Pre-conference Schedule

Despite the potential of learning analytics (LA) to enhance student learning in higher education, the adoption of learning analytics is still lagging. In this workshop, participants will share evidence and experiences with implementations of LA applications in higher education. The workshop aims to lower the threshold for a wider audience to engage with study data, thereby scaling LA. The focus is on increasing stakeholders’ engagement and data availability, with an emphasis on the methods and conditions for making data available to LA stakeholders. A key area of interest is the use of Learning Analytics Dashboards (LADs), aimed, for example, at improving student self-directed and self-regulated learning by actively engaging stakeholders to use the data. The workshop will provide insights into applications of dashboards, the commonalities and differences between them, as well as the potential and the use of AI to empower LADs. It will also focus on determining the theoretical principles that are not yet implemented in practice, and what is needed to accomplish more evidence-based LAD design. Accordingly, the workshop will provide a platform for a thorough discussion on the mapping between theory and practice in LA adoption.

Organizers:

Website:

https://sites.google.com/view/lak25adoptionworkshop/home?authuser=0

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

Building on the resounding success of the First International Workshop on GenAI-LA at LAK24, which ignited conversations and collaborations around practical tools and methodologies, the Second International Workshop on GenAI-LA aims to make even greater strides. The inaugural workshop attracted over 60 participants, published nine workshop papers, and received an impressive overall rating of 4.8/5. Over the past year, significant progress has been made, and this second workshop will bring together learning scientists, learning analytics practitioners, software engineers, and AI specialists. The focus will be on delving deeper into the actual impacts of GenAI technologies on human learning. We will explore the pivotal role of learning analytics in understanding and nurturing essential cognitive, metacognitive, and creative skills. In an era where human-GenAI collaboration is increasingly valued in education and the workplace, this workshop aims to envision and inspire future research in learning analytics and GenAI.

Organizers:

• Lixiang Yan (Monash University)
• Andy Nguyen (University of Oulu)
• Ryan S. Baker (University of Pennsylvania)
• Mutlu Cukurova (University College London)
• Dragan Gašević (Monash University)
• Kaixun Yang (Monash University)
• Yueqiao Jin (Monash University)
• Linxuan Zhao (Monash University)
• Yuheng Li (Monash University)

Website:

https://sites.google.com/monash.edu/genai-la-workshop-lak25/home

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

This half-day workshop explores the potential for combining learning analytics systems, such as those embedded in Digital Learning Platforms (DLP), with improvement science methodologies to address educational challenges. Learning analytics systems provide detailed data on student engagement and performance, while improvement science offers a structured framework for continuous, data-informed improvement. The workshop aims to equip participants with strategies for designing improvement cycles that leverage data, foster collaboration between practitioners and researchers, and produce scalable, equitable interventions. Through hands-on activities and case studies, participants will explore practical applications of these methods in various educational contexts.

Organizers:

Jojo Manai, Sharjah Private Education Authority

Jeremy Roschelle, Digital Promise

Website:

https://sites.google.com/carnegiehub.org/learningfromlargescaledata/home

Learning analytics offers tremendous potential to improve educational outcomes, but new measures and metrics often remain isolated within institutions. Building on the success of the First Workshop on New Measures & Metrics LAK24, this half-day workshop aims to collaboratively advance the development and dissemination of innovative analytic measures in education. Submissions of new metrics will be compiled on a website and presented at the event. Through mini-presentations, structured discussion, and breakout sessions, participants will exchange insights about creating, validating, and distributing novel metrics. The workshop will conclude by voting on the most promising measure and awarding a prize. By synthesizing diverse viewpoints, the workshop intends to catalyze the evolution and adoption of impactful new techniques in learning analytics. Outcomes will be shared through a public website, potential publications, and continued online community dialogue. This interactive workshop provides an exciting opportunity to collectively spur progress in developing the next generation of learning metrics.

