This chapter explores human creativity in AI-assisted learning environments through the lens of student agency. We begin by examining four theoretical perspectives on agency, including instrumental, effortful, dynamically emergent, and authorial agency, and analyze how each frames the relationship between agency and creativity. Under each theoretical perspective, we discuss how the integration of generative AI (GenAI) tools reshapes these dynamics by altering students' roles in cognitive, social, and creative processes. In the second part, we introduce a theoretical framework for AI agentic engagement, contextualizing agency within specific cognitive, relational, and ethical dynamics introduced by GenAI tools. This framework is linked to the concept of Mini-c creativity, emphasizing personal relevance and self-directed learning. Together, these perspectives support a shift from viewing creativity as product-oriented to understanding it as a process of agentive participation and meaning-making. We conclude with two directions for future research focused on the creative process and performance in AI-assisted learning.
Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and is an emerging topic for HCI research on digital health. A key element of PM, the Polygenic Risk Score (PRS), uses genetic data to predict an individual's disease risk. Despite its potential, PRS faces barriers to adoption, such as data inclusivity, psychological impact, and public trust. We conducted a mixed-methods study to explore how people perceive PRS, formed of surveys (n=254) and interviews (n=11) with UK-based participants. The interviews were supplemented by interactive storyboards with the ContraVision technique to provoke deeper reflection and discussion. We identified ten key barriers and five themes to PRS adoption and proposed design implications for a responsible PRS framework. To address the complexities of PRS and enhance broader PM practices, we introduce the term Human-Precision Medicine Interaction (HPMI), which integrates, adapts, and extends HCI approaches to better meet these challenges.
Recent studies suggest that while generative AI (GenAI) can enhance individual creativity, it often reduces the diversity of collective outputs. A well-known example of this homogenization effect is by Doshi and Hauser (2024) who found that GenAI-generated plot ideas improved story writing creativity but led to convergence across writers' outputs. This study extends their experiment, identifying the design choices behind the apparent creativity-diversity trade-off. In Phase 1, we used structured prompting with 10 diverse GenAI personas to generate 300 story plots, and confirmed the plots' diversity using text embedding analysis. In Phase 2, participants wrote stories with or without access to these plots. Results show that diverse GenAI inputs can preserve story diversity compared to a human-only baseline, with some evidence of enhancement in the 1-plot condition. Beyond addressing the diversity component of the trade-off, our findings offer broader insights for human-AI system design. Our findings suggest that the trade-off may emerge from uniform deployment practices rather than from an inherent limitation of GenAI, and that diversity can be intentionally built into AI-mediated collaboration. Our study highlights the risks of over-standardization, the importance of prompt variation, and the value of treating GenAI not as a static tool but as a configurable partner. These insights have important implications for the design of GenAI systems that support, not constrain, collective creativity.
Kaitlynn Taylor Pineda, Ethan Brown, Chien-Ming Huang
Small talk can foster rapport building in human-human teamwork; yet how non-anthropomorphic robots, such as collaborative manipulators commonly used in industry, may capitalize on these social communications remains unclear. This work investigates how robot-initiated small talk influences task performance, rapport, and interaction dynamics in human-robot collaboration. We developed an autonomous robot system that assists a human in an assembly task while initiating and engaging in small talk. A user study ($N = 58$) was conducted in which participants worked with either a functional robot, which engaged in only task-oriented speech, or a social robot, which also initiated small talk. Our study found that participants in the social condition reported significantly higher levels of rapport with the robot. Moreover, all participants in the social condition responded to the robot's small talk attempts; 59% initiated questions to the robot, and 73% engaged in lingering conversations after requesting the final task item. Although active working times were similar across conditions, participants in the social condition recorded longer task durations than those in the functional condition. We discuss the design and implications of robot small talk in shaping human-robot collaboration.
Barshagul Baikara, Kobey Karamendin, Yermukhammet Kassymbekov
et al.
Low pathogenic H9N2 avian influenza viruses have become widespread in wild birds and poultry worldwide, raising concerns about their potential to spark pandemics or their role in enhancing the virulence and infectivity of H5Nx viruses through genetic reassortment. Therefore, influenza monitoring studies, including those of H9N2 viruses, are crucial for understanding, evaluating, and mitigating the risks associated with avian infections, and have broader implications for global public health. Although H9N2 viruses are not considered enzootic in Kazakhstan, they have been repeatedly detected in wild waterfowls and domestic poultry. In this study, all eight gene segments of influenza A/H9N2 viruses isolated in various regions of Kazakhstan between 2014 and 2020 were sequenced and analyzed. Molecular characterization revealed the presence of genetic markers associated with mammalian infectivity and disease potential. Furthermore, their predicted receptor binding site sequences indicate their potential capacity to attach to human-type receptors. These findings highlight the importance of continued surveillance and molecular investigation to better understand the evolution and zoonotic potential of H9N2 viruses in Kazakhstan.
