儿童如何设计与推理可信 AI 聊天机器人
How Children Design and Reason about Trustworthy AI Chatbots
研究团队开发了可调节置信度、透明度、正式度、果断性等信任相关特质的聊天机器人搭建环境，115 名 8-18 岁学习者共制作了 119 个聊天机器人。10-13 岁学生设定的置信度显著高于 14-18 岁学生，部分人故意让机器人给出错误答案却仍称其可信，认为机器人只是做了它被造出来要做的事。年幼者将信任等同于目的达成，年长者则将其与透明、校准的设计联系起来，学术类机器人被调得比兴趣类更透明正式。
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
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:2609.25244v4 [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)
[v4]
Wed, 7 Oct 2026 01:59:32 UTC (11,038 KB)
来源：arXiv cs.AI · arxiv.org