Ethical Considerations Debate:​

Organize a class debate on the ethical implications of AI in NLP, where students discuss topics like bias in language models, privacy concerns, and responsible AI development.​

Live NLP Demos:

Perform live demonstrations of AI-driven NLP applications, such as sentiment analysis of social media data or language translation using voice commands.
Let the students explain the underlying AI techniques while showcasing their real-time applications.

Group challenge with Chatbots:​

Assign group challenge where students design and build NLP-powered chatbots to address specific scenarios (e.g., customer support, language translation). Encourage students to use AI frameworks to develop chatbot algorithms and integrate them with messaging platforms.​

Interactive Coding Challenges:​

Provide students with an AI-driven code analysis platform where they can write and test NLP algorithms. The platform can offer instant feedback, suggest improvements, and visualize algorithmic processes.​

AI-Assisted Peer Review:​

Incorporate an AI tool that assists students in peer reviewing written assignments. The tool can identify grammar and style issues, but also evaluate the coherence and relevance of content.​ Include talks about plagerism.

Final AI Project Showcase:​

Conclude the course with a project showcase event where students present their advanced AI applications in NLP. Incorporate AI-powered audience engagement tools, such as sentiment analysis of audience reactions during the presentations.​