Abstract Silurian strata are well-developed in the northwest margin of Yangtze Platform. A total of 117 densely spaced argillaceous samples were taken from the Shenxuanyi Member, upper Ningqiang Formation to the lowermost Chejiaba Formation of the Majia section in northern Sichuan Province. The main aim of this study is to obtain chitinozoans to test whether Wenlock deposits are preserved here. A highly diverse and abundant chitinozoan assemblage is documented, including 21 species from six genera. This assemblage was then compared to contemporaneous chitinozoan assemblages reported from adjacent areas. This study proposes that Eisenackitina venusta (corresponds to the Pterospathodus celloni conodont Biozone) is of chronostratigraphical significance for discussing the Telychian of the Yangtze region. It is suggested that the Silurian upper red bed (lower part of the Shenxuanyi Member) in the study area is younger than the Xiushan Formation and can be correlated with the Huixingshao Formation of the Central Yangtze Platform. There are no index chitinozoan species near the Llandovery–Wenlock boundary that have been found in the upper part of the Shenxuanyi Member, and the updated chronostratigraphic framework suggests that it is Telychian-aged deposits.
A hallmark of the highly conserved CYP4B1 enzyme in mammals is the capability to bioactivate both xenobiotic and endobiotic substrates. However, due to a single amino acid change (p.P427S) within the evolutionary conserved meander region no catalytic activity of the native human CYP4B1 has been identified so far. To identify at which point in human evolution the loss of CYP4B1 activity had occurred, we evaluated the activities of CYP4B1 orthologs from 14 primate genera against 4-ipomeanol and perilla ketone in human liver cells. The activity of recombinant CYP4B1 proteins isolated from E. coli was also tested against 4-ipomeanol and lauric acid. Surprisingly, CYP4B1 already became catalytically inactive at the split between apes and monkeys; all tested CYP4B1 orthologs from monkeys were able to bioactivate both protoxins and to hydroxylate lauric acid. Amino acid analysis of the CYP4B1 orthologs revealed four additional evolutionary changes, each affecting the function of ape and human enzymes: p.V71G specific for Denisovans, p.R106C, p.R244H, and an exon deletion found only in the gorilla CYP4B1. Systematic functional analyses proved the negative impact of the genetic changes on CYP4B1 activity and showed that reversion of the mutations restored enzyme activity. The occurrence of five independent inactivating genetic changes in the same gene of closely related species is a clear indication of the importance of inactivating CYP4B1 in apes and humans. Elucidating the evolutionary trigger(s) for CYP4B1 inactivation in our ancestors will ultimately improve our understanding of primate evolution.
Mirroring non-verbal social cues such as affect or movement can enhance human-human and human-robot interactions in the real world. The robotic platforms and control methods also impact people's perception of human-robot interaction. However, limited studies have compared robot imitation across different platforms and control methods. Our research addresses this gap by conducting two experiments comparing people's perception of affective mirroring between the iCub and Pepper robots and movement mirroring between vision-based iCub control and Inertial Measurement Unit (IMU)-based iCub control. We discovered that the iCub robot was perceived as more humanlike than the Pepper robot when mirroring affect. A vision-based controlled iCub outperformed the IMU-based controlled one in the movement mirroring task. Our findings suggest that different robotic platforms impact people's perception of robots' mirroring during HRI. The control method also contributes to the robot's mirroring performance. Our work sheds light on the design and application of different humanoid robots in the real world.
Conveying human goals to autonomous systems (AS) occurs both when the system is being designed and when it is being operated. The design-step conveyance is typically mediated by robotics and AI engineers, who must appropriately capture end-user requirements and concepts of operations, while the operation-step conveyance is mediated by the design, interfaces, and behavior of the AI. However, communication can be difficult during both these periods because of mismatches in the expectations and expertise of the end-user and the roboticist, necessitating more design cycles to resolve. We examine some of the barriers in communicating system design requirements, and develop an augmentation for applied cognitive task analysis (ACTA) methods, that we call robot task analysis (RTA), pertaining specifically to the development of autonomous systems. Further, we introduce a top-down view of an underexplored area of friction between requirements communication -- implied human expectations -- utilizing a collection of work primarily from experimental psychology and social sciences. We show how such expectations can be used in conjunction with task-specific expectations and the system design process for AS to improve design team communication, alleviate barriers to user rejection, and reduce the number of design cycles.
Social interactions promote well-being, yet barriers like geographic distance, time limitations, and mental health conditions can limit face-to-face interactions. Emotionally responsive AI systems, such as chatbots, offer new opportunities for social and emotional support, but raise critical questions about how empathy is perceived and experienced in human-AI interactions. This study examines how empathy is evaluated in AI-generated versus human responses. Using personal narratives, we explored how persona attributes (e.g., gender, empathic traits, shared experiences) and story qualities affect empathy ratings. We compared responses from standard and fine-tuned AI models with human judgments. Results show that while humans are highly sensitive to emotional vividness and shared experience, AI-responses are less influenced by these cues, often lack nuance in empathic expression. These findings highlight challenges in designing emotionally intelligent systems that respond meaningfully across diverse users and contexts, and informs the design of ethically aware tools to support social connection and well-being.
3D visualization and segmentation are increasingly widely used in physical, biological and medical science, facilitating advanced investigative methodologies. However, the integration and reproduction of segmented volumes or results across the spectrum of mainstream 3D visualization platforms remain hindered by compatibility constraints. These barriers not only challenge the replication of findings but also obstruct the process of cross-validating the accuracy of 3D visualization outputs. To address this gap, we developed an innovative revisualization method implemented within the open-source framework of Drishti, a 3D visualization software. Leveraging four animal samples alongside three mainstream 3D visualization platforms as case studies, our method demonstrates the seamless transferability of segmented results into Drishti. This capability effectively fosters a new avenue for authentication and enhanced scrutiny of segmented data. By facilitating this interoperability, our approach underscores the potential for significant advancements in accuracy validation and collaborative research efforts across diverse scientific domains.
Francesc Pérez-Peris, Jonathan M. Adrain, Allison C. Daley
Abstract Cheiruridae is one of the most diverse families of trilobites known from the Ordovician with 453 species assigned. Within Cheiruridae eight subfamilies (Acanthoparyphinae, Cheirurinae, “Cyrtometopinae”, Deiphoninae, Eccoptochilinae, Heliomerinae, Pilekiinae, and Sphaerexochinae) have historically been recognised. Insights about the evolution of the family and the relationships within and between subfamilies have been published. However larger scale phylogenetic hypotheses are needed in order to explore the monophyly, the basal structure, the deep nodes and the relationships of the subfamilies. Cheirurinae, Deiphoninae and “Cyrtometopinae” have historically been defined by various morphological features (e.g., anteroposterior constriction of the thoracic pleura, pleural furrow morphology, pygidial morphology) that differentiate them from the rest of Cheiruridae. However, the phylogenetic status of “Cyrtometopinae” is unclear owing to a lack of obvious synapomorphies. Here, we present phylogenetic analyses of Cheirurinae, Deiphoninae, and “Cyrtometopinae”. The results indicate that both Cheirurinae and Deiphoninae are monophyletic. “Cyrtometopines” are resolved as a paraphyletic grade at the base of Deiphoninae and Cyrtometopinae should be considered a junior subjective synonym of Deiphoninae. The new phylogenetic hypothesis reveals that paedomorphosis plays an important role in the evolution of Deiphoninae. Within Cheirurinae two major clades are identified, the ‘Ceraurus-like’ clade and the ‘Ceraurinella-like’ clade.
Catholijn M. Jonker, Luciano Cavalcante Siebert, Pradeep K. Murukannaiah
With the growing capabilities and pervasiveness of AI systems, societies must collectively choose between reduced human autonomy, endangered democracies and limited human rights, and AI that is aligned to human and social values, nurturing collaboration, resilience, knowledge and ethical behaviour. In this chapter, we introduce the notion of self-reflective AI systems for meaningful human control over AI systems. Focusing on decision support systems, we propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches to create AI systems responsive to human values and social norms. We also propose a possible research approach to design and develop self-reflective capability in AI systems. Finally, we argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI), thus increasing meaningful human control and empowering human moral reasoning by providing comprehensible information and insights on possible human moral blind spots.