Section outline

      • Video: "Ethics and Sustainability in AI"

        Reading: Responsible AI use in education.

        Case Studies: Ethical dilemmas and solutions.

        • Reading: Responsible AI Use in Education

          • AI introduces ethical challenges such as bias, misinformation, and privacy concerns. Educators must ensure transparency, fairness, and accountability when integrating AI tools.
          • Key principles:
            • Bias Mitigation: AI models can perpetuate stereotypes. Validate outputs and diversify training data sources.
            • Privacy Protection: Avoid sharing sensitive student data with third-party tools.
            • Academic Integrity: Establish clear guidelines for AI-assisted work.
            • Environmental Impact: Large AI models consume significant energy. Opt for efficient tools and limit unnecessary queries.
          • Frameworks like UNESCO’s AI Ethics Guidelines and institutional policies should guide practice.

        • Video: AI Ethics 101: What Educators and Students Should Know

          Duration: 5:53
          License: Creative Commons
          Overview: Covers fairness, transparency, accountability, privacy, and responsible AI use in education. Includes practical questions for reflection.

        • Case Studies:

          • Example 1: AI-generated feedback introducing bias.
          • Example 2: Carbon footprint of large-scale AI deployments.
      • Bias Spotting Challenge: Review AI-generated text for biased language.
        Privacy Policy Review: Compare two AI tool policies and identify risks.
        Carbon Footprint Estimator: Calculate energy use for AI queries and discuss sustainability strategies.