Generative AI in Teaching

The basic course is structured into four modules (12 hours) and introduces the foundations of Large Language Models (LLMs), their probabilistic nature, structural limitations, biases, hallucinations, and ethical implications. Participants learn how to use AI as a tool that supports learning while fostering critical thinking and metacognitive skills. The course also presents the current AI tool landscape, illustrating how different tools can be selected according to specific educational objectives, such as scientific search, literature review, computation, and course-specific content management. 

A major focus is placed on instructional design, including effective prompting strategies, authentic assessment, AI-resilient learning activities, and the integration of AI into teaching through established pedagogical frameworks such as TPACK and Bloom’s taxonomy. 

The course also addresses ethical, legal, and institutional aspects of AI adoption, including academic integrity, GDPR compliance, and the development of course-specific AI policies. Participants conclude the programme by designing and peer-reviewing an AI-enhanced teaching activity ready for implementation in their own courses. 

• Understand the capabilities, limitations, and ethical implications of generative AI, with particular emphasis on Large Language Models (LLMs) and their responsible use in higher education. 
• Select and effectively use generative AI tools to support teaching, learning, scientific information retrieval, content creation, and discipline-specific educational activities
• Design AI-enhanced teaching and assessment activities that foster critical thinking, metacognition, and responsible AI use, applying pedagogical frameworks such as Bloom's Taxonomy and TPACK. 
• Develop and implement responsible AI practices in education, considering academic integrity, privacy, institutional policies, and the effective integration of AI into both STEM and non-STEM teaching contexts.

Lectures will be taught by Daniela Rotelli, postdoctoral researcher in AI in Education, for a total of 12 teaching hours divided into 4 modules of 3 hours each.