Foundations and Applications of Generative AI for Teachers Course

Learning Objectives

• Understand foundational principles of generative AI and how generative AI processes data to generate insights.
• Learn how generative AI can be applied in educational settings for curriculum design, student assessment, and interactive teaching.
• Identify and evaluate AI tools suited for various disciplines, age groups, and educational settings.
• Develop strategies to create personalized and adaptive learning experiences using generative AI.
• Critically analyze the ethical considerations, challenges, and risks of deploying AI in educational systems.
• Explore ways AI can streamline administrative tasks, enabling educators to focus more on teaching and student engagement.

Key Course Features

• Comprehensive Curriculum: Covering everything from the fundamentals of AI to applications in education, ethics, and implementation.
• Expert Instruction: Led by experts in the fields of AI and education, this course will deliver lectures on AI, tools & techniques, applications for improving educational outcomes & personalizing learning experience, ethical considerations, and implementing AI.
• Collaborative Environment: Discuss lecture topics and create presentations alongside like-minded peers in a supportive and dynamic learning environment, fostering creativity and innovation.
• Hands-on Experience: Practice what you learn with hands-on activities related to the various lecture topics.

Who Should Attend?

This course is designed for school teachers and school administrators in primary and secondary schools.

Please note that you do not need to have a background AI to successfully complete this course.

Dr. Marzieh Ahmadzadeh

Dr. Marzieh Ahmadzadeh received her Ph.D. degree from the University of Nottingham, UK in 2006, where she had also obtained her MSc. degree in Information Technology, specialized in Software Engineering in 2002. Her BSc. is in Computer Engineering from Isfahan University. She has worked in academia since 2006 in a number of Computer Science and Engineering departments including Shiraz University of Technology, the University of Toronto and University of Georgia, USA. Prior to 2015, before she decided to dedicate herself to teaching, she proudly supervised a number of talented MSc and PhD students. She is also a certified Professional Engineer (P.Eng.) in Ontario. Working in both industry and academia, she realized that computer science and engineering education requires more attention - This inspired her to carry out research in Computer Science/Engineering Education and Human Computer Interaction, where these two areas converge. Besides CE and HCI research, she is also interested in Applied Data Mining. Her work has been published in the prestigious ACM and IEEE conferences and journals.

Dr. Maleknaz Nayebi

Maleknaz is an Assistant Professor at York University in Toronto. Before that she was a professor at Ecole Polytechnique of Montreal. She received her PhD degree from Software Engineering Decision Support (SEDS) lab at The University of Calgary in Canada. Maleknaz has been working on software open innovation with a focus on the platform mediated software products. She is one of the main collaborators on DEEL Collaborative Research and Development Grants with a total value of 2M dollars. She was also a professor of IVADO data science institute with the role of fostering cross-disciplinary collaboration with industry.

She has six years of professional software engineering experience. Her main research interests are in mining software repositories for open innovation in two-sided markets. Maleknaz co-chaired SANER 2020 ERA track, ESEM 2020 Emerging ideas track, RE data track 2018, IASESE 2018 advanced school, and several workshops. Maleknaz is a member of the IEEE and ACM. Maleknaz leads a team of students and associates on analytical methods for decision support in software innovation in society.