Speaker
Description
In recent years, artificial intelligence (AI) has rapidly advanced across multiple domains, reshaping how individuals learn, work, and make decisions. While AI systems increasingly outperform humans in rule-based and pattern-recognition tasks, human intelligence remains diverse and multifaceted, resisting standardization. This raises critical questions about how “intelligence” is defined and understood in the context of machines. Despite its transformative potential, AI adoption is uneven across regions, disciplines, and user groups. Moreover, significant risks accompany its use, including misinformation, hallucinated content, data privacy concerns, and the potential manipulation of user behavior. These challenges highlight the urgent need for AI literacy as a fundamental competence in higher education.
AI literacy can be defined as the ability to understand, use, and critically evaluate AI systems. It encompasses conceptual knowledge of how AI works, awareness of its limitations, critical assessment of outputs, and ethical, responsible use. At the European level, the AI Act (EU Regulation 2024/2689) establishes a comprehensive risk-based framework, reinforcing the need for informed and responsible engagement with AI technologies.
This paper proposes a three-level educational model for integrating AI literacy into higher education: (1) foundational AI literacy for all students, (2) applied AI competencies tailored to specific disciplines, and (3) advanced specialization in AI and ethics. Such an approach supports critical thinking, reduces technological dependency, and prepares graduates for an AI-driven labor market. Ultimately, embedding AI literacy into university curricula is essential for fostering responsible innovation and ensuring that future professionals can navigate the opportunities and risks of AI effectively.
