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.

人工智能的生成性挑战和学习分析领域的框架与人工智能工具之间的合作关系