Beyond Problem-Solving: The Future of Learning in an AI-Driven World

Abstract

As generative AI becomes increasingly integrated into higher education, its influence is reshaping what it means to learn, teach, and think. This paper questions whether education should continue prioritizing problem-solving in an era where machines excel at it. Drawing from constructivist theory, Bloom’s Taxonomy, and the Cattell-Horn-Carroll model of intelligence, the paper proposes a shift from utilitarian models of learning to a framework that emphasizes conceptual agility, dialectical reasoning, and meaning-making. We explore how AI may alter cognitive development, risk passive knowledge acquisition, and deepen achievement gaps—while also offering opportunities for enhanced scaffolded learning. Ultimately, this paper argues for a human-centered, inquiry-based model of education that redefines learning beyond the mastery of problems toward the cultivation of imagination, ethical reasoning, and epistemic curiosity.