AI-powered coding assistants can support students in programming courses by providing on-demand explanations and debugging help. However, existing research often focuses on individual tools, leaving a gap in evidence-based design recommendations that reflect both educator and student perspectives in education settings. To ground the design of learning-oriented AI coding assistants for both sides' needs, we conducted parallel surveys of educators (N=50) and students (N=90) to compare preferences about (i) how students should request help, (ii) how AI should respond, and (iii) who should control. Our results show that educators generally favored indirect scaffolding that preserves students' reasoning, whereas students were more likely to prefer direct, actionable help. Educators further highlighted the need for course-aligned constraints and instructor-facing oversight, while students emphasized timely support and clarity when stuck. Based on these findings, we discuss the interaction-focused design space and derive design implications for learning-oriented AI coding assistants, highlighting scaffolding and control mechanisms that balance students' agency with instructional constraints.

翻译：具备AI能力的编程助手可通过提供按需解释与调试支持来协助学生完成编程课程。然而，现有研究多聚焦于单一工具，缺乏基于证据且能同时反映教育场景中教师与学生观点的设计建议。为兼顾双方需求，构建面向学习的AI编程助手设计基础，我们并行开展了针对教师（N=50）与学生（N=90）的问卷调查，比较双方在以下方面的偏好：（i）学生应如何请求帮助，（ii）AI应如何回应，（iii）应由谁控制。结果表明，教师普遍倾向于能保持学生推理过程的间接支架式引导，而学生则更偏好直接可执行的帮助。教师进一步强调了需遵循课程约束和面向教师的监控机制，学生则更重视遇到困难时的及时支持与清晰度。基于这些发现，我们探讨了以交互为中心的设计空间，并推导出面向学习的AI编程助手的设计启示，重点阐述了在平衡学生自主性与教学约束之间的支架与控制机制。