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Ying Xu | AI and the Developing Child: Myths, Evidence, and Open Questions

Ying Xu’s Stanford seminar examined how preschool‑age children engage with voice‑based artificial intelligence, arguing that this demographic warrants a child‑centered AI approach distinct from adult usage. She highlighted that young learners interact with AI primarily for companionship, play, and informal learning, rather than goal‑oriented tasks, making the developmental window both vulnerable and ripe for impact. Xu presented three empirical studies, the most extensive involving interactive PBS Kids episodes. In a controlled experiment, children experienced three conditions: a true AI conversational interface, a pseudo‑interactive script, and a traditional broadcast. Regression‑adjusted results showed the AI‑conversation group achieved higher post‑test recall and transfer scores, confirming that genuine dialogue—not merely questioning—enhances learning. Response rates to AI prompts rose over time, while non‑interactive groups disengaged. A vivid illustration came from a early video of a child asking Google about princesses, underscoring how children attribute agency to voice assistants. Further, a parasocial relationship scale revealed no significant differences in perceived friendship or realism across conditions, suggesting that AI dialogue does not intensify one‑sided emotional bonds beyond existing media attachments. The findings imply that safely designed, controllable AI—using dialogue trees rather than open‑ended generation—can be scaled into homes, offering an additional educational media option. However, parental attitudes remain polarized, indicating the need for clear guidelines and evidence‑based messaging as AI becomes embedded in early childhood learning environments.