LAK26 Wellness | Half-Day Event

Education for the Whole Human: Embedding Wellness into Learning Analytics

Exploring AI-Supported Holistic Learning Experiences

@LAK26 | Bergen, Norway

About the Workshop

This half-day workshop brings together interdisciplinary experts to examine how advances in AI can support wellness as a foundational part of the learning experience. Given the impact of wellness, wellbeing, and mental health on learning, the workshop will explore the ways in which learning analytics methods are poised to support a more holistic perspective that does not compartmentalize learning from the rest of our lives.

We bring together experts with theoretical and methodological expertise from across the LA community (and beyond) to better understand how we can support learners' wellbeing, while giving equal attention to the practical and safety implications that are critical to the success of these approaches.

Call for Papers

We invite submissions of approximately 1000 words that detail works in progress, with or without data. These can include theoretical frameworks, methodological approaches, preliminary findings, or conceptual papers that explore the intersection of learning analytics, AI, and wellness. Submissions may address topics such as ethical considerations, privacy concerns, student wellbeing measurement, holistic learning approaches, or innovative applications of AI in supporting learner wellness.

Submission Deadline: January 20th, 2026

Workshop Organizers

Caitlin Mills, Tanya Gamby, Laura Allen, Srecko Joksimovic, Bec Marrone, Cati Poulos, Walter Reilly, David Kil, George Siemens

Interested in Participating?

We welcome submissions from researchers, practitioners, and students interested in the intersection of learning analytics and wellness.

Submit Your Work

Share your research and ideas with the community

Submission Guidelines

Format: IEEE format, ~1000 words

File Type: PDF only

File Size: Maximum 10MB

Deadline: January 20th, 2026

Click to upload or drag and drop

PDF only (Max 10MB)

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