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The Artificial Intelligence Revolution in Psychological Evaluation

For more than a century, psychology has relied on questionnaires and interviews to measure personality, emotions, or mental disorders. However, these methods have significant limitations, as they depend on the sincerity of people’s responses and memory. Now, with the emergence of artificial intelligence, a fascinating possibility opens up: to use language directly as a source of information about who we are and how we work psychologically.

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A team of researchers from the United States has developed a comprehensive guide on the use of large language models in psychological evaluation. This paper explains how these tools work, the opportunities they offer for research and clinical practice, and the risks that need to be handled with care.

For decades, psychology has depended almost exclusively on self-assessment through personality scales and lists of behaviors. Although this approach has allowed progress, it also presents problems, such as the tendency of people to exaggerate or minimize their symptoms. Language analysis offers a more objective alternative, since language is a behavior in itself.

In the 1990s, programs like LIWC allowed words associated with emotions in texts to be counted. However, this approach was limited, as it treated each word in isolation. The arrival of language models based on neural networks has revolutionized this area, allowing to represent complex meanings and emotional nuances.

Today, technology behind models like GPT or Llama allows us to analyze not only what words are used, but how they relate to each other. This opens the possibility that algorithms find invisible patterns in thousands of conversations, thus improving psychological evaluation.

Language models are systems trained with large volumes of text from different sources. They learn to predict words and, in the process, develop a surprising ability to capture meanings and tones. There are different architectures, each with specific uses in psychology, from classification of emotions to generation of hypothesis about personality.

To apply AI in the psychological field, it is crucial to have quality data. Researchers can work with different sources, from recorded interviews to social media publications. Each type of language has its advantages and limitations, and the preprocessing of this data is key to obtaining accurate analysis.

The possibilities of using language models in psychology are enormous. They have been used to predict personality traits and detect depression, achieving results comparable to those of human evaluators. Despite being in experimental phase, these applications show great potential to offer rapid and less intrusive evaluations.

However, the use of language models also presents risks. These systems can inherit biases from training data and pose privacy and security issues. The scientific community is discussing how to balance the benefits of these technologies with their environmental impact.

The future of psychological evaluation points to multimodal models that analyze not only words, but also gestures and facial expressions. Although LLMs will not replace psychologists, they are expected to become powerful allies, helping in the evaluation and follow-up of patients. The key will always be to maintain human supervision.