[论文解读] Promises and challenges of generative artificial intelligence for human learning

Lixiang Yan, Samuel Greiff+2 more|arXiv (Cornell University)|Aug 22, 2024

Engineering Education and Technology被引用 6

一句话总结

本观点综述了生成式人工智能（GenAI）如何通过个性化支持、多样化资源、反馈和评估来改变学习，同时强调伦理、方法论和实际挑战。

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.

研究动机与目标

• 从学习科学、教育技术和人机交互（HCI）的视角，推动并构建将 GenAI 融入人类学习的框架。
• 识别 GenAI 如何扩展个性化学习支持、多样化资源、提供反馈并实现创新性评估。
• 评估与负责任部署相关的伦理、方法论与评估挑战。
• 提出指导以人为本设计与政策的研究议程与需求。

提出的方法

• 综合 GenAI、学习科学和教育技术等领域多条文献的见解。
• 讨论近端探知区（Zone of Proximal Development）等理论基础以及基于探究的学习，以支撑 GenAI 的应用。
• 描述具体的 GenAI 支持的学习活动：辅导、资源生成、反馈与自适应评估。
• 突出经验缺口并提出评估 GenAI 影响的严格研究设计。
• 概述伦理、公平性和人工智能素养方面的考量，并呼吁将人类参与到循环开发中。

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 learn

实验结果

研究问题

• RQ1 GenAI 在学习支持、资源、反馈和评估方面的核心承诺是什么？
• RQ2 在教育领域中，GenAI 会带来哪些伦理、公平性和方法论挑战？
• RQ3 应如何培养 AI 素养、循证决策和方法论严谨性，以引导 GenAI 在学习中的应用？
• RQ4 评估人机学习互动需要哪些未来的研究方向和方法标准？

主要发现

• GenAI 可作为可扩展的认知促进者，丰富反馈、资源和自适应评估。
• 利用 GenAI 进行内容生成支持多样化的多媒体材料，但需要教育者对准确性和教学法进行验证。
• 幻觉现象和缺乏透明度对学习内容和评估的完整性构成风险。
• 伦理关注包括透明度、隐私、平等和受益性，需要治理和 AI 素养。
• 评估实践可能需要转向人机混合认知以及真实的多模态评估。
• 方法论的严谨性和循证决策对于避免高估 GenAI 的效益至关重要。

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 learnin