Generative artificial intelligence (GenAI), exemplified by ChatGPT, Midjourney, and other state-of-the-art large language models and diffusion models, holds significant potential for transforming education and enhancing human productivity. While the prevalence of GenAI in education has motivated numerous research initiatives, integrating these technologies within the learning analytics (LA) cycle and their implications for practical interventions remain underexplored. This paper delves into the prospective opportunities and challenges GenAI poses for advancing LA. We present a concise overview of the current GenAI landscape and contextualise its potential roles within Clow's generic framework of the LA cycle. We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions. As the lines blur between learners and GenAI tools, a renewed understanding of learners is needed. Future research can delve deep into frameworks and methodologies that advocate for human-AI collaboration. The LA community can play a pivotal role in capturing data about human and AI contributions and exploring how they can collaborate most effectively. As LA advances, it is essential to consider the pedagogical implications and broader socioeconomic impact of GenAI for ensuring an inclusive future.

翻译：生成式人工智能（GenAI），以ChatGPT、Midjourney及其他先进大语言模型和扩散模型为代表，在变革教育及提升人类生产力方面潜力巨大。尽管GenAI在教育中的普及已推动众多研究项目，但这些技术如何融入学习分析循环及其对实际干预举措的影响仍鲜有探讨。本文深入分析了GenAI为推进学习分析所带来的潜在机遇与挑战。我们概述了GenAI的当前发展格局，并基于Clow提出的学习分析通用框架，将其潜在作用置于具体语境中。我们认为GenAI在分析非结构化数据、生成合成学习者数据、丰富多模态学习者交互、推动交互式与解释性分析，以及促进个性化与自适应干预方面可发挥关键作用。随着学习者与GenAI工具间界限日益模糊，亟需重新定义学习者的概念。未来研究可深入探索倡导人机协作的框架与方法论。学习分析社区可通过采集人类与人工智能贡献数据并探究其最优协作模式发挥关键作用。随着学习分析的发展，必须审慎考量GenAI对教学实践及社会经济的更广泛影响，以确保包容性未来的实现。