Organizers:

• Charles Lang, Teachers College Columbia University
• Geraldine Gray, TU Dublin
• Ruth Cobos, Universidad Autonoma de Madrid

Website: https://github.com/charles-lang/measures-metrics-LAK25

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

This half-day interactive workshop emphasizes the role of adaptive lifelong learning in the dynamic landscape of learning analytics (LA). When learners, trainees, teachers, and trainers are confronted with AI in an educational context, they often face challenges such as information overload or the necessity to exhibit high degrees of flexibility to adapt to the rapid changes and continuous evolution of learning tools. By highlighting the virtues of AI for adaptive learning, the workshop examines the impact of AI as a source of support for bridging learning gaps and differences, to streamline automation, and so on. Furthermore, the workshop emphasizes the critical necessity for multidisciplinary expertise in the evolving LA domain, moving beyond its roots in computer science (technical knowledge) to encompass a broader educational perspective (didactical knowledge). With a specific focus on multi-criteria adaptive learning, the workshop advocates for collaboration among learners, educators, policymakers, researchers, and EdTech companies. The ultimate goal is to facilitate the development of relevant and evidence-based adaptive learning tools that significantly enhance and support lifelong learning.

Organizers:

• Alireza Gharahighehi, KU Leuven
• Rani Van Schoors, KU Leuven
• Paraskevi Topali, Radboud University
• Jeroen Ooge, Utrecht University

Website: https://itec.kuleuven-kulak.be/all25/

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

Hybrid Intelligence aims to enhance the collaboration between humans and machines by fostering mutual understanding and learning from each other. By leveraging the complementary strengths of both humans and AI, Hybrid Intelligence has the potential to achieve superior outcomes that neither human nor Artificial Intelligence (AI) can attain independently. This 1st workshop on Hybrid Intelligence is designed as a platform for dialogue and collaboration, highlighting the transformative potential of Hybrid Intelligence within the context of learning analytics. The workshop acts as a catalyst for enhancing the conceptualization, operationalization, and design of hybrid intelligence. By bringing together a transdisciplinary group of learning scientists, learning analytics practitioners, software engineers, and AI specialists, the workshop aims to facilitate a comprehensive exploration and envisioning of hybrid intelligence in learning analytics research and practice.

Organizers:

• Andy Nguyen (University of Oulu)

• Sanna Järvelä (University of Oulu)

• Mutlu Cukurova (University College London)

• Carolyn Rose (Carnegie Mellon University)

• Alyssa Wise (Vanderbilt University)

• Michail Giannakos (Norwegian University of Science and Technology)

• Kshitij Sharma (Norwegian University of Science and Technology)

• Luna Huynh (University of Oulu)

Website:

https://sites.google.com/view/hilak

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

The CROSSMMLA workshop series has focused on collecting and analyzing educational datasets from multiple modalities of interaction across physical and digital learning spaces. In this year’s workshop, we aim to explore how the rise of Generative AI (GenAI) models is transforming the landscape of multimodal learning analytics (MMLA) research, driving new possibilities for understanding and enhancing the learning process. In recent years, GenAI models have made impressive strides, particularly in their ability to process various types of data beyond text, including images, audio, and videos. This development has led to the use of Multimodal Large Language and Vision Models in MMLA research. However, a recent analysis highlighted several challenges that need to be addressed when applying GenAI in education, including concerns about data privacy, algorithmic bias, ethical use of AI-generated content, and the scalability of such models in diverse educational settings. Additionally, issues surrounding the transparency of AI decision-making, the environmental costs of training large models, and the societal implications of over-reliance on AI must be considered. To address these challenges, we propose a half-day workshop to explore the opportunities and complexities of Generative AI for advancing MMLA research.

Organizers:

• Namrata Srivastava – Vanderbilt University & Monash University
• Roberto Martinez-Maldonado – Monash University
• Daniele Di Mitri – DIPF | Leibniz Institute for Research and Information in Education
• Daniel Spikol – University of Copenhagen
• Vanessa Echeverria – Monash University & Escuela Superior Politécnica del Litoral
• Kateryna Zabolotna – University of Oulu
• Simon Knight – University of Technology Sydney
• Mohammad Khalil – University of Bergen
• Gloria Fernandez Nieto – Monash University
• Ruth Cobos – Universidad Autónoma de Madrid

Website:

https://crossmmla.org/

The data collected passively through professional interactions with workplace technologies has huge utility to support just-in-time, adaptive, and informal learning opportunities. Despite this, professional and workplace learning analytics remains largely underdeveloped in the context of learning analytics. This workshop is designed to bring together dispersed individuals interested in this exciting field to showcase what has been done to date, discuss key challenges in developing the area further, and set an agenda for future development in this space. Through this workshop, we aim to bring together key individuals interested in this space and begin to build a subcommunity of researchers interested in this area.

