As artificial intelligence (AI) and digital tools grow more prevalent in K-12 classrooms, examining teacher preparation is crucial for effective and ethical use. This literature analysis combines findings from various academic sources to analyze current levels of teacher readiness, look at professional development (PD) trends, and identify persisting gaps. The study uses two major conceptual frameworks - Technological Pedagogical Content Knowledge (TPACK) and the SAMR (Substitution, Augmentation, Modification, and Redefinition) model - to assess both the knowledge foundation teachers need and the depth of instructional transformation enabled by technology. While most educators understand AI's potential to improve learning, their readiness is influenced by a variety of characteristics such as confidence, attitudes about AI, ethical awareness, and institutional support. The analysis concludes that, while PD programs are beginning to address AI-specific demands, they are frequently fragmented, inequitable, and too focused on short-term technical training. Critical issues such as algorithmic bias, data privacy, and the emotional and ethical consequences of AI use are usually overlooked. The analysis indicates that effective AI integration preparation must go beyond technical proficiency and include long-term, context-sensitive, and equity-driven professional development techniques. It also advocates for further research into long-term educational impact, differential preparation across contexts, and the role of ethical reasoning in AI-supported education. This study adds to the expanding discussion around AI in education by explaining what teacher preparedness means and offering concrete approaches for policy and practice.
Shardey et al. (2025) studied this question.