aidemia--modules-courseplan_type	Create a plan of a course
Which subject	Design and technology
What age group	Adult courses
What topic	L’Intelligence Artificielle 101
Number of lessons	34
Split into modules
Add goal and aims
Add intro
Add references
Any other preferences	Please also propose interactive project to present a very practical and hands on course content

Course Plan: L’Intelligence Artificielle 101

Course Introduction

Welcome to "L’Intelligence Artificielle 101," a comprehensive adult learning course designed to empower participants with foundational knowledge of artificial intelligence (AI) and its applications in design and technology. As AI increasingly influences various industries, this course aims to demystify the technology, enabling learners to engage critically and creatively with AI tools. Participants will explore the principles of AI, its ethical implications, and hands-on applications relevant to design and technology.

Goals and Aims

• Goal: To provide adult learners with a practical understanding of artificial intelligence concepts, applications, and ethical considerations in design and technology.

• Aims:

  • To introduce key AI terminology, concepts, and frameworks.
  • To explore AI tools and their applications in design and technology.
  • To examine ethical considerations in AI development and deployment.
  • To engage participants in interactive and collaborative projects that enhance learning.

Course Structure

Module 1: Introduction to Artificial Intelligence (Lessons 1-5)

1. Lesson 1: What is Artificial Intelligence?

   • Definition and historical context
   • Overview of AI technologies

2. Lesson 2: Types of AI

   • Narrow vs. General AI
   • Machine learning, deep learning, and neural networks

3. Lesson 3: Core Concepts of AI

   • Algorithms and data
   • Natural language processing and computer vision

4. Lesson 4: AI in Everyday Life

   • Real-world applications
   • Case studies in various industries

5. Lesson 5: Overview of Canadian AI Landscape

   • Major AI initiatives in Canada
   • Government policies and funding opportunities

Module 2: Design and Technology Applications of AI (Lessons 6-10)

6. Lesson 6: AI in Design

   • Generative design processes
   • AI-assisted tools for designers

7. Lesson 7: AI in Product Development

   • Enhancing prototyping with AI
   • Predictive analytics for market trends

8. Lesson 8: AI-Driven User Experience Design

   • Personalization and adaptive interfaces
   • Usability testing with AI tools

9. Lesson 9: Robotics and Automation

   • Role of AI in robotics
   • Case studies of automated design processes

10. Lesson 10: AI in Manufacturing

    • Smart factories and predictive maintenance
    • AI's impact on the supply chain

Module 3: Ethics and Societal Impacts of AI (Lessons 11-15)

11. Lesson 11: Ethical Considerations in AI

    • Introduction to AI ethics
    • Bias and fairness in AI

12. Lesson 12: Privacy and Surveillance

    • Data privacy concerns
    • Balancing innovation with ethical standards

13. Lesson 13: Employment and the Future of Work

    • AI's impact on job markets
    • Reskilling opportunities

14. Lesson 14: Accountability in AI Systems

    • Responsibility for AI-driven decisions
    • Legal implications of AI

15. Lesson 15: Addressing Bias and Discrimination

    • Strategies for equitable AI design
    • Case studies of bias mitigation

Module 4: Hands-On AI Projects (Lessons 16-25)

16. Lesson 16: AI Tool Overview

    • Introduction to popular AI tools (TensorFlow, PyTorch, etc.)

17. Lesson 17: Data Collection and Preparation

    • Techniques for gathering and preparing data

18. Lesson 18: Building Your First AI Model

    • Step-by-step practical experience

19. Lesson 19: User Interface Design for AI Applications

    • Designing user-friendly applications

20. Lesson 20: Project Work: Phase 1

    • Begin group projects using AI tools in design

21. Lesson 21: Project Work: Phase 2

    • Continue developing group projects

22. Lesson 22: Project Work: Phase 3

    • Finalize group projects for presentation

23. Lesson 23: User Testing and Feedback

    • Conduct tests on AI applications
    • Gather user feedback

24. Lesson 24: Preparing for Presentation

    • Tips and strategies for presenting projects

25. Lesson 25: Project Presentations

    • Groups present their projects for peer review

Module 5: Future Directions in AI (Lessons 26-34)

26. Lesson 26: Emerging Trends in AI

    • Overview of current research and advancements

27. Lesson 27: The Role of AI in Sustainable Development

    • Addressing climate change with AI

28. Lesson 28: Global Perspectives on AI Innovation

    • Comparisons of AI initiatives worldwide

29. Lesson 29: AI for Social Good

    • Case studies of positive social impacts

30. Lesson 30: Building AI Solutions

    • Problem-solving with AI technologies

31. Lesson 31: Career Pathways in AI and Design

    • Exploring job opportunities and required skills

32. Lesson 32: Networking with AI Professionals

    • Building connections within the industry

33. Lesson 33: Reflective Practice

    • Analyzing your learning journey and outcomes

34. Lesson 34: Course Evaluation and Future Learning

    • Feedback session
    • Resources for continued study

Interactive Project Proposal

Project Title: Designing an AI-Driven Prototype

Objective:

Students will work in groups to conceptualize, design, and prototype an AI-driven application that addresses a specific problem in design or technology.

Steps:

1. Idea Brainstorming: Groups brainstorm potential applications of AI.
2. Research Phase: Identify existing solutions and gaps.
3. Data Gathering: Collect and prepare relevant data.
4. Model Development: Use AI tools to create a prototype.
5. User Testing: Conduct usability tests within the target audience.
6. Final Presentation: Present the prototype, findings, and feedback received.

Outcomes:

Participants will apply hands-on skills with AI, enhance teamwork abilities, and gain real-world experience in developing AI applications.

References

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Prentice Hall.
2. Nilsson, N. J. (2014). Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
3. Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency.
4. Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
5. Canadian Institute for Advanced Research (CIFAR). (2023). AI Research and Initiatives in Canada. CIFAR AI Research

By following this structured course outline, participants will gain a comprehensive understanding of artificial intelligence and its applications in design and technology, equipping them for future innovations in the field.