Organizers:

• Anna Janssen, Faculty of Medicine and Health, The University of Sydney
• Emma Nicholls, Faculty of Medicine and Health, The University of Sydney
• Bernard Bucalon, Faculty of Engineering, The University of Sydney
• Tobias Ley, Tallinn University
• Martin Pusic, Harvard Medical School
• Judy Kay, Faculty of Engineering, The University of Sydney
• Tim Shaw, Faculty of Medicine and Health, The University of Sydney

Website:

https://pla25.practiceanalytics.net/

The first four editions of the Workshop on Learning Analytics and Assessment were successfully organized at LAK21-24 conferences, resulting in multiple post-workshop collaborations and a special issue in a journal. In this workshop, we intend to address some of the key open challenges in learning analytics that are related to the use of learning analytics in formative and summative assessment; measurement of learning progression; reliability and validity of data collection and analysis; and assurance of assessment trustworthiness, particularly given the emergence of generative artificial intelligence (AI) methods. An open call for contributions will be distributed to solicit brief descriptions of current research and practice projects for roundtable-style discussions with workshop participants. Expected outcomes are the further formation of a community of practice and possible follow-up publications and special issues in journals.

Organizers:

• Dragan Gašević, Monash University

• Mladen Raković, Monash University

• Blaženka Divjak, University of Zagreb

• Yoon Jeon Kim, University of Wisconsin

• Abhinava Barthakur, University of South Australia

• Lukas Liu, Hong Kong University

Website:

https://sites.google.com/monash.edu/lak25assess/

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

Game-Based Learning Analytics (GBLA) is an emerging field that combines game design principles with learning analytics to create personalized, engaging, and data-driven educational experiences. Despite significant contributions in this space, there is a lack of structured collaboration within the learning analytics community. This workshop aims to address that gap by bringing together researchers and practitioners interested in the intersection of games and learning analytics. Through this half-day workshop, we aim to formalize a community around GBLA, set foundational principles, and coordinate initial scholarly contributions while laying the groundwork to establish a special interest group.

Organizers:

• Maurice Boothe, NYU
• Xavier Ochoa, NYU
• David Gagnon,
• Luke Swanson and
• Erik Harpstead

Website:

https://sites.google.com/nyu.edu/gbla

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

This workshop seeks to explore the need for international industry standards in AI in education, to understand the requirements and scope for any such standards, identify key stakeholders, and consider the process by which standards might be collaboratively created and agreed. The workshop will consist of a series of short talks and discussions, initially establishing the current needs and expectations of Higher Education Institutions in relation to AI tools; then discussing the enterprise opportunities offered by the use of AI in education and finally, by considering how and what kind of industry standards might be created to respond to the needs in the education sector. The aim of this event is to work to establish a network of professionals who can work collaboratively together in the development and realization of international industry standards in AI in education.

Organizers:

• Dr Haiming Liu, University of Southampton

Website:

https://sites.google.com/view/ai-in-education-standards/home

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

This is the proposal for the third interactive workshop on Measuring and Facilitating self-regulated learning (SRL). Measuring SRL using unobtrusive trace data and facilitating SRL through real-time analysis of such data have been identified as highly valuable research directions. However, significant challenges remain in this area, including: (i) the detection, measurement, and validation of SRL processes using trace data is still a debated issue; (ii) the design principles for effective interventions and the complex conditions under which these interventions facilitate learning are not yet well understood; and (iii) the potential benefits of advanced AI techniques, such as ChatGPT, for learners, as well as the mechanisms through which learners can effectively co-regulate with AI, remain unclear. Therefore, we aim to enhance SRL measurement and facilitation through a full-day workshop, providing participants hands-on experience with our AI-powered Trace-SRL tools. We aim to share our platform, tasks, data, and project experiences, then discuss an annual international joint study to initiate international collaboration and deepen SRL research. An open call for contributions will be distributed, and the participants will join roundtable-style discussions and hands-on co-design activities. Expected outcomes are forming a community of practice, potential collaborative projects, and possible follow-up joint publications.

