Uncovering patterns and trends in vast, ever-increasing quantities of data has been enabled by different machine learning methods and techniques used in Artificial Intelligence (AI) systems. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI. AI literacy is trying to close the gap between research and practical knowledge transfer of AI-related skills. Teachers' AI-related technological, pedagogical and content knowledge (TPACK) are important factors for AI literacy. However, as teachers' perspectives, beliefs and views impact both the interpretation and operationalisation of curriculum. this study explores teachers' and teacher educators' understanding and preconceptions of AI to inform teacher education and professional development. To gain a comprehensive understanding of teachers’ conceptualisations regarding AI an anonymous questionnaire together with focus group discussions were employed. The qualitative content analysis underpinned by the theoretical framework Intelligent TPACK reveals that teachers' AI-related content knowledge is generally gained through incidental learning and often results in pre- and misconceptions of AI. Our analysis also revealed several potential challenges for teachers in achieving core constructs of Intelligent TPACK, examples of such challenges are vague and unclear guidelines in both policy and curriculum, a lack of understanding of AI and its limitations, as well as emotional responses related to participants' preconceptions. These insights are important to consider in designing teacher education and professional development related to AI literacy.

人工智能 (AI) 系统中使用的不同机器学习方法和技术可以揭示海量且不断增加的数据中的模式和趋势。该领域的发展渗透到我们生活的许多方面并影响我们的选择，不断推动并日益影响我们个人和社会。输入数据或算法设计的偏差等风险和意外影响最近引发了关于如何在 K-12 教育中宣传和教授人工智能的讨论。由于人工智能不仅对 K-12 学生而且对教师来说都是一个新主题，因此需要新的技能来实现与人工智能的关键参与。人工智能素养正试图缩小人工智能相关技能的研究和实践知识转移之间的差距。教师人工智能相关技术、教学和内容知识（TPACK）是人工智能素养的重要因素。然而，教师的观点、信念和观点会影响课程的解释和实施。这项研究探讨了教师和教师教育工作者对人工智能的理解和偏见，为教师教育和专业发展提供信息。为了全面了解教师对人工智能的概念，我们采用了匿名问卷和焦点小组讨论的方式。以Intelligent TPACK理论框架为基础的定性内容分析表明，教师的人工智能相关内容知识一般是通过偶然学习获得的，往往会导致对人工智能的先入为主和误解。我们的分析还揭示了教师在实现智能 TPACK 核心结构方面面临的一些潜在挑战，这些挑战的例子包括政策和课程中模糊不清的指导方针、对人工智能及其局限性缺乏了解，以及与参与者相关的情绪反应'先入之见。在设计与人工智能素养相关的教师教育和专业发展时，需要考虑这些见解。