发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出“设计中的教学治理”多维框架，通过六个维度配置AI教学工具的角色、权威与问责，并基于案例比较提炼可复用设计问题，以指导负责任AI整合。
AI 中文摘要
随着生成式AI渗透到计算教学领域，一个新兴的设计挑战是如何配置每个工具的教学角色、权威及其所执行教学工作的问责机制。我们主张“设计中的教学治理”：治理应编码在教学工具的交互相式模型、约束条件和工作流程中。我们引入了一个多维框架，通过以下六个维度来刻画AI教学工具：（1）教学基础，（2）AI教学权威，（3）人类问责与控制，（4）学习者能动性与认知参与，（5）情境特定性与边界设定，以及（6）评估可见性与修订。这些维度产生了治理概况，帮助教育者将工具与特定目的和教育风险对齐。我们通过对计算和一年级工程领域一系列AI教学工具的比较分析来阐述这一立场：基于量规的助教（GTA）模拟、反思导向的代码伴侣、员工审核的论坛回复系统、助教监督的图表生成器，以及课程特定的代码风格教练。这些案例表明，常见的教学功能需要不同的量规组合、学习理论承诺、审批关卡、监督和课程特定约束。我们进一步将框架应用于选定的已发表工具，以展示其超越单一机构工具组合的用途。从这些案例中，我们识别出可复用的设计问题，用于将治理与教学风险、人类能力和预期学习过程对齐。这一立场将负责任的AI整合重新定义为课程和交互设计的挑战，并为工具构建者、教师和研究人员提供了设计、比较和评估AI中介学习环境的通用词汇。
英文摘要
As generative AI permeates computing instruction, the emergent design challenge is to configure each tool's pedagogical role, authority, and accountability for the instructional work it performs. We argue for instructional governance by design: governance should be encoded in a teaching tool's interaction model, constraints, and workflow. We introduce a multidimensional framework that characterizes AI teaching tools through (1) pedagogical grounding, (2) AI instructional authority, (3) human accountability and control, (4) learner agency and cognitive engagement, (5) context specificity and boundary setting, and (6) evaluation visibility and revision. These dimensions yield governance profiles that help educators align tools with specific purposes and educational stakes. We develop the position through a comparative analysis of a portfolio of AI teaching tools across computing and first-year engineering: rubric-anchored GTA simulations, reflection-oriented code companions, staff-reviewed forum-response systems, TA-supervised diagram generators, and course-specific code-style coaches. These cases show how common instructional functions call for different combinations of rubrics, learning-theory commitments, approval gates, supervision, and course-specific constraints. We further apply the framework to selected published tools to demonstrate its use beyond a single institutional portfolio. From these cases, we identify reusable design questions for aligning governance with instructional stakes, human capacity, and intended learning processes. This position reframes responsible AI integration as a curricular and interaction-design challenge and offers a common vocabulary for tool builders, instructors, and researchers to design, compare, and evaluate AI-mediated learning environments.
Comments7 pages, 3 tables
