面向统计学与数据科学领域的生成式AI负责任且有效使用的教学框架
A Framework for Teaching the Responsible and Effective Use of Generative AI in Statistics and Data Science
AI总结：
本文提出了统计学与数据科学领域生成式AI负责任且有效使用的教学框架，围绕四大领域构建，经多场景实践后明确了教育者的相关设计考量。
摘要：
ChatGPT等工具公开发布后，生成式人工智能（AI）的快速兴起已开始重塑高等教育中学生参与编码、统计分析及书面交流的方式。对话式AI系统的日益普及，既为学习创造了机会，也带来了可靠性、透明度、隐私以及恰当学术与专业实践等方面的挑战，凸显了开展生成式AI负责任使用结构化教育的必要性。本文提出了一个面向统计学与数据科学领域的生成式AI负责任且有效使用的教学框架，该框架围绕理解、开发、报告、安全四个领域组织，涵盖概念基础、分析工作流程内的实际交互、文档与透明度，以及可靠性、偏差、隐私和伦理等问题。其设计借鉴了AI素养领域的新兴研究成果，以及统计学与数据科学教育的既定原则，并通过研讨会、会议活动及参与者反馈进一步完善。该框架已在多种场景中实施，包括大规模开放在线课程、短期培训课程，以及本科和研究生的技能型教学。我们利用这些经验为教育工作者确定了更广泛的设计考量因素，包括真实分析任务的价值、对生成内容的批判性评估、负责任实践的综合考量，以及随着生成式AI技术不断发展所需的灵活性。
英文摘要：
The rapid rise of generative artificial intelligence (AI) following the public release of tools such as ChatGPT has begun to reshape how students engage with coding, statistical analysis, and written communication in higher education. Growing use of conversational AI systems creates opportunities for learning alongside challenges around reliability, transparency, privacy, and appropriate academic and professional practice, highlighting the need for structured education in their responsible use. This paper presents a framework for teaching the responsible and effective use of generative AI in statistics and data science. The framework is organised around four areas: Understanding, Development, Reporting, and Safety, covering conceptual foundations, practical interaction within analytical workflows, documentation and transparency, and issues including reliability, bias, privacy, and ethics. Its design draws on emerging work in AI literacy and established principles from statistics and data science education, and was further refined through workshop and conference activities and participant feedback. The framework has been implemented across multiple settings, including a massive open online course, short training sessions, and undergraduate and postgraduate skills-based teaching. We use these experiences to identify broader design considerations for educators, including the value of authentic analytical tasks, critical evaluation of generated outputs, integrated consideration of responsible practice, and flexibility as generative AI technologies continue to evolve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
