AI in Culture: "Truth or Fiction?" Navigating Deep Reading and Education in the Age of Misinformation

How can we ensure that reading, learning, and openly shared knowledge equip people to distinguish truth from fabrication?

In an era where misinformation spreads faster than facts, the core mission of education—to foster informed and critical thinkers—is under increasing pressure. Research by Stanford University (Wineburg et al., 2016) showed that over 80% of middle school students struggled to distinguish between credible and non-credible online content—a challenge that has only intensified with the rise of generative AI.

As AI tools become more powerful and blur the boundaries between reliable and unreliable information, educators, publishers, media professionals, and policymakers face an urgent and shared responsibility. We are witnessing a structural transformation in how knowledge is created, distributed, and consumed.

Despite ongoing discussions that are often fragmented and led by individual sectors—there is still no inclusive, cross-sector platform that connects academia, industry, education, technology, and policy. This event aims to fill that gap by focusing on real-world impact rather than hype, through a global and interdisciplinary dialogue.

Recent research shows that media and publishing organisations are increasingly adopting collaborative approaches to AI integration. Journalists, editors, and content creators are working alongside AI as partners, raising important questions around linguistic adaptation, ethics, capability development, and training. This shift highlights the need to better understand how human–AI collaboration emerges, how institutions can support it, and how quality and integrity can be maintained.

The open knowledge movement offers a powerful and proven counter-model. Open Educational Resources (OER), freely available, openly licensed learning materials endorsed by UNESCO's 2019 OER Recommendation, have demonstrated that when knowledge is verifiable, collaboratively maintained, and openly shared, communities are structurally better equipped to resist manipulation. Creative Commons licensing, open-access publishing, and open peer review are not idealistic aspirations anymore but functioning infrastructures that protect the origin and integrity of knowledge and resist the degradation of reliable information that misinformation thrives on. In an age where AI systems are trained on the world's knowledge, the openness, quality, and diversity of that knowledge base have deep implications for democratic societies.

Cultural identity also plays a key role in shaping AI partnerships. Different socio-cultural contexts influence collaboration models, privacy expectations, and approaches to technology adoption. From a semiotic perspective, AI is not merely a computational tool but a cultural actor participating in meaning-making and communication—what we could refer to as Allied Intelligence.

The promise of Allied Intelligence can only be realised if the partnership is genuinely open. Proprietary AI systems trained on undisclosed data, with opaque reasoning, risk becoming amplifiers of the same misinformation they are meant to counter. Open-source AI models, openly curated training corpora, and transparent algorithmic pipelines represent the structural conditions under which AI can truly ally itself with truth rather than with whoever controls the data. Openness is not a technical preference, it is an ethical and democratic imperative.

The growing field of Socio-cultural Artificial Intelligence (SCAI) reflects this shift, with applications across media, governance, education, and cultural industries. This raises important questions about ethical frameworks, cultural diversity, and policy approaches that support inclusive and context-sensitive AI integration.