Using AI in Education

Artificial intelligence is rapidly becoming an integral part of everyday study practices, but using AI effectively requires more than just prompting tools. This workshop introduces participants to evidence-based learning science frameworks and demonstrates how AI can support deep learning, critical thinking and self-regulated study habits rather than replace them. 
 
Through hands-on experimentation, guided reflection and discussion of real-life study scenarios, participants will explore the advantages and disadvantages of AI-supported learning. They will learn to recognise risks such as cognitive outsourcing, shallow processing and reduced retrieval effort, and will develop strategies to maintain agency, thinking skills and academic integrity when using AI tools. 
 
By the end of the course, participants will be able to design purposeful AI prompts that align with proven learning techniques, such as spaced retrieval, the Feynman technique, blurting and interleaving, while ensuring that AI enhances rather than undermines their learning processes. 

General information

Duration 3 hours
  • Foundations of learning science frameworks 
  • Responsible and reflective AI in the learning process 
  • Evidence-based learning techniques 
  • Prompting for learning 
  • Basic familiarity with common AI tools 
  • A personal study task to work with during the workshop 
  • Laptop or device with access to AI tools 
Students, tutors, and educators interested in using AI tools responsibly and effectively within academic study contexts.
Participants will be able to: 
  • understand core frameworks of learning science, including self-regulated learning, theories of motivation, and Bloom’s Taxonomy, in the context of planning and reflecting on their own academic learning tasks. 
  • analyse concrete AI-supported study scenarios to identify and evaluate the risks of AI-supported learning, such as cognitive outsourcing, shallow processing, and reduced retrieval effort. 
  • design and justify AI prompts that support evidence-based learning techniques, such as spaced retrieval, blurting, the Feynman technique, and interleaving to enable self-regulated study and prevent the outsourcing of thinking. 
Learning material will be provided during the course.
Interactive workshop with practical experimentation. 

Dates

Code Instructor Dates Available seats Venue
FS26-AIFE-01 Rani Obilisetty Venkata Akhila Fri 24 April 2026 (01:00pm - 04:00pm)
13 Universität Zürich Irchel Register
FS26-AIFE-02 Rani Obilisetty Venkata Akhila Thu 25 June 2026 (09:00am - 12:00pm)
20 Universität Zürich Zentrum Register