Artificial intelligence (AI) based mental health apps, especially chatbots, are increasingly being developed for youth, but rarely with their input, especially that of marginalised groups. This results in the development of apps that have low engagement and pose safety concerns. We use participatory methods to explore the preferences and design requirements of youth who face social exclusion, come from migrant backgrounds, or have low socioeconomic positions. We recruited 64 youths from youth work programs around the Netherlands and carried out 6 workshops. The first three explored the use of apps and large language models (LLMs) for well-being, while the last explored youth’s preferences for an LLM chatbot. Data was analysed thematically. Our results showed participants were open to using apps, preferring multifunctional apps, and identified human connection, self-development, and education as potential functions. However, they were reluctant to use chatbots, perceiving them as fake and lacking emotional intelligence. Instead, participants saw chatbots as providers of information, favouring shorter outputs with simple language, although they disagreed on how human-like chatbots should sound. Finally, the need for personalisation was emphasised, showing a desire for control with extensive customisation settings and clear privacy policies. Further work must be done to explore other relevant stakeholders’ views.

User engagement (UE) is widely discussed in HCI articles, but its definition, reliability, and application remain elusive. This research conducts a systematic literature review of 241 articles from 1993 to 2023 to analyze how UE is defined and measured within the domain of HCI. Our findings reveal significant definitional inconsistencies that hinder UE's practical application in HCI research and system design. Based on our findings, we recommend using UE as a categorical label rather than a unified construct until more systematic frameworks are established. We also highlight the need for divergent views of UE across HCI research communities as a valuable avenue to pursue. This divergent view approach can help HCI researchers focus on specific, measurable aspects of UE that align with specific community practices and norms. Our findings also suggest that until such a framework emerges, researchers should be aware of its limitations when using UE as a research construct.

Personas inform design by representing diverse user needs. Since their initial application in commercial technology contexts, personas have been adopted in several research domains for public good, such as health, accessibility, politics and civic society, education, sustainability, cybersecurity, and criminology. In this review paper, we analyzed 58 research studies that created personas in these domains, referred to as Personas for Social Good (PFSG). In most studies, PFSG was primarily exploratory and focused on initial methodology development. More than half (59%) neglected to discuss concerns with stereotyping or evaluate how personas contributed to improving social concerns in their respective domains. To facilitate a shift towards more socially conscious persona applications, we identified and critically examined the most comprehensive PFSG domain applications in our sample. Based on their strengths, we present an ecological framework to guide researchers in holistically aligning persona creation efforts with addressing critical social challenges.

Personas represent the needs of users in diverse populations and impact design by endearing empathy and improving communication. While personas have been lauded for their benefits, we could locate no prior review of persona use cases in design, prompting the question: how are personas actually used to achieve these benefits? To address this question, we review 95 articles containing persona application across multiple domains, and identify software development, healthcare, and higher education as the top domains that employ personas. We then present a three-stage design hierarchy of persona usage to describe how personas are used in design tasks. Finally, we assess the increasing trend of persona initiatives aimed towards social good rather than solely commercial interests. Our findings establish a roadmap of best practices for how practitioners can innovatively employ personas to increase the value of designs and highlight avenues of using personas for socially impactful purposes.

Data-driven persona development unifies methodologies for creating robust personas from the behaviors and demographics of user segments. Data-driven personas have gained popularity in human-computer interaction due to digital trends such as personified big data, online analytics, and the evolution of data science algorithms. Even with its increasing popularity, there is a lack of a systematic understanding of the research on the topic. To address this gap, we review 77 data-driven persona research articles from 2005–2020. The results indicate three periods: (1) Quantification (2005–2008), which consists of the first experiments with data-driven methods, (2) Diversification (2009–2014), which involves more pluralistic use of data and algorithms, and (3) Digitalization (2015–present), marked by the abundance of online user data and the rapid development of data science algorithms and software. Despite consistent work on data-driven personas, there remain many research gaps concerning (a) shared resources, (b) evaluation methods, (c) standardization, (d) consideration for inclusivity, and (e) risk of losing in-depth user insights. We encourage organizations to realistically assess their data-driven persona development readiness to gain value from data-driven personas.

Quantitative persona creation (QPC) has tremendous potential, as HCI researchers and practitioners can leverage user data from online analytics and digital media platforms to better understand their users and customers. However, there is a lack of a systematic overview of the QPC methods and progress made, with no standard methodology or known best practices. To address this gap, we review 49 QPC research articles from 2005 to 2019. Results indicate three stages of QPC research: Emergence, Diversification, and Sophistication. Sharing resources, such as datasets, code, and algorithms, is crucial to achieving the next stage (Maturity). For practitioners, we provide guiding questions for assessing QPC readiness in organizations.