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Promises and challenges of generative artificial intelligence for human learning

Nature Human Behaviour volume 8, pages 1839–1850 (2024)Cite this article

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Abstract

Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human–computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI’s effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

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Fig. 1: Examples of human–AI interactions in human learning.

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Acknowledgements

This study was supported by grants from the Australian Research Council (grant agreement numbers DP220101209 and DP240100069 to D.G.). L.Y.’s work is fully funded by the Digital Health Cooperative Research Centre (DHCRC). D.G.’s work was supported in part by the DHCRC and Defense Advanced Research Projects Agency (DARPA) through the Knowledge Management at Speed and Scale (KMASS) programme (HR0011-22-2-0047). The DHCRC is established and supported under the Australian Government’s Cooperative Research Centres Program. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the US Government. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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    Present address: Centre for International Student Assessment (ZIB) & School of Social Sciences and Technology, Technical University of Munich, Munich, Germany

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  1. Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia

    Lixiang Yan & Dragan Gašević

  2. Department of Behavioral and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Samuel Greiff & Ziwen Teuber

  3. Department of Educational Psychology, Goethe-University Frankfurt, Frankfurt, Germany

    Samuel Greiff

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Yan, L., Greiff, S., Teuber, Z. et al. Promises and challenges of generative artificial intelligence for human learning. Nat Hum Behav 8, 1839–1850 (2024). https://doi.org/10.1038/s41562-024-02004-5

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