Critical Learning in the Age of AI

About this course

Additional course information

• Learning objectives

  By the end of this course, students will be able to:

  • Describe and explain how generative AI systems produce content and identify key features that distinguish AI-generated from human-generated work in academic settings.
  • Map and analyse the main stakeholders involved in the creation, use, and regulation of generative AI in education, highlighting their roles and interests.
  • Evaluate how generative AI influences key social and institutional contexts, such as universities, research, and media, and discuss implications for learning and academic integrity.
  • Apply core principles of learning theory to assess how AI tools can support or hinder their own study practices.
  • Analyse and reflect on their personal learning strengths and weaknesses using structured self-assessment activities.
  • Design an intentional balance between human and AI-assisted learning by developing strategies for critical, ethical, and independent engagement with AI tools.
  • Create and present a personal education plan (PEP) that demonstrates how to use generative AI responsibly to safeguard the quality and honesty of their academic learning.

• Course schedule and programme

  The first week focuses on personal learning and reflection. Students investigate how AI tools affect their study habits, apply learning theory to evaluate when AI supports or hinders understanding, and begin developing strategies for intentional, critical engagement with AI.

  The second week expands toward systems and responsibility. Students map the stakeholders involved in AI and education, analyze power dynamics, and debate ethical questions about authorship, accountability, and academic integrity. They then create a Personal Education Plan (PEP), a practical, values-based strategy for maintaining independent and meaningful learning in an AI-rich environment.

  By the end of the course, students will be able to apply analytical, ethical, and reflective skills to make informed decisions about how to use generative AI in ways that protect and strengthen their own learning at university and beyond.

• Forms of tuition and assessment

  Forms of tuition
  
  This course uses a mix of short interactive lectures, guided workshops, and collaborative activities designed to help students explore how to use generative AI responsibly while protecting the quality of their own learning.  Each session combines brief lectures introducing key ideas with hands-on exercises, group discussions, and reflection activities.

  Students will analyse examples of AI-generated content, compare it with human work, and discuss how to maintain honesty, depth, and independence in their studies. Active learning is central to the course. Students will work individually and in groups to apply theories of learning, evaluate AI-supported study methods, and test strategies for critical engagement with AI tools.

  Regular class debates and peer feedback sessions help students practise communicating their standpoint clearly and respectfully.  Structured in-class time is provided for ongoing assignments, including the daily logbook and the Personal Education Plan (PEP). These assignments guide students in applying course insights directly to their own academic context, developing habits that safeguard learning integrity beyond the classroom.
  
  Forms of assessment
  
  Assessment for this course combines individual reflection, practical application, and collaborative analysis to ensure students meet the learning goals related to safeguarding learning quality and independence in the age of AI. 

  • Individual Personal Education Plan (PEP) – 30% 

  Each student submits a written Personal Education Plan (~1500 words) that reflects on their learning process, critically evaluates their engagement with generative AI tools, and outlines a values-driven strategy for using AI in ways that protect the depth, honesty, and independence of their academic learning. 

  • Individual Daily Logbook – 30% 

  Students complete brief daily logbook entries (about 100 words each) responding to structured prompts. These entries encourage continuous reflection on how AI influences their study practices and demonstrate awareness of how to maintain active, critical learning.

  • Group Debate – 30% 

  At the end of the course, students participate in a structured debate on the role of generative AI in effective learning. This group assessment tests collaborative skills, ethical reasoning, and the ability to defend a standpoint on how to preserve learning quality in AI-rich education.

  • Group Presentation of Stakeholder Analysis – 10% 

  In small groups, students prepare and deliver a short presentation mapping stakeholders in the generative AI ecosystem and analysing related power dynamics and educational impacts. This assesses teamwork, applied understanding, and the ability to explain how different actors influence the integrity of learning environments.

• About the course organisers

  Isabel Braadbaart is a jack-of-all-trades educator, with experience ranging from primary school to Master’s level education. Her Master’s degree in Education focussed on the implementation of critical literacy skills within teachers’ daily practice. Currently she works as lecturer, researcher, and programme coordinator at the VU Faculty of Science. She has extensive experience with designing for learning, effective didactic practices, and academic skills development and supports two Bachelor programmes and a Master’s programme in this.

  Additionally, Isabel is passionate about supporting starting lecturers in developing their professional and educational skills, and her research focuses on exploring the development of agency within this group.

  Mary-Jo Diepeveen is a PhD candidate at the VU Faculty of Science, where she researches the integration of artificial intelligence in STEM education. She holds a Master’s degree in Neuroscience from the University of Amsterdam and has over eight years of experience at Microsoft, where she is now a Senior Content Developer creating innovative learning experiences for diverse global audiences. Her expertise includes teaching and designing curricula in data science, machine learning, and AI. 

  By combining deep industry experience with academic research, Mary-Jo brings a unique perspective on how emerging technologies can be critically and meaningfully integrated into education. Her current research focuses on supporting inclusive and effective STEM teaching practices and developing frameworks that help educators navigate the opportunities and challenges of AI in the classroom.

• Faculty and department

  Faculty of Medicine (MED)

  Anatomy & Neurosciences