The GenAI-LA workshop aims to highlight the significant impact generative artificial intelligence (GenAI) can have on learning analytics (LA). GenAI offers a wide range of benefits, including the ability to process unstructured data automatically, create adaptive educational materials, and improve the way LA findings are communicated through engaging stories and thorough explanations. This workshop aims to bring together a diverse mix of professionals, including learning scientists, LA practitioners, software developers, and AI experts, to engage in meaningful discussions and collaborations. Our goal is to thoroughly explore and envision how GenAI can play a crucial role in advancing both the research and application of learning analytics. We received a total of 12 paper submissions. Following a thorough peer review process, we accepted seven full papers and two short papers. Each paper presents a unique perspective on the potential of GenAI to transform the field of LA and education, from addressing data sparsity in intelligent tutoring systems to supporting self-regulated learning and beyond.
Generative AI for Critical Analysis: Practical Tools, Cognitive Offloading and Human Agency by Simon Buckingham Shum discusses the implications of GenAI for critical analysis tasks. The paper highlights practical examples where GenAI can support intellectual engagement and examines the balance between the benefits and risks of cognitive offloading to AI.
Whitehead et al. introduces the GMA method, a systematic framework for applying GenAI to the analysis of multimodal learning process data. This paper presents a case study to demonstrate the method's applicability and effectiveness, contributing to the advancement of learning analytics methods.
Test by Bor-Chen Kuo et al. assesses the performance of advanced GenAI models in Chinese reading comprehension tasks. The study compares these models' performances with that of fifthgrade students, offering insights into the educational applications of GenAI.
Supporting Self-Regulated Learning with Generative AI: A Case of Two Empirical Studies by Jacqueline Wong and Olga Viberg explores the use of GenAI chatbots to enhance self-regulated learning and learning performance. The paper presents preliminary results from two empirical studies, contributing to the discussion on personalising SRL support using AI. Together, these papers represent a comprehensive exploration of the transformative potential of GenAI in learning analytics and education, offering valuable insights and innovative solutions to the challenges faced by learners, educators, and researchers in the field.