ICLS 2026
We are excited to share the slides from our presentation at International Conference of the Learning Sciences 2026, where we presented our paper:
“Incorporating Young Children’s Values Through Laddering Methodology: Examples from Early Literacy and AI.”
This work explores an important methodological question in the Learning Sciences: How can we better understand young children’s perspectives during the earliest stages of designing emerging learning technologies?
While approaches such as Design-Based Research and co-design are widely used to refine learning technologies, there remains a challenge in understanding learners’ perspectives before initial design conjectures are even formed — particularly when working with young children who may struggle to articulate their preferences and reasoning directly.
In this project, we adapted laddering interviews, a user experience research method built around the Attribute–Consequence–Value (ACV) framework, to uncover how young children make sense of AI-supported literacy technologies.
Across two studies, we explored children’s perspectives on:
- AI-generated avatars used in reading applications
- AI-supported reading tools, including automatic speech recognition and interactive reading support
Our findings suggest that laddering interviews can help researchers move beyond surface-level preferences and better understand how children connect design features to deeper ideas such as identity, belonging, autonomy, competence, cognitive load, and emotional comfort.
More broadly, this work highlights the importance of developing methods that meaningfully involve young learners in the earliest stages of educational technology design, especially as AI becomes increasingly integrated into children’s learning environments.
We are sharing our presentation slides below and welcome conversations with researchers interested in Learning Sciences, AI in Education, child-centered design, early literacy, and research methodology.
Xintian Tu-Shea 