‘Did a robot write that?’: AI-generated digital storybooks

BackgroundShared reading is an important opportunity for parents and children to connect and learn, which can support later independent reading skills. Much of the research to date has examined shared reading as parents read physical print books with their children. This research has demonstrated that parents tend to engage in more activities that emphasise the meaning of the stories over the code (i.e., print). Here, we examine the focus of shared reading when parents are reading with their children on paper versus on a digital device and whether this differs across the preschool years.MethodsA total of 253 parents of children aged 0–5 years completed an online self‐report questionnaire. Parents reported on the frequency of engaging in meaning‐ versus code‐related activities during shared book reading on paper and on screen with their youngest child. We conducted a linear regression analysis contrasting code‐ versus meaning‐related activities on paper versus screen modality with age as a continuous variable.ResultsKey to our objectives, parents reported engaging in meaning‐related activities more frequently during shared reading on paper versus on screens and in code‐related activities more frequently during shared reading on screens than on paper. These effects did not differ across age, although overall, parents reported engaging slightly more frequently in shared reading activities in general when their child was older.ConclusionsThe findings show that parents are engaging with their children differently as they read together on paper versus screens. Consistent with prior research, we found that activities emphasising the meaning of stories dominate shared paper book reading experiences in the preschool years. Critically shared reading on screens tips this balance, with parents reporting more code‐related activities. These patterns identify the learning opportunities enabled by the affordances of shared reading on screens.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

Extra-textual talk (ETT), the spontaneous conversation that occurs alongside the text read aloud during book reading is a common but also critically important feature of shared reading that cultivates interactions and supports the language development of young children. This exploratory review of 45 papers describing observations and measures of spontaneous ETT in parent-and-child dyadic shared reading from 1977 to 2022 illustrates the varying methods of categorization and measurement of the ETT have been used. The purpose of this review was to organize and consolidate this large collection of previous research to address two research questions. First, how can current researchers and practitioners organize this array of systems for measuring and categorizing ETT proposed by past researchers? Across the studies reviewed, three primary approaches for measuring or categorizing parents’ spontaneous ETT were found based on (1) content, (2) overall reader style, or (3) quantity-based measures of volume and complexity. Secondly, this review addressed what specific factors researchers have tested for their impact on extra-textual talk during shared reading. The three major influences on ETT come from variability among (1) readers, (2) children, and (3) the books being shared in support of a triangular model of shared book reading. The specific subcategories used in typing and measuring ETT, as well as the subfactors of those variables that may affect it are discussed.

This paper reports findings from a research and development project to support fathers’ involvement with their children’s literacy development. Eighty Emirati fathers attended workshops about sharing reading with their young children, where they were provided with a variety of children’s picture books in both Arabic and English, and reported their experiences of sharing reading in their homes with their young children. In the context of a rapidly evolving United Arab Emirates, the role of fathers is changing within this multilingual society to encompass enhanced engagement with their young children. Emirati fathers reported their experiences via WhatsApp voice-notes and text messages, which offered a naturalistic and culturally appropriate way for them to share their everyday shared reading activities. The overarching takeaway from these responses indicates a sense of their joy at engaging with their children in such volitional shared reading experiences. This leads to a feeling of wellbeing for themselves and their children, as well as a way of being more engaged in child-rearing in this nation which is moving towards more shared spousal responsibilities and more of an emphasis on the importance of connecting with their young children, for not only their children’s benefit but also their own.

This study aims to investigate the latest trends in educational technology and identify emerging innovations in the scientific literature using the Bibliometric Analysis method. The background of the research confirms the importance of an in-depth understanding of the latest developments in technology-based education, given the rapid changes taking place in this domain. The results highlight the significant influence of countries such as the United States, Russia, Brazil, and China in initiating and realizing educational technology innovations. In addition, the study identified several significant journals, such as "Educational Technology Research," "Education and Information," and "Journal of Dental Education" that play a crucial role in documenting and disseminating information about the latest developments in educational technology. Another interesting finding is the emergence of the keyword 'gaming' as an innovation that increasingly dominates the educational technology literature, showing a trend towards game-based play and learning approaches in educational contexts. The conclusion of this study provides valuable insights for practitioners and researchers in the field of educational technology. This information can inform educational strategies, develop innovative projects, and direct future research to better understand and utilize educational technology in the learning process.

