Human–AI Synergy and the Jagged Frontier of Generative AI

Ziele

The course aims to enable participants to critically understand and practically evaluate human-AI collaboration in the context of generative AI systems. Participants will learn to identify the “jagged frontier” of AI capabilities, distinguishing between tasks where AI augments human performance and those where it introduces risks or failure modes.
By the end of the course, participants will:

• Understand core concepts of human-AI complementarity and limitations of generative AI
• Critically assess when and how AI improves or hinders reasoning, learning, and productivity
• Design and conduct small-scale experiments to evaluate AI performance across task types Reflect on epistemic, ethical, and societal implications of human-AI collaboration
• Develop informed perspectives on the future of expertise, education, and knowledge production

Kursinhalte

The course covers three main thematic areas:

1. The Jagged Frontier of Generative AI

• Uneven capabilities of generative AI systems
• Differences between benchmarks and real-world performance
• Common failure modes and misleading fluency (“illusion of competence”)
• Comparative strengths of human vs AI cognition

2. Human-AI Synergy and Empirical Evidence

• Models of human-AI collaboration (assistant, tutor, collaborator)
• Productivity gains vs learning trade-offs
• Evidence from recent field experiments and meta-analyses
• Task-dependent complementarity and performance outcomes

3. Normative and Societal Dimensions

• Epistemic risks: overreliance, bias, and loss of skills
• Transparency, reproducibility, and accountability
• Impacts on education, research practices, and knowledge ecosystems
• Future scenarios of human-AI coexistence

Hands-on components include guided experimentation with AI systems, working with personalized agents, and group-based reflection on observed outcomes.

Form

The workshop uses a combination of interactive and experiential teaching formats:

• Lectures with discussion to introduce key concepts and empirical findings
• Hands-on lab sessions where participants design and run mini-experiments comparing human-only, AI-assisted, and hybrid workflows
• Group discussions and structured reflections to analyze outcomes and identify patterns of success and failure
• Collaborative exercises exploring real-world tasks
• Participant presentations or short reports summarizing experimental findings

The approach emphasizes active learning, critical thinking, and direct engagement with AI tools.

Adressatinnen und Adressaten

All Doctoral Candidates & Postdocs

Voraussetzungen

Participants should be comfortable holding short presentations in English and reading research papers in English.

Informationen zu den Dozierenden

Johanna Einsiedler is a postdoctoral researcher at the Digital Humanities Lab at the University of Basel and the Copenhagen Center for Social Data Science at the University of Copenhagen. Her work sits at the intersection of artificial intelligence and social science, with a focus on how machine learning can improve scientific research and how uncertainty in AI systems can be measured and understood.

Leistungsspektrum / Workload

Course attendance: 12h

Besonderheiten

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.