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Learning at Scale conference artwork

Learning at Scale (L@S) 2024

July 15, 2024 - July 19, 2024

Atlanta, Georgia, USA

Learning at Scale conference artwork

Returning to Atlanta to celebrate the 10th anniversary of the ACM Learning @ Scale Conference, the 2024 ACM Learning @ Scale Conference will take place at the Global Learning Center at Georgia Tech between July 15 and July 19. More specific details and information about co-located events are forthcoming. The conference is jointly presented by the Georgia Tech College of Computing and the Georgia Tech Lifetime Learning.

Learning @ Scale 2024 will be co-located with Educational Data Mining 2024.

Learning@Scale 2024 Theme: Scaling Learning in the Age of AI

Rapid advances in AI have created new opportunities but also challenges for the Learning@Scale community. The advances in generative AI show potential to enhance pedagogical practices and the efficacy of learning at scale. This has led to an unprecedented level of interest in employing generative AI for scaling tutoring and feedback. The prevalence of such tools calls for new practices and understanding on how AI-based methods should be designed and developed to enhance the experiences and outcomes of teachers and learners.

At this year’s Learning@Scale conference, we focus on scaling learning in the age of AI. We enthusiastically welcome submissions that discuss learning-at-scale research with the aim of improving the experiences and outcomes of learners, teachers, and educators. This year we are particularly interested in, but not limited to, contributions that explore the technological, social, cultural aspects of the responsible use of AI in scaling learning. We specifically encourage works that address the use of generative AI in both classrooms and informal learning settings. This encompasses contributions related to systems and designs to facilitate learning at scale, qualitative and quantitative empirical studies seeking to understand stakeholders’ experiences with AI in scaling learning, empirical studies and interventions that address equity, trust, algorithmic transparency and explainability when using AI in education, methodologies for evaluating and quantifying the impact of AI-based interventions, and synthesis papers discussing the potentials and risks involved in utilizing AI to scale learning.

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