AI in education is commonly delivered through web-based systems such as online forms and institutional platforms. However, these approaches can exclude teachers in low-resource contexts, where everyday mobile platforms like WhatsApp serve as primary digital infrastructure. To address this gap, we present a field pilot in Cameroon that deploys a WhatsApp-based chatbot with LLM-supported content for teacher professional development (TPD), compared with an online form baseline. The system was evaluated through a mixed-methods study with 47 primary school teachers, integrating quantitative measures with qualitative insights from interviews and participant feedback. Results show that the chatbot was rated higher in perceived usability and overall experience, while learnability remained comparable. These improvements were driven by platform familiarity, low interaction overhead, and the modular structure of LLM-supported content, but were constrained by connectivity limitations, prepaid data costs, and multilingual needs (English/French). Building on these findings, we outline design directions for multilingual, culturally grounded interaction and for supporting prompting and reflection in AI use. More broadly, this work points to Thoughtful AI that supports reflection, relevance, and sustained professional growth.

翻译：人工智能在教育中的应用通常通过网络系统实现，如在线表单和机构平台。然而，这些方法可能将资源匮乏环境中的教师排除在外——在这些环境中，WhatsApp等日常移动平台构成了主要数字基础设施。为弥合这一鸿沟，我们在喀麦隆开展了一项实地试点，部署了基于WhatsApp的聊天机器人，并采用大语言模型（LLM）支持的内容进行教师专业发展（TPD），与在线表单基线进行对比。通过对47名小学教师采用混合方法研究，整合定量测量与访谈及参与者反馈的定性见解，系统评估结果显示：聊天机器人在感知可用性与整体体验方面评分更高，而学习便捷性保持相当。这些改进源于平台熟悉度、低交互开销及LLM支持内容的模块化结构，但也受限于连接可靠性、预付数据成本及多语言需求（英语/法语）。基于这些发现，我们提出面向多语言、文化适应性交互的设计方向，以及支持AI使用中提示工程与反思的设计路径。更广泛而言，本研究指向支持反思、相关性与持续专业成长的具有思考能力的AI。