Generative AI and Learning Analytics (GenAI-LA): 

CALL FOR WORKSHOP PAPERS

We will have a call for papers to welcome researchers and practitioners to communicate their contributions. Attendees can optionally submit a Workshop Paper (4-10 pages, using this template) or Extended Abstract (2-4 pages, using this template).  The organisers welcome two types of contributions:

• Empirical work to showcase work in progress, experiments, and etc. that illustrates impacts of GenAI technologies on human learning.

• Discussion piece to provoke the audience to think about key issues and problems of GenAI usage on human learning.

Accepted workshop papers will be published in the CEUR Workshop Proceedings (CEUR-WS.org). Please note that accepted workshop papers will not be published in the LAK proceedings this year.

Suggested topics

The following can guide the selection of topics for submitted papers: 

• Generative AI's Influence on Self-Regulated Learning Strategies: Empirical studies that evaluate the impacts of generative AI technologies on learners' ability to plan, monitor, and assess their learning processes.

• Evaluating the Effectiveness of Generative AI in Collaborative Learning Settings: Research that assesses how generative AI tools influence collaborative learning experiences, including their impact on communication, teamwork, and collaborative learning outcomes.

• Generative AI and Its Impact on Learners' Cognitive Load and Mental Health: Studies that assess how the use of generative AI in learning processes affects learners' cognitive load, stress levels, and overall mental health.

• Generative AI and Affective Learning Outcomes: Investigations into how integrating generative AI technologies in learning practices impacts affective learning outcomes, such as attitudes, motivation, and values.

• Generative AI's Impact on Creative Thinking and Problem-Solving Skills: Research that investigates how generative AI tools support or damper learners' abilities to think creatively, solve problems, and engage in innovative thinking processes.

• Generative AI and Its Impact on Critical Thinking and Ethical Reasoning: Research examining whether generative AI tools can enhance or diminish learners' critical thinking and ethical reasoning skills.

• Impact of Generative AI on Attention and Memory Processes: Research that assesses how the integration of generative AI in learning activities influences attentional control and memory processes, such as working memory, long-term memory formation, and retrieval strategies.

Workshop Day Schedule

• Opening Keynote (30 minutes)

  • Unpacking Three Paradigms of GenAI Use in Education through Case Studies - Prof. Mutlu Cukurova (University College London)

• Workshop Paper Presentations (90 minutes)

  • Human-Artificial Intelligence Interaction’s Effects on Chinese Secondary Students’ Metacognitive Skills in English Learning - Xiaohua Zhou

  • Driving Improvements in Quality and Safety of AI-Generated Lesson Resources - Hannah-Beth Clark, Owen Henkel, Laura Benton, Margaux Dowland, Reka Budai, Ibrahim Kaan Keskin, Emma Searle, Matthew Gregory, Mark Hodierne, William Gayne and John Roberts

  • Towards Learning Analytics for Interdisciplinary Learning: Leveraging Knowledge-empowered Fine-Tuned GPT Models - Tianlong Zhong, Gaoxia Zhu, Swee Chiat Low and Siyuan Liu

  • Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment - Conrad Borchers, Danielle R. Thomas, Jionghao Lin, Ralph Abboud and Kenneth R. Koedinger

  • Retrieval-Augmented Chatbots for Scalable Educational Support in Higher Education - Hassan Soliman, Hitesh Kotte, Miloš Kravčík, Norbert Pengel and Nghia Duong-Trung

  • User-centric Evaluation of GenAI Recommendations and Alignment based on Predictive Learning Analytics - Hesham Ahmed, Halil Kayaduman, Sonsoles López-Pernas, Markku Tukiainen and Mohammed Saqr

  • LLM-based Literature Recommender System in Higher Education - A Case Study of Supervising Students' Term Papers - Xia Wang, Nghia Duong-Trung, Rahul R. Bhoyar and Angelin Mary Jose

  • Towards Scalable AI Feedback Systems: Preparing A Turing-Test-Inspired Experiment - Peter A. M. Ruijten-Dodoiu, Manual Oliveira and Esther Ventura-Medina

  • Can Generative Artificial Intelligence Ever Be a True Collaborator? Rethinking the Nature of Collaborative Problem-Solving - Laura Brandl, Constanze Richters, Nicola Kolb and Matthias Stadler

  • Learning Analytics and Generative AI: Mapping Cognitive Engagement in Nursing Education - Mamta Shah

  • Memoire: Harnessing Generative AI to Bridge the Metacognitive Gap in Reflective Writing - Matea Tashkovska, Seyed Parsa Neshaei, Paola Mejia-Domenzain and Tanja Käser

• Collaborative Design Sessions (90 minutes)

  • In this session, participants will work in small, interdisciplinary groups to design empirical studies investigating the impact of Generative AI on specific learning processes or outcomes. By collaboratively developing research proposals, attendees will not only gain deeper insights but also foster connections that can lead to ongoing collaborations after the workshop.