Generative AI has become part of classrooms, homework and group chats. The Future Report, commissioned by Google and delivered by Livity, found that 88% of respondents aged 13–15 in the European countries surveyed reported using AI tools for learning or creative tasks at least a couple of times a week, according to the AI Literacy Framework website. But being surrounded by a technology is not the same as benefiting from it equally, and evidence suggests that AI risks opening a new chapter in the gender gap in tech.
A new gap in the making
The 2026 working paper Global Evidence on Gender Gaps and Generative AI Over Time, by Katelyn Cranney, Solène Delecourt and Rembrand Koning, brings together 76 sources spanning more than 100 countries and over 300,000 people. Across the pooled data, 39.3% of women reported using generative AI, compared with 47.8% of men: an 8.5 percentage-point difference. Equal access alone does not ensure equal use: gaps also appear within the same occupations and organisations.
The barriers identified include differences in familiarity, training and confidence, alongside concerns about trust and whether AI-assisted work will be seen as cheating. These findings will sound familiar to anyone who has followed our work. They echo the cultural script we have described in the Italian context, where girls are raised to be careful and perfect rather than to experiment.
Why it matters for work
This is not only about who tries out a chatbot. In May 2025 the International Labour Organization published Generative AI and Jobs: A Refined Global Index of Occupational Exposure. In high-income countries, occupations in the highest exposure category account for a far larger share of women’s employment than men’s (9.6% against 3.5%), largely because of the weight of administrative and clerical roles.
Exposure does not mean replacement: in most cases, it means that tasks will change. Yet that is exactly why the two findings, taken together, are worrying. If women’s jobs are the most transformed by AI, and women are the least likely to build familiarity with these tools, the gap is set to widen, unless education steps in early.
What young people need to know: three frameworks
Over the past two years, European and international institutions have worked to define what “AI literacy” actually means. Three frameworks are now the main reference points.
DigComp 3.0 (European Commission, Joint Research Centre, 2025). The fifth edition of the European Digital Competence Framework does not add AI as a separate subject: it weaves it through all 21 competences. Of its 362 competence statements, 14% mention AI explicitly and 68% implicitly. They include recognising that AI can produce plausible but inaccurate outputs, and that responsibility for checking them lies with the person using it; understanding that human and algorithmic biases shape the information we receive; and promoting inclusive, accessible technologies. An Italian translation has been available since March 2026.
The AI Literacy Framework (European Commission and OECD, 2026). Published on 18 June 2026 after a public draft in 2025, it is designed for primary and secondary education and will inform the PISA 2029 assessment of Media & AI Literacy. It organises competences into four domains (Engage with AI, Create with AI, Manage AI and Shape AI) and draws a clear line: AI literacy is not the same as using AI tools. Simply interacting with a chatbot does not build the knowledge and judgement needed to question it. Its learner expectations include explaining how biased data and design choices can produce unfair outcomes for different groups.
The UNESCO AI Competency Framework for Students (2024). The first global framework of its kind defines 12 competencies across four dimensions (human-centred mindset, ethics of AI, AI techniques and applications, and AI system design) and three levels of progression: understand, apply, create. Two of its recommendations speak directly to gender equality. AI tools recommended for schools should be validated to avoid gender bias, and students’ learning outcomes in AI should be analysed by gender, so that those falling behind can receive targeted support.
Where the frameworks meet
Read together, the three documents converge on a few shared ideas. AI literacy means understanding how systems work, not just how to operate them. It means evaluating outputs critically, recognising bias and taking responsibility for one’s choices. And it means seeing young people as future co-creators of AI, not only as its users.
What frameworks cannot do on their own is make girls feel that these competences are meant for them. Schools are still catching up, too: according to the OECD TALIS 2024 survey, among lower secondary teachers who had not used AI in teaching in the previous 12 months, three in four reported lacking the knowledge and skills to do so, on average across OECD education systems. (OECD source) As our research has shown, the how matters as much as the what: learning environments where mistakes become material for learning, collaborative activities, and technology connected to real problems and social impact.
This is the direction Sineglossa will take in the coming months, as we develop the Her Tech syllabus: an AI literacy pathway for younger learners that translates these European and global frameworks into concrete, gender-inclusive learning experiences. We will share more as the work takes shape.
If gender bias works like pre-installed software, AI literacy is a chance to rewrite the code before it is copied into the next generation of technology.