Organizers:

Xinyu Li, Monash University

Yizhou Fan, Peking University

Mladen Raković, Monash University

Linxuan Zhao, Monash University

Dragan Gašević, Monash University

Website:

https://sites.google.com/monash.edu/lak25trace-srl/home

Multimodal data integration is one of the major analytical challenges for Multimodal Learning Analytics (MMLA). Continuing conversations begun in previous LAK CROSSMMLA workshops, this workshop will focus on a relatively new conceptual and methodological framework, Transmodal Analysis (TMA), that addresses the data integration challenge using a functions-not-fusion approach. In this workshop, TMA adopters, MMLA methodologists, and learning scientists will discuss the affordances and challenges of this new approach. Participants will gain hands-on experience and learn how to use TMA on their own multimodal data. This workshop, thus, provides a venue for the MMLA SIG and others interested in MMLA to exchange expertise and develop future collaborations.

Organizers:

• Yeyu Wang, University of Wisconsin–Madison
• David Shaffer, University of Wisconsin–Madison
• Andrew Ruis, University of Wisconsin–Madison
• Brendan Eagan, University of Wisconsin–Madison
• Zachari Swiecki, Monash University
• Mamta Shah, Elsevier
• Sanna Järvelä, University of Oulu
• Andy Nguyen, University of Oulu
• Zack Carpenter, University of Minnesota
• David DeLiema, University of Minnesota
• Liv Nøhr, University of Copenhagen
• Daniel Spikol, University of Copenhagen
• Crina Damsa, University of Oslo
• Rogers Kaliisa, University of Oslo

Website:

https://www.epistemicanalytics.org/2024/08/16/lak25-workshop-transmodal-analysis-a-new-conceptual-and-methodological-framework-for-multimodal-learning-analytics/

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

This interactive workshop brings together researchers who have explored the use of large language models (LLMs) for the processing and analysis of qualitative data, in both the learning analytics community and other communities related to educational technologies. The workshop will feature presentations of research examples and demonstrations of these applications, providing insights into the methodologies and tools that have proven effective for automating qualitative analysis across research contexts. Additionally, the session will address challenges associated with the application of LLMs in education, such as data privacy, ethical considerations, and ways to build community and shared resources. Attendees will share their experiences and contribute to a collective understanding of best practices in the use of AI for qualitative research. Participants will engage in discussions and hands-on activities to understand the capabilities and limitations of LLMs in handling qualitative data. An output of the workshop will include a plan for developing a systematic review of progress in using LLMs for qualitative data analysis. Large Language Models, Qualitative Data Analysis, AI in Education, Natural Language Processing.

Organizers:

• Amanda Barany, University of Pennsylvania

• Ryan S. Baker, University of Pennsylvania

• Andrew Katz, Virginia Tech Engineering Education 

• Jionghao Lin, Carnegie Mellon University & Monash University 

Website:

https://sites.google.com/view/lak-25-workshop-llms-for-qual/home

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

In this workshop, we explore the limits of learning analytics. We acknowledge some limits in learning analytics raised in recent scholarly discourse. In response, we reframe limits as the horizons of learning analytics—areas of potential and excitement. We introduce perspectives that may serve as useful guides through new frontiers from psychometrics, sociology, and learning theory. This workshop blends collaborative theoretical reflection and practical research design considerations. In this workshop, participants are positioned as collaborators, and the workshop leaders facilitate discussion by highlighting three relevant concepts in theory, providing summaries of research, and designing resources and activities to structure reflection, debate, and envision the methodologies underpinning our work into the future.

Organizers:

• Danielle Hagood, University of Copenhagen

• Liv Nøhr, University of Copenhagen

Website:

https://sites.google.com/view/lak25-limits/

Artificial Intelligence (AI) is reshaping industries and redefining leadership. This workshop, AI Essentials for Leaders, provides academic and industry leaders with the critical tools and understanding needed to navigate this AI-driven transformation. Drawing from the forthcoming books by Alfred Essa and Teresa Martin-Retortillo in the AI Essentials for Leaders series (DeGruyter Brill), the workshop will focus on three key themes:

1. Understanding how AI works: A technical primer for non-technical people, covering the basic mechanisms of AI.
2. Leveraging AI for product innovation: An overview of innovation models for technical product development.
3. Technology and business innovation: Exploring how AI-driven technological advancements can be combined with innovative business strategies to drive long-term growth.

