How will AI empower humans? Dr. Haiyi Zhu explores this topic and more in this second article in a series on AI visionaries. Continue reading...

Sam Altman discusses coming transformation from traditional interfaces to AI-powered natural language computing systems.

A recent study published in the International Journal of Human-Computer Studies sheds light on how Danish high school students use chatbots for social interactions. The study found that while most students...

Sam Altman discusses coming transformation from traditional interfaces to AI-powered natural language computing systems.

Meta’s Aria Gen 2 smart glasses come with AI chips, cameras, sensors, and heart rate tracking to support research.

Background: It is challenging to design usable and effective digital health interventions (DHIs). The person-based approach (PBA) has been proposed to incorporate users’ perspectives for the design of DHIs. However, it does not explicitly describe the iterative stages of design and evaluation that are essential in moving from early planning to deployment. For this, we draw on methods from human computer interaction (HCI) that have been developed for various situations. Objective: This study aimed to reflect on the adaptation and synthesis of PBA and HCI approaches to developing DHIs. We present a case study applying both approaches to develop Empowering People through Informed Choices for HIV (EPIC-HIV1), a DHI designed for men living in rural KwaZulu-Natal, South Africa, intended to support them in making an informed choice about whether to take an HIV test and, if necessary, engage in care. Methods: We conducted a retrospective analysis of the documentation generated during the development of EPIC-HIV1 including findings about requirements, design representations, and the results of iterative rounds of testing. We developed an account of the process, the outcomes, and the strengths and limitations of the design and evaluation techniques applied. We also present the design of EPIC-HIV1 and summarize considerations when designing for hard-to-reach people in such settings. Results: The PBA was applied to deliver a first prototype. This helped identify key messages to convey and how to manage issues such as user privacy, but the resulting prototype was judged by the team not to be engaging for potential users, and it was unclear whether the design was inclusive of people with low digital or health literacy. We therefore introduced methods from HCI to iteratively test and refine the app. Working with local community representatives, we conducted four refinement cycles with 29 participants, adapting and retesting the app until no further changes were needed. Key changes included making it clearer what the consequences of selecting options in the app were and changing wording to minimize misconceptions (eg, that the app would test for HIV) while addressing common concerns about testing and emphasizing long-term benefits of engaging with care, if needed. Conclusions: Techniques for developing DHIs need to be situationally appropriate. The PBA enabled us to establish both empirical data and theory to design the content of EPIC-HIV1, but it did not directly inform interaction design to make the app usable and effective for the intended users; HCI techniques tailored to the setting enabled us to refine the app to be easy for men with little familiarity with digital technologies to use within the constraints of the setting. Iterative testing ensured the app was easy to use and that the intended clinical messages were communicated effectively.

A recent study published in Neuroscience of Consciousness sheds light on a surprisingly powerful factor that shapes how we make — and revise — decisions: our own sense of confidence. The research...