Psychology in the Era of AI

Transformative Implications for Research and Practice?

Renato Frey

Course Description

This course delves into the transformative potential of AI and particularly Large Language Models (LLMs), which have sparked immense interest both in the general public and the scientific community. Students will develop a basic understanding of the principles and functioning of AI models, and then dive into the opportunities but also perils of various groundbreaking applications of AI and LLMs. For instance, we will discuss questions such as: How do LLMs affect the professional training of future academics in the context of university curricula? What are the implications of AI methods for basic (psychological) research? And more broadly, what are the implications of AI for human cognition, and consequently, society as a whole? To accommodate the very dynamic development of this field and the associated, constantly emerging issues, this course will adopt a highly flexible format with plenty of time for discussion and active student inputs.

Learning Objectives

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

  • ✅ Understand the fundamental mechanics of AI and LLMs (tokens, transformers, training).
  • ✅ Critically evaluate how AI tools transform academic training and professional development in psychology.
  • ✅ Analyze the opportunities and risks of integrating AI into psychological research methodologies.
  • ✅ Discuss and synthesize the broader cognitive and societal implications of AI developments and propose adaptive strategies for researchers and practitioners.

Literature

All reading assignments will be provided through the collaborative reading platform Perusall.

Access Reading Assignments →

Course Schedule

Introduction

Core Themes: Course orientation & alignment

Module 1: How AI Works

An introduction into how AI works, moving from historical symbolism to connectionism and the Transformer revolution.

Core Themes: Good old fashioned AI (GOFAI), deep learning, transformers

Preparation:
Lee, T. B., & Trott, S. (2023). Large language models, explained with a minimum of math and jargon. Understanding AI.
https://www.understandingai.org/p/large-language-models-explained-with

Additional resources:

Core Themes: Vanishing gradients, self-attention, local vs. global context

Preparation:
Lee, T. B. (2018). How computers got shockingly good at predicting the next word.

Additional resources:

Core Themes: Steering via RLHF & SFT; hallucinations

Preparation:
Hussain, et al. (2024). A tutorial on open-source LLMs for behavioral science.

Additional resources:

Module 2: AI as Models of Human Cognition and Behavior?

Exploring whether AI can serve as a proxy for human cognitive processes or participants.

Core Themes: Plausibility vs. truth; Cognitive mirroring vs. Stochastic Parrotism

Preparation:
Binz, M., et al. (2025). A foundation model to predict and capture human cognition. Nature. DOI
Bowers, J., et al. (2025). Centaur: A model without a theory. OSF. DOI

Core Themes: Validity of synthetic data

Preparation:
Crockett, M. J., & Messeri, L. (2025). AI Surrogates and illusions of generalizability in cognitive science. DOI

Core Themes: Signal processing & inference

Preparation:
Feuerriegel, S., et al. (2025). Using natural language processing to analyse text data in behavioural science. Nature Reviews Psychology. DOI

Core Themes: Student Lightning Demos

Module 3: Academic Transformation — AI as Tools in Research

Examining the transition from simple offloading to agentic workflows and the resulting transformation of academic training.

Core Themes: Augmented intelligence vs. atrophy; Algorithmic bias

Preparation:
Guest, O., et al. (2026). Against the uncritical adoption of “AI” technologies in academia. Digital Culture and Education, 16(2).

Core Themes: Curatorial vs. Generative competence; Historical parallels of EdTech adoption.

Preparation:
Binz, M., et al. (2025). How should the advancement of large language models affect the practice of science? PNAS. DOI
Demszky, D., et al. (2023). Using large language models in psychology. Nature Reviews Psychology. DOI

Core Themes: Student Lightning Demos

Module 4: Implications of AI for Human Cognition and Society

Analyzing the long-term impact of AI agents on cognition and existential risks.

Core Themes: Cognitive offloading & decline

Preparation:
Cash, T. N., et al. (2026). Is AI making us stupid? Trends in Cognitive Sciences. DOI

Core Themes: Alignment & Existential risks; Paperclip problem; Pacing the frontier.

Preparation:
Spitale, G., et al. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances. DOI