Generative AI in the Information Society: Implications for Higher Education and Research

Abstract

Generative Artificial Intelligence (GenAI) represents a fundamental shift in AI development, moving from rule-based systems to neural networks capable of creating novel content and solving complex problems through pattern recognition and contextual understanding. This evolution challenges traditional Computer Science (CS) paradigms, as evidenced by innovations in large language models and diffusion-based image generation. This paper investigates how GenAI's emergence affects education and research in computer science and related fields. Through White's cultural model—examining technological, societal, and institutional dimensions—we analyse how GenAI's capabilities diverge from traditional CS approaches in both theory and practice. Our research reveals specific challenges for higher education, including the need to teach contextual reasoning, handle emergent behaviors, and develop adaptive problem-solving skills. We propose educational strategies such as project-based learning with GenAI tools and cross-disciplinary integration. These recommendations aim to establish GenAI as a distinct academic discipline while preparing students and researchers for its increasing role in scientific and professional practices.