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About the workshop:
Data Storytelling (DS) in education has provided tools and methods to support data experts to make stories more accessible to non-data experts (i.e., learners, educators, and professional staff) while also allowing data savvy stakeholders (i.e., researchers), using human-centred approaches, in the creation process. With the rise of Generative AI (GenAI), interest has grown in exploring its potential to automate the process of creating effective data stories, as this is often a time-consuming task. This workshop seeks to foster critical discussion and hands-on activities around the opportunities and challenges of integrating GenAI tools and methods into DS in educational contexts. Key topics for exploration include: (i) How can GenAI be integrated into DS stages (analysis, planning, implementation, and communication) to automate the generation of actionable data stories that improve teaching and learning outcomes? (ii) How can researchers, designers, and educational stakeholders adapt and adopt GenAI tools and produce meaningful learning and teaching stories? (iii) What challenges and risks arise from including GenAI in DS stages? This workshop aims to bring together experts in storytelling and GenAI within the LA community to discuss and shape the future of DS in LA, addressing both its challenges and opportunities.
Motivation and Background
In recent years, the Learning Analytics (LA) community has embraced new methods from Human-centred Interaction (HCI) and Information Visualisation (InfoVis) communities, such as the adoption of Data Storytelling (DS) in LA interfaces (e.g., Maheshi et al., 2024; Fernandez-Nieto et al., 2024). DS, which incorporates narratives, visual elements, and storytelling techniques in large and complex data, aims to support and guide the interpretation and communication of educational insights to non-data experts stakeholders (i.e., students and teachers) (Ryan, 2016).
The current GenAI’s capabilities to process, generate, and combine together diverse data types (e.g., datasets, text, images, audio) have enabled a human-AI partnership to support humans in diverse and complex tasks. While recent studies have begun to explore AI-assisted automation of crafting data stories to enhance accessibility and scalability (Li et al., 2023; Li et al., 2024; Ye et al., 2024), this remains a nascent area of empirical research. Given the limited work in this field, this workshop seeks to explore how GenAI can support various stages of Data Storytelling, including analysis, planning, implementation, and communication (Li et al., 2024) to support teaching and learning. Specifically, this workshop will explore benefits and challenges of using GenAI as a design material during the DS creation with stakeholders, and GenAI-enabled authoring tools for crafting data stories, uncovering topics such as overreliance, trust, human and AI roles, bias mitigation and impact on learning.
The workshop will provide participants with a space to reflect on and critically discuss the following aspects: How can GenAI be integrated into DS stages? How can researchers and practitioners adapt and adopt GenAI tools to produce meaningful learning data stories? What are the challenges and risks of relying on AI-assisted data stories? To begin, the organisers will present a review of seminal work (tools, techniques, empirical results) on using GenAI to automatically craft data stories in LA and other fields, such as InfoVis and HCI. This review will serve as a starting point for discussions on the opportunities, challenges, and risks of using GenAI to automate DS, focusing on how these stories can help educational stakeholders make sense of data traces and take actionable steps to improve their practices.
References
Ryan, L. (2016). The visual imperative: Creating a visual culture of data discovery. Amsterdam, Netherlands: Elsevier Science.
Li H., Ying L., Zhang H., Wu Y., Qu H., and Wang Y. 2023. Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks. In CHI ‘23. Association for Computing Machinery, New York, NY, USA, Article 173, 1–16.https://doi.org/10.1145/3544548.3580965
Li H., Wang Y., and Qu H. (2024). Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration. In CHI ‘24. Association for Computing Machinery, New York, NY, USA, Article 845, 1–19.https://doi.org/10.1145/3613904.3642726
Ye, H., Zhang, J., Wu, Y., & Cao, N. (2024). Generative AI for visualization: State of the art and future directions. IEEE Transactions on Visualization and Computer Graphics, 30(1), 1211-1221. https://doi.org/10.1016/j.visinf.2024.04.003
Fernandez-Nieto, G., Martinez-Maldonado R., Echeverria, E., Kitto, K., Gašević, D., and Buckingham Shum, S. 2024. Data Storytelling Editor: A Teacher-Centred Tool for Customising Learning Analytics Dashboard Narratives. In Proceedings of the 14th Learning Analytics and Knowledge Conference (LAK ‘24). Association for Computing Machinery, New York, NY, USA, 678–689. https://doi.org/10.1145/3636555.3636930
Maheshi B., Milesi M. E., Palihena H., Zheng A., Martinez-Maldonado R., and Tsai Y. (2024). Data Storytelling for Feedback Analytics. In LAK Workshops (pp. 150-161)