Module overview
This module will provide an overview of how machine learning and Artificial Intelligence can be used to answer questions in different fields of psychology.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Understand and evaluate the logic behind typical questions and datasets used in psychological research (Conceptual Expansion).
- Search for information when developing research on AI applications in psychology (Research Literacy).
- Choose among a wide range of AI and machine learning tools based on typical use cases (Method Expansion).
- Use available AI tools (ChatGPT, Google Bard) efficiently and responsibly in professional contexts = (AI Literacy).
- Evaluate and discuss the benefits and challenges of using AI and machine learning to answer questions in psychology (Interdisciplinary Bridges).
Syllabus
The aims of this modules are:
- to provide psychology and life science students with an overview of available AI and machine learning tools for psychological questions.
- to provide technical students (computer science, engineering) with an overview of typical questions in psychology and the scientific rationale behind those questions and the datasets used to answer the questions.
- to train students in presenting and communicating information across disciplines (technical <> psychology), including awareness of conceptual pitfalls and potential for misunderstandings.
- to promote critical understanding of the use of AI in professional contexts.
Key topics may include:
- History of AI in Psychology
- AI applications in Cognition and Perception (e.g., Object recognition, depth perception, reading and language processing, haptics and grasping).
- AI applications in mental health and clinical Psychology (e.g., interaction with AI technology, mental health chatbots, prediction of treatment response, identification of biomarker of mental health, analysis of mental health questionnaires).
- AI applications in social and interpersonal Psychology (e.g., social robots, social media, moral decisions).
- Methodological aspects of AI applications (e.g., brain imaging, model comparisons).
Learning and Teaching
Teaching and learning methods
Teaching and learning methods include 11 x two-hour hybrid lectures, and 11 x one-hour tutorial sessions with student-expert groups. Hybrid lectures are taught half by an expert in psychology and half by an expert on AI and machine learning. In the tutorials, teaching assistants will present example studies that illustrate the specific AI tools to the respective weekly topic.
| Type | Hours |
|---|---|
| Preparation for scheduled sessions | 33 |
| Completion of assessment task | 70 |
| Wider reading or practice | 64 |
| Lecture | 22 |
| Tutorial | 11 |
| Total study time | 200 |
Resources & Reading list
Internet Resources
Journal Articles
Goetschalckx, L., Andonian, A., & Wagemans, J. (2021). Generative adversarial networks unlock new methods for cognitive science . Trends in Cognitive Sciences, 25(9), pp. 788-801.
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020). Deep learning in mental health outcome research: a scoping review. Translational Psychiatry, 10(1), pp. 116.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 70% |
| Coursework | 30% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat Information
Repeat type: Internal & External