Flag

We stand with Ukraine and our team members from Ukraine. Here are ways you can help

Home ›› Artificial Intelligence ›› ​6 Ways to Improve Psychological AI Apps and Chatbots

​6 Ways to Improve Psychological AI Apps and Chatbots

by Marlynn Wei
3 min read
Share this post on
Tweet
Share
Post
Share
Email
Print

Save

Repetitiveness, complicated setups, and lack of personalization deter users.

A new study of an AI chatbot and smartphone app to reduce drinking shows that users do not like repetitiveness, lack of individualized guidance, and complicated setups.  The study interviewed users to find out what barriers caused people to abandon either. 

Apps and chatbots can deliver effective interventions to improve sleep, decrease alcohol use, and reduce anxiety and depression, but the challenge is keeping users on the app. Patterns of user engagement vary widely in terms of frequency, intensity, timing, and accessed features. 

Sustained user engagement is a key factor in the success of psychological apps and chatbots. The number of app installs can be high, but only a small percentage of users use mental health apps consistently over time. One study found that after one week, only 5 to 19% of users continued to use mental health apps. Even when content is helpful, dropout rates are high. 

Features that increase engagement are appealing visual design, easy navigation, goal setting, reminders, and feedback. New content and a supportive positive tone keep users coming back.

One way to measure user experience is using the mobile app rating scale (MARS) which examines dimensions of engagement, functionality, aesthetics, and information quality. Another method is conducting user experience interviews or looking at consumer reviews.

Researchers in a recent study conducted semi-structured user interviews and found that the top reasons users stopped engaging were due to technology glitches, notification issues, repetitive material, and a long or glitchy setup. With the AI chatbot, users were frustrated by repetitive conversation, lack of control over navigation, and delivery platform.

Here are six features to enhance user engagement of psychological AI apps and chatbots:

1. Make setup easy. Complicated and glitchy setup deters users. One participant in the study described how their data disappeared after reregistration was required. Informed consent is ethically necessary for apps and chatbots dealing with mental health personal data, but a streamlined setup is equally important. 

2. Offer tracking. Tracking is an important way to get people to interact with the app or chatbot regularly. More importantly, tracking raises awareness and can change behavior. Mindfulness calls this developing an “observer mind,” a powerful stress management skill and catalyst for change. For example, tracking the number of alcoholic drinks one has daily helps people realize automatic habits.

3. Provide personalized feedback and accurate insights. Individualized guidance based on one’s data helps people get feedback or insight into their patterns. Tracking data around anxiety levels and timing can help predict anxiety episodes and narrow down potential triggers. Accuracy is critical. One participant described that the app said that they had met their daily goal when they had not. This lack of accuracy reduces user confidence in the app.

4. Make interactions less repetitive. Overly scripted and repetitive bots are not welcome. Like therapy, the therapeutic alliance between the user and the conversational agent determines whether people return. Novelty and a positive tone make the interaction therapeutic.

5. Ensure notifications are customizable, accurate, and timely. Faulty or absent notifications can deter users. If the app is based on changing daily habits, the timing of daily reminders is essential.

6. Prioritize user agency and avoid bottlenecking navigation with an unwelcome bot. Users should be able to navigate to resources on their own, rather than be forced to interact with a bot. Users described being frustrated with having to go through a bot to get to basic features. One participant in the study described how it felt “strange” to have the bot constantly bothering them when they were working on a task. This is like Microsoft’s Clippy, which caused a lot of user frustration.

These features will make psychological AI apps and chatbots more effective. Integrating personalized feedback, high-quality dynamic conversations, and a smooth glitch-free set up will improve both user engagement and enjoyment.

post authorMarlynn Wei

Marlynn Wei,

Marlynn Wei, MD, JD is a Harvard and Yale-trained psychiatrist, writer, interdisciplinary artist, and author of the Harvard Medical School Guide to Yoga. Dr. Wei is an expert contributor to Psychology Today and Harvard Health and has published in The Journal of Health Law, Harvard Human Rights Journal, and many other academic journals. Her research focuses on innovation and emerging technology, including empathic design, human-AI collaboration, AI in mental health and neurotechnology, and related legal and ethical issues. She is the creator of Elixir: Digital Immortality and other immersive and interactive performances. She is a graduate of Yale Law School, Yale School of Medicine, and Harvard Medical School's MGH/McLean psychiatry residency. Twitter: @marlynnweimd Website: www.marlynnweimd.com

Tweet
Share
Post
Share
Email
Print
Ideas In Brief
  • Personalized feedback, high-quality dynamic conversations, and a streamlined setup improve user engagement.
  • People dislike an overly scripted and repetitive AI chatbot that bottlenecks access to other features.
  • Tracking is a feature that engages users and develops an “observer mind,” enhancing awareness and change.
  • New research shows that users are less engaged in AI apps and chatbots that are repetitive, lack personalized advice, and have long or glitchy setup processes.

Related Articles

Discover the future of user interfaces with aiOS, an AI-powered operating system that promises seamless, intuitive experiences by integrating dynamic interfaces, interoperable apps, and context-aware functionality. Could this be the next big thing in tech?

Article by Kshitij Agrawal
The Next Big AI-UX Trend—It’s not Conversational UI
  • The article explores the concept of an AI-powered operating system (aiOS), emphasizing dynamic interfaces, interoperable apps, context-aware functionality, and the idea that all interactions can serve as inputs and outputs.
  • It envisions a future where AI simplifies user experiences by seamlessly integrating apps and data, making interactions more intuitive and efficient.
  • The article suggests that aiOS could revolutionize how we interact with technology, bringing a more cohesive and intelligent user experience.
Share:The Next Big AI-UX Trend—It’s not Conversational UI
5 min read

Curious about the next frontier in AI design? Discover how AI can go beyond chatbots to create seamless, context-aware interactions that anticipate user needs. Dive into the future of AI in UX design with this insightful article!

Article by Maximillian Piras
When Words Cannot Describe: Designing For AI Beyond Conversational Interfaces
  • The article explores the future of AI design, moving beyond simple chatbots to more sophisticated, integrated systems.
  • It argues that while conversational interfaces have been the focus, the potential for AI lies in creating seamless, contextual interactions across different platforms and devices.
  • The piece highlights the importance of understanding user intent and context, advocating for AI systems that can anticipate needs and provide personalized experiences.
Share:When Words Cannot Describe: Designing For AI Beyond Conversational Interfaces
21 min read

Uncover the dynamic landscape of UX design as artificial intelligence continues to reshape the field. With automated tools revolutionizing our roles, what does the future hold for designers?

Article by Michal Malewicz
The End of Design?
  • The article explores the impact of AI on UX design, questioning the future role of designers as automated tools become more prevalent.
  • It highlights the historical evolution of UX design and the commodification of design roles, emphasizing the shift from creative problem-solving to efficiency-driven practices.
  • It emphasizes the need for future designers to be generalists with strong decision-making skills, capable of leading projects and maintaining creativity in an AI-driven landscape.
Share:The End of Design?
9 min read

Tell us about you. Enroll in the course.

    This website uses cookies to ensure you get the best experience on our website. Check our privacy policy and