AI and Language

Course Description

This course examines the foundations of large language models (LLMs) and artificial intelligence (AI) systems, as well as their practical applications in language-related tasks. The course has three main aims. First, emphasizing the "know and understand" dimension of AI literacy, it introduces students to the mechanisms underlying modern AI systems in an accessible way. Topics include distinctions between intelligent and non-intelligent systems and between artificial and human intelligence, fundamentals of machine learning and deep learning, how LLMs are trained, how they represent knowledge, and how they generate language. Second, building on this foundational understanding, the course explores strategies for using LLMs and AI systems effectively and responsibly in real-life language-related tasks. The course highlights the essential role of human agency in co-constructing knowledge with AI systems. Students learn how prompt engineering and LLM fine-tuning can support AI-assisted language analysis, annotation, generation, learning, and assessment, while maintaining active human control and judgment in the process. Third, the course addresses the ethical, societal, and cognitive implications of AI use. Issues include critical evaluation of AI output, bias and fairness, overreliance and skill atrophy, privacy and data governance, transparency and explainability, intellectual property and authorship, academic integrity, and the environmental impacts of AI development and deployment. Throughout the course, students will engage in collaborative, hands-on activities and projects to design, implement, and critically evaluate AI-supported solutions to language-related problems.

Code

APLNG 265N

Credits

3

Subject

APLNG