and fairness.
arXiv Detail & Related papers (2024-06-27T20:03:27Z) - AI for social science and social science of AI: A Survey [47.5235291525383]
Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities.
The increasing human-like capabilities of AI are also attracting attention in social science research.
arXiv Detail & Related papers (2024-01-22T10:57:09Z) - "I'm Not Confident in Debiasing AI Systems Since I Know Too Little":
Teaching AI Creators About Gender Bias Through Hands-on Tutorials [11.823789408603908]
School curricula fail to educate AI creators on this topic.
Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women.
arXiv Detail & Related papers (2023-09-15T03:09:36Z) - Towards Understanding Gender-Seniority Compound Bias in Natural Language
Generation [64.65911758042914]
We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models.
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
arXiv Detail & Related papers (2022-05-19T20:05:02Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.