Promises and challenges of generative artificial intelligence for human learning

TL;DR

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.

Abstract

Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation, and evaluation of human learning. This Perspective examines 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 personalised 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. 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 impact on human cognition, metacognition, and creativity. Humanity must learn with and about GenAI, ensuring it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

Figures (2)

• Figure 1: Overview of the impacts of generative artificial intelligence on human learning. The left side of the figure lists various learning impacts, which are categorised into promises (green), challenges (red), and needs (blue). The middle column presents key components associated with each learning impact. These components detail specific aspects that need to be addressed or leveraged to use generative AI as a tool for learning. The matrix on the right shows the five main groups involved in implementing these key components: learners, educators, researchers, policymakers, and technologists. The dots in each column indicate that the relevant group needs to make a substantive contribution to achieving the goals of the key component in the corresponding row.
• Figure 2: Examples of human-AI interactions in human learning.a, Learners receive personalised and adaptive support from generative AI tutors, which are co-designed with educators and have access to prior learner data and domain knowledge. b, Educators use generative AI to create multimodal learning resources, incorporating text, audio, and video content. c, Educators collaborate with generative AI to deliver multimodal feedback to learners. d, Generative AI agents use input requirements from educators, prior learner data, and domain knowledge to create assessment activities that evaluate learners.