This means I can finally give you advice based on real usage patterns instead of hunches. I annotated OpenAI’s chart with some suggestions about when to use free versus advanced models.

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Our study suggests that concerns around personalization and AI persuasion are warranted, reinforcing previous results by showcasing how LLMs can outpersuade humans in online conversations through microtargeting. We emphasize that the effect of personalization is particularly remarkable given how little personal information was collected (gender, age, ethnicity, education level, employment status and political affiliation) and despite the extreme simplicity of the prompt instructing the LLM to incorporate such information (see Supplementary Section 2.5 for the complete prompts). Even stronger effects could probably be obtained by exploiting individual psychological attributes, such as personality traits and moral bases, or by developing stronger prompts through prompt engineering, fine-tuning or specific domain expertise.

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A new survey from The Verge and Vox Media addresses this moment of internet fragmentation.

Anti - surveillance apps

Bucher will discuss how she uses artificial intelligence (AI), specifically reinforcement learning, in her work at Lirio to personalize at scale

learning module

Audiopedia Academy GPT is an advanced AI-powered assistant built using OpenAI's GPT technology. Designed specifically to help Community-Based Organizations (CBOs), NGOs, government organizations, and individual stakeholders, this tool guides users through the EDUC method for creating meaningful and effective audio-based outreach campaigns. This interactive, empathetic, and resource-aware tool is now available at adp.ax/gpt.

The development of effective interventions for COVID-19 vaccination has proven challenging given the unique and evolving determinants of that behavior. A tailored intervention to drive vaccination uptake through machine learning-enabled personalization of behavior change messages unexpectedly yielded a high volume of real-time short message service (SMS) feedback from recipients. A qualitative analysis of those replies contributes to a better understanding of the barriers to COVID-19 vaccination and demographic variations in determinants, supporting design improvements for vaccination interventions. Objective: The purpose of this study was to examine unsolicited replies to a text message intervention for COVID-19 vaccination to understand the types of barriers experienced and any relationships between recipient demographics, intervention content, and reply type. Method: We categorized SMS replies into 22 overall themes. Interrater agreement was very good (all κpooled . 0.62). Chi-square analyses were used to understand demographic variations in reply types and which messaging types were most related to reply types. Results: In total, 10,948 people receiving intervention text messages sent 17,090 replies. Most frequent reply types were “already vaccinated” (31.1%), attempts to unsubscribe (25.4%), and “will not get vaccinated” (12.7%). Within “already vaccinated” and “will not get vaccinated” replies, significant differences were observed in the demographics of those replying against expected base rates, all p . .001. Of those stating they would not vaccinate, 34% of the replies involved mis-/disinformation, suggesting that a determinant of vaccination involves nonvalidated COVID-19 beliefs. Conclusions: Insights from unsolicited replies can enhance our ability to identify appropriate intervention techniques to influence COVID-19 vaccination behaviors.

This narrative review summarizes several health equity frameworks to help digital health practitioners conceptualize the equity dimensions of importance for their work, and then provides design approaches that accommodate an equity focus. Specifically, the Double Diamond Model, the IDEAS framework and toolkit, and community collaboration techniques such as participatory design are explored as mechanisms for practitioners to solicit input from members of underserved groups and better design digital health tools that serve their needs.

Video: https://www.youtube.com/watch?v=N_TOX9mjEtw

This highlights some really important things to consider in creating behavior change interventions - there isn't one “user journey“ - as Amy said many times, personalization will matter (and we can have a whole other conversation on what personalization means). There may be a “dose“ effect for some people where they need to accumulate a certain understanding before any message works and it is more about the dose than the personalization (or not) of the most proximal message.

Planned Parenthood sexual health chatbot

Our latest capability “Ask Viamo Anything” is providing access to the latest AI technology to the digitally disconnected – at no cost to them. It was built and will soon be offered on the Viamo Platform. Ask Viamo Anything works on simple mobile phones without internet access. And because of its use of voice technology, it can even be used by people with low literacy — leapfrogging text-based approaches and truly democratizing access.

Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-thewild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification.