Day 1: Deep Dive into AI Technologies in Education

Session Goals:

• Gain an understanding of advanced AI technologies and their applications in educational contexts.

Topics Covered:

• Overview of AI technologies: Neural networks, deep learning, and natural language processing.

• Real-world applications of AI: Learning analytics, intelligent tutoring systems, and virtual learning environments.

Activities:

• Case study analysis of successful AI implementations in education.

• Group discussion: Trends in AI technology and their implications for education.

Day 2: Data and Analytics in Educational AI

Session Goals:

• Explore how data analytics can enhance decision-making in educational settings using AI.

Topics Covered:

• Types of data in education: Student data, learning outcomes, and engagement metrics.

• Utilizing AI for predictive analytics and personalized learning paths.

Activities:

• Hands-on workshop: Using data visualization tools to analyze educational data.

• Group exercise: Designing a data-driven learning intervention based on case studies.

Day 3: AI-Powered Assessment and Feedback

Session Goals:

• Learn about AI applications in assessment, grading, and feedback mechanisms.

Topics Covered:

• Automated assessments: AI grading systems, formative and summative assessments.

• Enhancing feedback with AI: Timeliness, personalization, and scalability.

Activities:

• Workshop: Evaluating and selecting AI assessment tools for specific educational needs.

• Peer review activity: Analyzing and providing feedback on AI-generated assessments.

Day 4: Designing and Implementing AI-Enabled Curriculum

Session Goals

• Develop strategies for integrating AI technologies into curriculum design and teaching practices.

Topics Covered:

• Curriculum frameworks that incorporate AI: Competency-based learning and project-based learning.

• Best practices in integrating AI tools into lesson plans and learning activities.

Activities:

• Workshop: Collaborative design of AI-infused curriculum modules.

• Group presentations: Sharing proposed curriculum designs and receiving constructive feedback.

Day 5: Ethical Considerations and Future Directions

Session Goals:

• Address ethical concerns and envision the future of AI in education.

Topics Covered:

• Ethical implications of AI: Data privacy, bias, and accountability.

• Future trends in AI and education: Opportunities and potential challenges.

Activities:

• Group discussion: Identifying ethical dilemmas and developing action plans for responsible AI use.

• Final project: Creating a strategic plan for implementing AI initiatives in participants' educational institutions.