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.
MR DAHL, CED, Aarhus University