Module 1 will introduce several distinctions based on artificial intelligence systems as fields of generative AI technologies and how they benefit online learning and education for students.

By the end of this module, the participants will be able to:

• Learning Objective 1- LO1: define key terms, artificial intelligence, generative AI technologies, including natural language processing (NLP) and machine learning (ML) (CO1).
• Learning Objective 2- LO2: identify the benefits of applying generative AI to online learning and education of students (CO1).
• Learning Objective 3 – LO3: identify best practices for integrating generative AI technologies into teaching and learning environments (CO1).

Module 2 will identify several generative AI tools based on natural language processing (NLP) and machine learning (ML) that online educators can leverage to enhance their teaching and learning experiences.

By the end of this module, the participants will be able to:

• Learning Objective 4 -LO4: list various AI tools that can enhance online instructional design and identify their specific features (CO2).
• Learning Objective 5 -LO5: create engaging online activities that leverage AI tools to foster student interaction and collaboration (CO2).
• Learning Objective 6 -LO6: incorporate AI-driven features, such as chatbots or interactive simulations, to enhance student engagement (CO2).
• Learning Objective 7 -LO7: implement AI tools that adapt content and learning pathways based on individual student needs and performance (CO2).
• Learning Objective 8 -LO8: utilize AI tools to generate personalized feedback for students based on their performance and engagement (CO2).
• Learning Objective 9 -LO9: assess the effect of AI tool integration on student learning outcomes and engagement (CO2).
• Learning Objective 10 -LO10: refine their instructional design strategies based on feedback and data collected from AI tools (CO2).

Generative AI is revolutionizing online education by enabling more personalized, scalable, and interactive learning experiences. Module 3 will explore some practical applications in benefiting personalized, scalable, and interactive experiences in online education.

By the end of this module, the participants will be able to:

• Learning Objective 11 -LO11: list various practical applications of AI in education, including automated grading systems, virtual assistants, and adaptive learning platforms (CO3).
• Learning Objective 12 -LO12: describe the functionalities and purposes of each AI application (CO3).
• Learning Objective 13 -LO13: identify the role of virtual assistants in enhancing teacher-student communication and support (CO3).
• Learning Objective 14 -LO14: analyze case studies or examples of successful AI implementation in educational settings and their outcomes (CO3).
• Learning Objective 15 -LO15: use data and feedback to evaluate the effectiveness of AI applications in improving student engagement and learning outcomes (CO3).

Module 4 focuses on how learners can develop a well-rounded set of pedagogical, technical, and ethical skills, ensuring they are equipped to use generative A.I. effectively in the creation of engaging, interactive, and ethically sound educational content.

By the end of this module, the participants will be able to:

• Learning Objective 16 - LO16: acquire proficiency in selecting and using generative A.I. tools (e.g., text generators, image creators, interactive simulation platforms) to enhance content creation and learning experiences.
• Learning Objective 17 - LO17: develop interactive assessments, quizzes, and activities that utilize generative A.I. to provide real-time feedback and personalized learning paths for students.
• Learning Objective 18 - LO18: build and implement A.I.-powered multimedia content (such as dynamic videos, chatbots, or adaptive learning modules) to promote student engagement and deeper learning.