Assistant Professor The University of Illinois Urbana Champaign
This study reviews AI-powered mobile apps for child mental health, evaluating their quality, readability, and functionality. Results highlight quality issues, poor readability, and a lack of child-friendly design. Future development should focus on improving accessibility, affordability, and evidence-based practices to better serve diverse and underserved populations.
Learning Objectives:
At the end of this session, attendees should be able to:
Upon completion, the participant will be able to describe the key characteristics and functionalities of AI-based mobile applications for child mental health, and analyze their potential to support social work practice in promoting equitable mental health care.
Upon completion, the participant will be able to evaluate the quality, readability, and accessibility of AI-driven mental health apps using tools like the Mobile Application Rating Scale, considering the importance of client-centered care and inclusivity in social work practice.
Upon completion, the participant will be able to identify the limitations of current AI-based apps in design, accessibility, and clinical validation, and propose strategies for integrating health technologies into social work education and practice to improve outcomes for diverse populations.
