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Generative artificial intelligence (GenAI), exemplified by ChatGPT,Midjourney, and other state-of-the-art large language models and diffusionmodels, holds significant potential for transforming education and enhancinghuman productivity. While the prevalence of GenAI in education has motivatednumerous research initiatives, integrating these technologies within thelearning analytics (LA) cycle and their implications for practicalinterventions remain underexplored. This paper delves into the prospectiveopportunities and challenges GenAI poses for advancing LA. We present a conciseoverview of the current GenAI landscape and contextualise its potential roleswithin Clow's generic framework of the LA cycle. We posit that GenAI can playpivotal roles in analysing unstructured data, generating synthetic learnerdata, enriching multimodal learner interactions, advancing interactive andexplanatory analytics, and facilitating personalisation and adaptiveinterventions. As the lines blur between learners and GenAI tools, a renewedunderstanding of learners is needed. Future research can delve deep intoframeworks and methodologies that advocate for human-AI collaboration. The LAcommunity can play a pivotal role in capturing data about human and AIcontributions and exploring how they can collaborate most effectively. As LAadvances, it is essential to consider the pedagogical implications and broadersocioeconomic impact of GenAI for ensuring an inclusive future.

Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle