The proliferation of artificial intelligence (AI) technologies has led to the rise of conversational decision-making support systems, such as dialogue persona systems that provide conversational access to various user segments. For example, product managers can ask personas about features before implementing them, politicians can learn about the needs of local communities through personas, and so on. Nascent research has looked at challenges when users interact with AI personas, but has not framed it as a principal–agent problem, in which the AI represents a persona that itself represents real people in the data. This setting exposes unique interaction challenges that decision makers face when engaging with AI-generated conversational personas, which we examine through a user study with 56 participants using AI-generated conversational personas. Our results indicate seven interaction challenges: (1) Hidden Information, (2) Hidden Personas, (3) Hidden UI, (4) Lack of AI Agency, (5) AI’s Selective Attention, (6) Confusing Distributional Information, and (7) Conversational Cold Start that we conceptually link with agency theory. We discuss how the interaction challenges could be alleviated and suggest directions for future work. • This article explores how decision makers interact with AI-generated conversational personas derived from real survey data. • It conducts a comparative study between conversational personas and traditional profile personas in decision-support contexts. • The study employs a think-aloud user study with 56 participants to capture interaction experiences and challenges. • It identifies seven specific interaction challenges unique to conversational personas that may hinder effective decision making. • The article provides insights and recommendations for designing conversational decision support systems using AI-generated personas.

We examined the accessibility of online information services (OISs) for underserved communities through a user study involving 56 blue collar participants interacting with a website and an app for five tasks. The blue collar participants were generally unsuccessful on both platforms, with 12.7% (n=7) unable to successfully complete any tasks; a hundred percent required at least minor assistance. Participants were also inefficient, taking 28.62 more steps than optimal (143.1%) on the website and 10.41 more steps (47.3%) on the app. Time inefficiency was also noteworthy, with 535.76 more seconds than optimal (248.0%) on the website and 266.55 more seconds (142.4%) on the app. Though still poor, the app yielded better outcomes with higher success rates and usability ratings. Digital proficiency correlated with success on both platforms, which is good news as this is addressable by OIS providers. Qualitative analysis revealed that many in this underserved population were unaware that these valuable OISs were available to them. Findings underscore the need for OIS providers to prioritize targeted outreach to inform underserved communities that OISs are open and welcoming. Designing OISs with accessibility and simplicity targeted for mobile devices is crucial for bridging the digital literacy gap and empowering underserved communities to engage effectively with OISs.

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.

Demographics are considered foundational information in most persona profiles. However, the effect of persona ethnicity and gender on designers’ identification with the persona has limited evaluation in the human-computer interaction literature. We conducted a study with 64 professional designers from the United States, Indian, Korean, and Mexican nationalities to investigate the effects of AI-generated persona ethnicity and gender on persona identification. The personas were created using Generative AI in the persona narratives and the persona video creation. The contribution of this work is that, against assumptions, neither persona ethnicity nor gender play a major role in persona identification among designers with different ethnic backgrounds. While there were some insinuations of ethnicity and gender in the open-ended feedback from the designers, the emergent qualitative themes describing persona identification were overwhelmingly universal and applicable regardless of ethnicity or gender. This implies that professional designers can effectively use personas with different demographic backgrounds, and effects of demographic attributes in personas leading to stereotyping are less impactful than presumed.

Stakeholders like designers use personas to learn about users. After persona development, stakeholders are usually presented with a persona set. However, there is little research on how stakeholders select a persona from a persona set. A think-aloud analysis with 37 stakeholders who were asked to select a persona for a content design task reveals that persona selection is influenced by comparative, non-comparative, and subjective elements. Persona choice is often made with task compatibility in mind: interests, professions, and education were important contextual factors in our focal task. Storifying is commonly applied by stakeholders, reflecting personas’ narrative nature. The persona’s picture is often evoked, in addition to nationality and name, though demographics do not play a decisive role. Stakeholders refer to a host of persona attributes when explicating their persona choice. Overall, reasonings for persona choice are multifaceted and individualistic, as we might expect given the information-richness of personas.

Cipherbot, an educational chatbot using large language models to answer student questions concerning learning materials uploaded by the educator, was pilot tested in a classroom setting. Forty-four students used Cipherbot for seven weeks, sending 8077 messages. The average number of messages sent per student was 184 (SD = 80), with an average length of 98 characters (SD = 80). The engagement followed a non-normal distribution, with few power users, implying that most students are still hesitant to adopt tools like Cipherbot. Cipherbot was able to answer 82.5% of the student questions, demonstrating a scalable ability to address students' learning queries, with some room for improvement.

This research characterises user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes, comments, cross-platform posting). We find that sentiments and topics differ by both news outlets and social media platforms, and both sentiments and topics by the four levels of user engagement expression. Finally, we predict a volume of four user engagement levels for given news content, with an 83% maximum average F1-score for the external posting of news articles from one platform to another using language and metadata features. Implications are that news outlets can benefit by developing a platform, sentiment and topic, and strategies to best achieve user engagement objectives.

Problem-based learning (PBL) educators resorted to developing their own environments even with existence of comprehensive Learning Management Systems (LMS). They realized that both commercial and open source systems were designed with traditional lecture-based instruction in mind. The systems lack the features and scenarios required by the inherently collaborative, dynamic, and timely pedagogy. The presence of several PBL flavors and models added more complexity to the development process preventing any single environment from being unanimously recognized by the PBL community. In this paper, we review and discuss the implementation approaches that were used in developing PBL environments in the open-source LMS Moodle. We focus on the instantiation methodologies, scaffolding, customizability, features, and flexibility. We go over a wide range of PBL implementations starting from the basic ones that used whatever features available without adding any new functionality. We then browse two Moodle environments that implemented a particular PBL model. Finally, we discuss a two environment that used scripting in two different forms, one benefitted from a standard like IMS Learning Design (IMS-LD) and the other made use of a specially developed PBL Domain-Specific Modelling Language (DSML).