Actionable companion · Motivation & engagement

Learning with agency: Children’s susceptibility to content generated by artificial intelligence

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Children · stage unspecified. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Advancements in artificial intelligence (AI) tools can make it difficult to discern what is “real”. Though children are interacting with AI-generated materials through entertainment and learning applications, no published work directly considers children’s ability to distinguish human-created from AI-generated information. In the current study, 37 children (6–10 years old) heard a narrative about a “SmartBot” (representing AI) that could write text, invent photos, and create art. They were then asked to discern which in a series of stimuli including short texts and images of objects, art, and faces, had come from the SmartBot vs. a human teacher and answer questions regarding their beliefs about AI. Children also completed measures of abstract reasoning and receptive vocabulary, and parents completed a home technology use survey. A separate sample of 49 adults (18–34 years) served as a comparison group for accuracy scores on the set of stimuli. On average, children performed at or below chance in discriminating human-created from AI-generated content across modalities, and their overall accuracy was significantly below that of adults. Children’s individual discernment varied and was significantly and negatively associated with parent reports of children’s at-home technology use. Children had varied beliefs about AI agency and experience, but broadly understood that an AI agent (the SmartBot) is not alive (95%). Our findings suggest that spending more time on digital devices is associated with susceptibility to artificial content and risk for misattributing AI content as human, highlighting the importance of early AI literacy to support children’s engagement with AI technologies. Keywords: Generative AI, Children, Human-computer interaction, Developmental psychology

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Children · stage unspecified. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Participant ages · source passage

In the current study, 37 children (6–10 years old) heard a narrative about a “SmartBot” (representing AI) that could write text, invent photos, and create art.

Study methods · source passage

Child participants (n = 37) ages 6 to 10 years old (meanage = 7.87, SDage = 1.11) completed the study remotely via Zoom. This age range was selected based on prior developmental work on child-technology interactions with similarly aged children that found meaningful trends in perceptions of AI (Oh et al., 2025) and machines broadly (Flanagan et al., 2023). Our sample size was determined by practical constraints of recruitment and data collection capacity during the study period, combined with power analyses suggesting this sample would provide adequate power (β = 0.80) to detect medium-to-large correlations. We acknowledge that the sample size is modest, particularly for detecting small effects.

Limitations and uncertainty · source passage

While our study provides novel insights into how children perceive content generated by AI, our findings are limited in part by a small sample size. Further, our participant pool was recruited solely from a small city in the eastern United States, with high average annual income and caregiver education levels. While such results are still informative, they may not generalize to children in different geographic or socioeconomic contexts with different exposure to, experience with, or beliefs about AI.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Establish the participants’ ages, developmental needs and educational setting from the full paper before selecting an intervention. Use adult-mediated activity and preserve hands-on and peer learning. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Evaluate an age-appropriate independent task after the interaction. Do not infer a preschool or primary-school effect when the paper does not establish that population. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Allison Langer; Steven Martinez; Peter J Marshall; Jason Chein. Children’s susceptibility to content generated by artificial intelligence. Technology in society (2026-03-18). https://doi.org/10.1016/j.techsoc.2026.103303

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.