ETHICAL CONSIDERATIONS IN AI AND MACHINE LEARNING FOR EDUCATION

Learning Objectives

• Understand the ethical implications of AI and ML technologies in education.
• Explore key ethical principles and frameworks relevant to AI and ML in educational contexts.
• Examine case studies highlighting ethical dilemmas and considerations in AI and ML applications.
• Develop strategies for promoting ethical AI literacy among students, colleagues, and stakeholders.
• Critically evaluate AI and ML algorithms, data sources, and decision-making processes for fairness, transparency, and accountability.
• Advocate for responsible and equitable AI and ML practices in education.
• Foster ethical decision-making and reflection in the design, implementation, and evaluation of AI and ML projects.
• Collaborate with peers to develop guidelines and resources for ethically integrating AI and ML technologies into educational settings.

Methodology and Implementation

This course employs a variety of interactive methodologies to engage participants in learning and reflection. These include lectures, case studies, group discussions, hands-on activities, workshops, and collaborative projects. Throughout the course, participants will have the opportunity to apply their learning to real-world scenarios and collaborate with peers to develop practical solutions to ethical challenges.

Assessment Implementation

Individual Reflections: Participants will be required to submit individual reflections at the end of each day, highlighting key insights, challenges, and questions raised during the sessions. These reflections will be assessed based on the depth of critical thinking, engagement with course content, and relevance to personal and professional practice.

Group Project: As a culminating activity, participants will work collaboratively in small groups to develop guidelines and resources for ethically integrating AI and ML technologies into educational settings. The group project will be assessed based on the clarity of communication, depth of analysis, creativity of solutions, and alignment with ethical principles and frameworks.

Peer Evaluation: Participants will engage in peer evaluation exercises to provide feedback on their peers' contributions to group discussions, activities, and projects. Peer evaluation will focus on factors such as active participation, collaboration, and contribution to the learning community.

Final Reflection Paper: Participants will write a final reflection paper synthesizing their learning throughout the course and outlining their action plan for promoting ethical AI and ML practices in their educational context. The reflection paper will be assessed based on the coherence of ideas, integration of course concepts, and feasibility of proposed strategies.

Daily Programme

Day 1: Understanding Ethical Considerations in AI and ML

Morning Session:

• Welcome and Course Overview
• Introduction to Ethical Principles and Frameworks in AI and ML

Afternoon Session:

• Case Study Analysis: Ethical Dilemmas in AI and ML Applications in Education
• Interactive Discussion: Ethical Decision-Making in Educational Settings

Day 2: Promoting Ethical AI Literacy

Morning Session:

• Strategies for Educating Students About Ethical AI and ML Practices
• Engaging Colleagues and Stakeholders in Ethical AI Literacy Initiatives

Afternoon Session:

• Workshop: Designing Ethical AI and ML Curriculum and Resources
• Collaborative Brainstorming: Ethical AI and ML Education Projects

Day 3: Evaluating AI and ML Applications

Morning Session:

• Fairness, Transparency, and Accountability in AI and ML Algorithms and Systems
• Techniques for Assessing Ethical Implications in AI and ML Projects

Afternoon Session:

• Hands-on Activity: Ethical Impact Assessment of AI and ML Applications
• Case Study Discussion: Ethical Challenges in AI and ML Data Collection and Analysis

Day 4: Advocating for Ethical Practices

Morning Session:

• Advocating for Responsible and Equitable AI and ML Policies and Practices
• Strategies for Engaging with Policy Makers, Industry Partners, and Community Stakeholders

Afternoon Session:

• Interactive Workshop: Developing Advocacy Campaigns for Ethical AI and ML in Education
• Resource Sharing and Collaboration: Building Networks for Ethical AI Advocacy

Day 5: Fostering Ethical Decision-Making

Morning Session:

• Simulations and Role-Playing Exercises: Ethical Decision-Making Scenarios in AI and ML Projects
• Reflection and Discussion: Ethical Challenges and Opportunities in AI and ML Implementation

Afternoon Session:

• Group Project Presentation: Guidelines and Resources for Ethical AI and ML Integration in Education
• Course Conclusion and Reflection