Understanding and Evaluating AI for Mental Health

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Understanding and Evaluating AI for Mental Health

This course is part of The Mental Health Clinician and AI Specialization

Instructors: Oscar Kjell

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

How AI works on a conceptual level, including Machine Learning, Natural Language Processing, and Large Language Models.
How AI models learn (supervised, unsupervised, and deep learning), making use of context and multimodal data (text, audio, sensor).
How to evaluate the trustworthiness of AI systems, with a focus on validity, reliability, and bias.

Skills you'll gain

Tools you'll learn

Generative AI

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This course is part of the The Mental Health Clinician and AI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

This course is designed for students and professionals in psychology, medicine, and other clinical disciplines who want to build a practical understanding of AI and the skills to evaluate it critically.

You will explore the core concepts behind modern AI systems, including Machine Learning, Natural Language Processing, and Large Language Models, learning how these technologies work on a conceptual level and why they are increasingly relevant to mental health. You will also learn how to assess whether AI systems are trustworthy, with a focus on validity, reliability, and bias — and what it means for an AI tool to be safe and fair for use across diverse patient groups. Throughout, expert interviews with researchers and developers bring these concepts to life from the perspective of people who have actually built these systems. By the end of this course, you will have the foundation to understand, question, and critically evaluate AI tools — an essential skill as AI becomes increasingly present in mental health practice.

Welcome to this module, where we introduce you to this course and then move to the fundamentals of AI in mental health—what it is, how it works, and the techniques behind it, such as Machine Learning, Natural Language Processing, and Large Language Models. Rather than overwhelming you with technical details, the goal is to give you a practical, working understanding of AI and highlight what matters most for clinical practice.

What's included

9 videos5 readings2 assignments

9 videosTotal 103 minutes

The Mental Health Clinician and AI2 minutes
Welcome to this Module3 minutes
What is AI?4 minutes
The Basics of Machine Learning10 minutes
Examples of Machine Learning Techniques10 minutes
Natural Language Processing10 minutes
Multimodal Large Language Models – Text, Audio, and Video5 minutes
Andrew Schwartz – What Words Reveal: An AI Researcher's View26 minutes
Gabriel Bonnin - The Intersection of Psychotherapy and AI32 minutes

5 readingsTotal 60 minutes

Course Overview10 minutes
Meet the Lecturers10 minutes
Open-source versus Proprietary AI15 minutes
Interviews: Meet the Experts10 minutes
Summary, Key terms, and Readings15 minutes

2 assignmentsTotal 50 minutes

Fundamentals of AI Quiz30 minutes
Reflection: Open-source vs. Proprietary AI20 minutes

Welcome to this module on validity and reliability, where we discuss the importance of carefully training and thoroughly evaluating AI. We will cover what validity and reliability mean in the context of AI, how bias can emerge and lead to unfair outcomes, and why psychometrics matter for developing safe and effective AI applications for mental health.

What's included

7 videos5 readings2 assignments

7 videosTotal 55 minutes

Welcome to this Module2 minutes
Validity, Reliability, and Biases in AI12 minutes
Evaluating Severity Scores and Diagnostic Classifications7 minutes
Training and Validating against Best-Estimate Assessments6 minutes
Prospectively Evaluating AI Models in New Data7 minutes
The Foundations of AI in Mental Health: From Methods to Evaluation5 minutes
Deven Shah – Bias in AI: From Research to Real-World Deployment17 minutes

5 readingsTotal 65 minutes

Effect Sizes: From Statistical Significance to Practical Importance15 minutes
Reporting Guidelines and Evaluation Standards for AI 15 minutes
Interview: Meet the Expert10 minutes
Summary, Key terms, and Readings15 minutes
Course Summary10 minutes

2 assignmentsTotal 50 minutes

AI Evaluation Quiz30 minutes
Reflection: Evaluating an AI tool20 minutes

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Instructors

Oscar Kjell

Lund University

0 Courses0 learners

Offered by

Lund University

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.