Teachers often lack awareness of the diverse Generative Artificial Intelligence (GenAI) tools available for instructional purposes. Existing technology integration frameworks provide valuable guidance on incorporating digital tools into education but do not explicitly address the use of GenAI-based tools. Additionally, these frameworks tend to focus on student learning activities rather than the broader instructional process. While discussions on AI in education primarily highlight how students interact with AI-driven learning platforms, little attention is given to how teachers can effectively integrate GenAI into lesson planning, material development, and assessment design. Despite the increasing availability of AI-powered tools, there is a scarcity of concrete, research-based examples that illustrate how teachers can ethically and pedagogically embed these technologies into their instructional workflows.

To address this gap, we propose a structured approach to GenAI integration in teaching, drawing from the Intelligent Technological Pedagogical Content Knowledge (Intelligent-TPACK) framework developed by the Authors (2023). This model extends the traditional TPACK framework by incorporating ethical considerations for AI use in education. Our approach categorizes instructional activities into three key types:
(i) planning activities, including AI-assisted lesson design, instructional material creation, and curriculum adaptation;
(ii) implementation activities, such as AI-supported tutoring, adaptive questioning, real-time feedback, and personalized learning pathways; and
(iii) assessment activities, which involve AI-enhanced formative and summative assessments, automated grading, and data-driven progress monitoring.

Ethical considerations are embedded within each category to ensure responsible AI use, focusing on aspects such as content reliability, academic integrity, student agency, transparency, and bias mitigation.

By providing concrete examples of how GenAI tools can support teachers throughout the instructional cycle—planning, implementation, and assessment, this work offers practical guidance for educators seeking to enhance their teaching practices with AI. These examples are built upon our systematic literature review of empirical (n = 27) and conceptual studies (n = 14) indexed in Web of Science (WoS) database, demonstrating GenAI applications in real-world educational settings. While our primary focus is on Science, Technology, Engineering, and Mathematics (STEM) education, the proposed teaching activity types are designed to be adaptable and transferable to social sciences and other subject areas.

Beyond offering specific examples, this study highlights the essential professional knowledge required for effective AI integration, emphasizing the need for teachers to develop technological, pedagogical, and ethical competencies. By mapping specific knowledge dimensions from the Intelligent-TPACK framework to each activity type, we provide a structured guide to help educators navigate AI adoption in an informed and responsible manner.
This study contributes to the growing discourse on AI in education by offering a systematic, ethical, and research-informed perspective on integrating GenAI into teaching. By equipping educators with actionable strategies and a clear framework for AI integration, we aim to support the pedagogically meaningful and ethically responsible use of AI in education.

Keywords: Generative Artificial Intelligence (GenAI), Intelligent-TPACK, Pedagogical AI Integration, Ethical AI Use in Education, AI-Enhanced Teaching and Assessment.