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Thomas, D. R., Lin, J., Gatz, E., Gurung, A., Gupta, S., Norberg, K. et al. (2024). Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation. In Association for Computing Machinery (Ed.), Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 404-415). ACM.
https://doi.org/10.1145/3636555.3636896

has been cited by the following article:

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

  AUTHORS: Lawrence R. Burns

  KEYWORDS: Generative AI, Higher Education, Deep Learning, Bloom’s Taxonomy, Fluid Intelligence, Epistemic Curiosity

  JOURNAL NAME: Creative Education, Vol.16 No.4, April 22, 2025

  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.