儿童如何设计与推理可信 AI 聊天机器人

Authors:Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Cateté, Tiffany Barnes, Xiaoyi Tian

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Abstract:Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's trust in AI mainly as users evaluating systems built by others, rather than as designers of their own chatbots. We developed a chatbot-building environment with adjustable trust-relevant traits (e.g., confidence, transparency, formality, assertiveness), rules, and persona. We conducted mixed-methods study with 115 learners (ages 8-18) who made 119 chatbots. We examined how children configured their chatbots, reasoned about trustworthiness, and how closely chatbot behavior aligned with their designs. Younger students (age 10-13) set significantly higher confidence than older students (age 14-18), and some deliberately built chatbots that gave wrong answers on purpose, yet still called them trustworthy, arguing that a chatbot does what it was built to do. Younger students equated trust with purpose-fulfillment, while older students linked it to transparent, calibrated design. Students also calibrated academic chatbots to be more transparent and formal than hobby chatbots. We identify seven design dimensions describing what children believe makes a chatbot trustworthy, and discuss implications for AI literacy tools.

Subjects:	Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2609.25244 [cs.HC]
	(or arXiv:2609.25244v3 [cs.HC] for this version)
	https://doi.org/10.48550/arXiv.2609.25244 arXiv-issued DOI via DataCite

Submission history

From: Deniz Ozturk [view email]
[v1] Mon, 21 Sep 2026 18:02:26 UTC (11,046 KB)
[v2] Wed, 23 Sep 2026 16:03:34 UTC (11,046 KB)
[v3] Tue, 6 Oct 2026 16:56:51 UTC (11,046 KB)