Participants will gain insights into both the technical underpinnings of AI and its strategic applications across sectors. Through case studies and collaborative discussions, leaders will develop the ability to combine AI innovation with business strategy to create sustained competitive advantage.

Learning Objectives:
1. Understand core AI concepts, such as machine learning, deep learning, and generative models, in a non-technical, accessible way.
2. Explore the intersection of technology and business innovation in the AI era.
3. Analyze the role of AI in driving both product and process innovation across various industries.
4. Develop practical skills for aligning AI-driven technological advancements with business strategy and leadership priorities.
5. Gain insights from real-world examples and case studies on how AI is transforming decision-making and leadership.

Workshop Structure:
The workshop will be highly interactive, blending theoretical insights from the AI Essentials
for Leaders series with practical applications and discussions.
1. Introduction:

Essa and Martin-Retortillo will introduce the workshop themes and outline how AI is transforming both technological innovation and business strategy.

2. Understanding AI: A Technical Primer for Leaders:

This session will provide a non-technical introduction to key AI concepts, including computation, machine learning, deep learning, and generative AI.

3. AI and Business Innovation: Strategic Alignment.

This session will explore how AI is reshaping business models, customer engagement, and competitive dynamics. Leaders will learn how to align AI-driven technology innovation with broader business strategies, focusing on long-term growth and organizational transformation.

4. Case Study Breakout Groups

Participants will work in small groups to analyze case studies of organizations that have successfully integrated AI into their business strategies. Each group will focus on how the organization combined technological advancements with strategic business innovation to drive success. Groups will present their findings and discuss how these lessons can be applied to their own organizations.

5. AI Leadership Panel Discussion

Essa and Martin-Retortillo will moderate a panel discussion of invited AI experts, exploring the opportunities and challenges of leading in the AI era. Topics will include ethical considerations, decision-making under uncertainty, and fostering collaboration between technical and non-technical teams.

6. AI Leadership Toolkit:

The workshop will conclude with a practical toolkit for leaders, including decision-making frameworks, key readings from the AI Essentials for Leaders
series, and templates for aligning AI innovations with business strategy. Participants will also be given access to a website for ongoing collaboration and
discussion of topics.

Target Audience:
This workshop is designed for leaders in higher education, business, and government sectors who are looking to develop AI literacy and integrate AI into their strategic vision. It is suitable for non-technical audiences who want to gain a deeper understanding of AI and its implications for leadership.

Expected Outcomes:
By the end of the workshop, participants will:
• Have a foundational understanding of how AI works and its potential to drive innovation.
• Be equipped with practical strategies for combining AI-driven technology innovation with business strategy.
• Leave with tools and frameworks to lead AI initiatives within their organizations.

Organizers:

• Alfred Essa, Author of Practical AI for Business Leaders, Product Managers, and Entrepreneurs, Artificial Intelligence: Shaping the Future of Innovation (upcoming 2025), co-editor of DeGruyter Brill book series AI Essentials for Leaders.

• Teresa Martin-Retortillo, Executive Chair of IE Exponential Learning, IE University, Author of Rethinking Business Strategy in the Era of AI (upcoming) and co-editor of AI Essentials for Leaders.

This full day event is for those students selected to the LAK25 Doctoral Consortium. 

Interactive and content-generating dashboards are incrementally becoming the norm in education. However, many dashboard pilots do not survive the vigors of fuller deployment. This hands-on one-day workshop provides discussion on best practices for strengthening the Learning Analytics (LA) feedback cycle within interactive LLM dashboards. LA provides a safety net for the intervention triggered by the generative AI. We frame the conversation in terms of a Dutch experimental infrastructure. We review the already established work of the participants and in groups through a mockup session paper or based on live infrastructure. We define requirements, design practices, and conceptual processes that aid in strengthening the relationship between Generative AI as an intervention and the entire Learning Analytics feedback cycle. The workshop is based on the knowledge gathered from Npuls, a Dutch National digital transformation effort aimed at all adult education levels. Specifically, the team Learning Analytics Best and Worst Practices disseminates evolving practices. The audience provides feedback on currently available dashboards through a review process. Later, documentation of essential requirements is ready for further dissemination and improvement. The workshop thus stimulates and documents the evolution of best practices around the dance between LA and generative AI within an educational dashboard setting.

