AI 中文总结
本研究通过对教师专业学习研讨班的4件AI学习应用设计制品开展分析，提出五问题设计协议，助力教师将科学学习需求转化为可问责的人机协作安排。
AI 中文摘要
科学教育工作者越来越多地遇到能生成内容的AI工具，但学科教学依赖于引出学习者的模型、诊断错误概念、解释证据并保留专业判断。本研究探讨科学教师如何将这类认知工作转化为智能学习应用的规格要求，属于会议主题“创新教学法，启迪思维：变革科学学习”及“教师专业学习”分支，将应用协同设计视为一种教学推理形式开展研究。我们对教师专业学习研讨班产出的4件匿名制品进行了限定性定性跨案例分析，这些制品分别是实验设计诊断工具、动能粒子理论对话指南、化学先验知识检查工具、物理应用/支架工具。每件制品都针对学科问题、学习者互动、呈现的证据、教师权威及保障措施进行编码。四件制品均将科学学习问题与互动及可教学解释的证据（包括错误概念与知识缺口、正在形成的解释、班级水平的准备模式或探究表现）相关联；但仅有两件明确提及教师控制或评价，仅有两件明确标注保障措施。因此，这些提案将AI定位为答案生成器之外的引出者、支架及证据返回机制，同时决策权限与保障措施的规定并不均衡。我们认为教师专业学习应将AI应用构想视为认知规格工作，一套包含问题、学习者互动、证据、教师权威、保障措施的五问题设计协议，可帮助教师在构建或采用工具前将科学学习需求转化为可问责的人机协作安排。
英文摘要
Science educators increasingly encounter AI tools that generate content, yet disciplinary teaching depends on eliciting learners' models, diagnosing misconceptions, interpreting evidence, and preserving professional judgment. This study asks how science teachers translate such epistemic work into specifications for agentic learning applications. It contributes to the conference theme, "Innovating Pedagogies, Inspiring Minds: Transforming Science Learning," and the Teachers' Professional Learning strand by examining app co-design as a form of pedagogical reasoning. We conducted a bounded qualitative cross-case analysis of four de-identified artifacts produced in a teacher professional-learning workshop: an experimental-design diagnostic, a Kinetic Particle Theory dialogue guide, a chemistry prior-knowledge checker, and a physics application/scaffolding tool. Each artifact was coded for the disciplinary problem, learner interaction, evidence made visible, teacher authority, and safeguard. All four connected a science-learning problem to an interaction and pedagogically interpretable evidence: misconceptions and gaps, explanations-in-progress, class-level readiness patterns, or investigation performance. However, only two made teacher control or evaluation explicit, and only two named a safeguard. The proposals therefore positioned AI less as an answer generator than as an elicitor, scaffold, and evidence-return mechanism, while leaving decision rights and protections unevenly specified. We argue that teacher professional learning should treat AI app ideation as epistemic specification work. A five-question design protocol--problem, learner interaction, evidence, teacher authority, and safeguard--can help teachers transform science-learning needs into accountable human-AI arrangements before building or adopting a tool.
Comments10 pages, 1 figure, 2 tables. Working paper. A related abstract with the same title was accepted for a 25-minute oral presentation followed by 10 minutes of Q&A at the 8th Singapore International Science Teachers' Conference (SISTC 2026), Science Centre Singapore, 24-26 November 2026. The full manuscript has not been peer reviewed or accepted for conference proceedings
