The rapid adoption of ChatGPT and other large language models (LLMs) in education has created new opportunities for human-AI collaboration research, e.g., studying interactions, automating support or implementing novel ways of assessment. However, existing methods for retrieving ChatGPT conversation data -either through OpenAI's API or manual transcription-are limited by technical, financial, and scalability constraints. This paper introduces chatGPTscrapeR, an open-source R package and Shiny web application that automates the extraction of ChatGPT conversation data from URLs. Thus, it enables researchers and educators to efficiently retrieve, organize, and subsequently analyze interaction logs, and their metadata. The retrieved data are ready to be assessed if they are part of an assignment or analyzed using different methods. In all such cases, automating the retrieval of human-AI interactions is instrumental for an efficient analysis of such interactions and for creating modern AI-enabled learning systems.

While learning analytics dashboards (LADs) are the most common form of LA intervention, there is limited evidence regarding their impact on students’ learning outcomes. This systematic review synthesizes the findings of 38 research studies to investigate the impact of LADs on students' learning outcomes, encompassing achievement, participation, motivation, and attitudes. As we currently stand, there is no evidence to support the conclusion that LADs have lived up to the promise of improving academic achievement. Most studies reported negligible or small effects, with limited evidence from well-powered controlled experiments. Many studies merely compared users and non-users of LADs, confounding the dashboard effect with student engagement levels. Similarly, the impact of LADs on motivation and attitudes appeared modest, with only a few exceptions demonstrating significant effects. Small sample sizes in these studies highlight the need for larger-scale investigations to validate these findings. Notably, LADs showed a relatively substantial impact on student participation. Several studies reported medium to large effect sizes, suggesting that LADs can promote engagement and interaction in online learning environments. However, methodological shortcomings, such as reliance on traditional evaluation methods, self-selection bias, the assumption that access equates to usage, and a lack of standardized assessment tools, emerged as recurring issues. To advance the research line for LADs, researchers should use rigorous assessment methods and establish clear standards for evaluating learning constructs. Such efforts will advance our understanding of the potential of LADs to enhance learning outcomes and provide valuable insights for educators and researchers alike.