> Source URL: /psychology.guide
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title: "Prompt Engineering for Psychology"
description: "How LLMs are showing up in psychology research and clinical work — and what you could build with them in this course."
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[@styles]: ./styles.css

# Prompt Engineering for Psychology

LLMs are already being used to code interview transcripts, synthesize literature, screen participants, and deliver interventions. They also raise questions about bias, consent, and behavior that psychology is uniquely positioned to answer.

## Where this is showing up in Psychology

- Tools like [Elicit](https://elicit.com), [Consensus](https://consensus.app), and [ATLAS.ti's AI coding](https://atlasti.com/intentional-ai-coding) are being used for literature review and qualitative data analysis in labs worldwide.
- [Woebot](https://woebothealth.com), [Wysa](https://www.wysa.com), and [Dartmouth's Therabot trial](https://ai.nejm.org/doi/full/10.1056/AIoa2400802) represent an early wave of LLM-based mental-health tools — with mixed clinical evidence and active ethical debate.
- The [APA](https://www.apa.org/topics/artificial-intelligence-machine-learning), [JAMA Psychiatry](https://jamanetwork.com/journals/jamapsychiatry), and [Nature Human Behaviour](https://www.nature.com/nathumbehav/) have published guidance and critiques on AI in clinical and research practice.
- Research from groups like [Binz & Schulz (PNAS, 2023)](https://www.pnas.org/doi/10.1073/pnas.2218523120) shows LLMs replicating — and sometimes amplifying — human cognitive biases, opening a new line of psychology-native AI research.

## Projects you could build in this course

- A tool that codes open-ended survey responses against a theoretical framework
- A RAG assistant for literature review across a specific area of psychology
- A journaling or reflection tool that demonstrates the design tradeoffs of AI in mental-health contexts

[← Back to Thinking With Machines](./index.path.md)


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## Backlinks

The following sources link to this document:

- [Psychology](/index.path.llm.md)