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

Abstract

As generative AI becomes increasingly integrated into higher education, its in-fluence is reshaping what it means to learn, teach, and think. This paper ques-tions whether education should continue prioritizing problem-solving in an era where machines excel at it. Drawing from constructivist theory, Bloom’s Tax-onomy, 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, in-quiry-based model of education that redefines learning beyond the mastery of problems toward the cultivation of imagination, ethical reasoning, and epis-temic curiosity.