Organizers:

Alan Berg, University of Amsterdam

Anouschka Leeuwen, Utrecht University

Manuel Valle Torre, TU Delft

Priyanka Pereira, University of Twente

Website:

https://sites.google.com/view/la-genai/

Generative Artificial Intelligence applications powered by large language models (LLMs) have significantly influenced education and, in particular, reimagined writing technologies. While LLMs offer huge potential to provide automated writing support to learners, it is also important to identify challenges they bring to learning, assessment, and critical interaction with AI. This workshop aims to shape possibilities for writing analytics to promote and assess learning-to-write and writing-to-learn that are appropriate for the generative AI era. In this seventh workshop of the Writing Analytics series, we propose a symposium-style format to identify how the field can unfold in the age of LLMs. In particular, we focus on (case) studies within two topics: (1) using writing analytics to design and evaluate interactive writing support systems and (2) using writing analytics to evaluate human-AI interactions and provide timely insights for students/educators. In addition, this workshop will serve as a community-building event to invigorate the SOLAR writing analytics community.

Organizers:

• Rianne Conijn, Eindhoven University of Technology, the Netherlands
• Antonette Shibani, University of Technology Sydney, Australia
• Laura Allen, University of Minnesota, USA
• Simon Buckingham Shum, University of Technology Sydney, Australia
• Cerstin Mahlow, ZHAW School of Applied Linguistics, Switzerland

Website:

https://wa.utscic.edu.au/lak25-workshop-on-writing-analytics-in-the-age-of-large-language-models/

**Is accepting some form of submission (papers, abstracts, problems to solve, etc.) - see workshop website for details.

Organizers:

• Blaženka Divjak, University of Zagreb, Faculty of Organization and Informatics, Croatia
• Darko Grabar, University of Zagreb, Faculty of Organization and Informatics, Croatia
• Barbi Svetec,University of Zagreb, Faculty of Organization and Informatics, Croatia
• Petra Vondra, University of Zagreb, Faculty of Organization and Informatics, Croatia
• Dragan Gašević, Monash University, Faculty of Information Technology, Australia
• Mladen Raković, Monash University, Faculty of Information Technology, Australia
• Bart Rienties, The Open University, Institute of Educational Technology, The United Kingdom

Website:

https://learning-design.eu/en/lak-2025

Actionable learning analytics have shown promise in improving educational outcomes, particularly in higher education, but their application to the K-12 context has been somewhat inconsistent. While some educators and researchers see benefits, others are concerned about data misuse and privacy issues. The growing volume of student data available in school systems presents both opportunities and challenges, with concerns about ethics, consent, and potential harm balancing against the possibilities of individualized educational support and tailored learning experiences. This half-day collaborative workshop will engage researchers and educators with an interest in expanding the use of actionable learning analytics in the K-12 context in structured discussions. Through the use of roundtable and rapid Delphi protocols, the workshop participants will identify three research themes for prioritization and the advancement of the field.

Organizers:

John Kennedy, University of South Australia

Website:

https://efa.unisa.edu.au/K12LA/

Data Storytelling (DS) in education has provided tools and methods to support data experts in making stories more accessible to non-data experts (i.e., learners, educators, and professional staff) while also allowing data-savvy stakeholders (i.e., researchers) to participate in the creation process using human-centered approaches. 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.

Organizers:

• Gloria Milena Fernandez-Nieto, Monash University, Australia
• Vanessa Echeverria, Monash University, Australia
• Namrata Srivastava, Vanderbilt University, United States
• Stanislav Pozdniakov, The University of Queensland, Australia
• Yi-shan Tsai, Monash University, Australia
• Mikaela Milesi, Monash University, Australia
• Adriano Pinargote, Escuela Superior Politecnica del Litoral, Ecuador
• Maurice Boothe Jr., New York University, United States
• Roberto Martinez-Maldonado, Monash University, Australia

Website:

https://datastorytelling-education.github.io/