Course Identification

- Basic machine learning algorithms: supervised and unsupervised 

- NLP and Generative AI: ChatGPT/Bard, prompt engineering (interactive/batch)

- Stochastic nature of machine learning

- Data Privacy:  Data privacy considerations when using AI in education, data protection regulations (e.g., GDPR)

- AI Ethics and Bias: ethical considerations in AI, bias and fairness in education, strategies for mitigating AI bias

- AI autonomy: Human-in the loop vs. autonomous AI systems 

- AI in the Classroom: Integrating AI into teaching, available AI-powered educational tools and resources

- AI for Student Assessment: Using AI for automated grading and feedback, adaptive learning systems

- Future of AI in Education: future directions in AI and education, challenges and opportunities

Theory:

• Understand the basics of machine learning (supervised/unsupervised) and in what it differs from programming

• Be familiar with the current capabilities and limitations of machine learning in the context of science education, as well as with future directions and the dynamic nature of the field

Practice:

• Gain hands-on experience in applying AI-powered educational technology in the classroom 

• Apply generative AI and prompt engineering for:

  • Lesson planning 

  • design AI-based interaction for students

AI & Society:

• Discuss societal risks associated with AI:

• Privacy and data ethics

• Fairness and bias in decision making

• Algorithmic aversion and trust

TBD