Artificial intelligence (AI) has revolutionized sports science, advancing performance analysis, injury prevention, and strategy optimization. However, its long-term impact remains underexplored. This study conducts a bibliometric and predictive analysis of -driven sports research over the past decade, identifying key contributors, emerging trends, and future directions through visualization techniques. Method: A systematic review was conducted on related sports research from 2014 to 2024 using the Web of Science Core Collection. Bibliometric tools Citespace and Vosviewer were employed to analyze publication trends, author networks, institutional collaborations, and keyword co-occurrences. Polynomial regression analysis was applied to forecast future research growth based on historical publication and citation trends. A total of 5,811 publications with 96,753 citations were identified. China, the United States, and the United Kingdom were the most productive countries, with China leading in volume but exhibiting lower citation impact. The Chinese Academy of Sciences, Stanford University, and the University of Oxford were the top research institutions. Keyword analysis revealed that "machine learning," "deep learning," and "computer vision" were the most studied topics, while emerging themes such as "stress analysis," "information processing," and "pose estimation" indicated shifts towards driven real-time monitoring and predictive analytics. Polynomial regression models predicted continued research growth, with publication trends following y = 354x ² + 1350x + 528 (r ² = 0.94) and citation growth modeled as y = 9680x ² + 24900x + 7850 (r ² = 0.99), suggesting sustained acceleration in AI applications within sports science. The integration of sports science has grown rapidly, with machine learning and computer vision playing a pivotal role in optimizing athletic performance, real-time feedback, and injury prevention. Predictive analytics and driven modeling can transform sports training by personalizing programs based on biomechanical and physiological data, enhancing both performance and injury resilience. However, disparities in AI adoption across regions and institutions highlight the need for greater international collaboration, particularly in developing regions where access to driven sports technologies is limited. Future research should refine -driven models for individualized training, integrate wearable sensor data for precision, and address ethical concerns. Additionally, policymakers and sports organizations should invest in AI-based training and health monitoring systems to bridge the gap between technology-rich and technology-limited regions. As it evolves, its role in sports science will expand, driving advancements in performance analysis, health monitoring, and strategic decision-making.

Focusing on the generative artificial intelligence (AI) tools using large language models, the present research explores the individual factors impacting the low-income Koreans’ attitudes toward generative AI tools utilized to search for information (e.g., ChatGPT, Google Gemini, etc.). Specifically, we first examine whether low-income Korean individuals’ prior AI experience, perceived usefulness of AI, and general attitude toward AI influence their attitudes toward generative AI tools (RQ1). Second, we examine whether the prior AI experience affects the attitude toward generative AI tools via the perceived usefulness of AI (RQ2). Third, we examine whether the prior AI experience influences the attitude toward generative AI tools via the general attitude toward AI (RQ3). Fourth, we examine whether the relationship between prior AI experience and attitude toward generative AI tools is serially mediated by the perceived usefulness of AI and general attitude toward AI (RQ4). To answer the research questions, we conducted a hierarchical multiple regression analysis and a mediation analysis using the low-income Koreans who were aware of the generative AI tools (n = 770). The results indicate that (1) both the perceived usefulness of AI and general attitude toward AI are positively associated with the attitude toward generative AI tools; (2) the perceived usefulness of AI mediates the relationship between the prior AI experience and the attitude toward generative AI tools; (3) the indirect effect of prior AI experience on the attitude toward generative AI tools, via the general attitude toward AI, is not statistically significant; and (4) the prior AI experience influences the attitude toward generative AI tools through a sequential process of the perceived usefulness of AI and general attitude toward AI. The findings provide important implications to enhance the attitude toward generative AI tools.

The general objective of the research was to determine the advances related to the startups and artificial intelligence. The specific objectives of the research are to identify the most successful startups that use artificial intelligence and the countries that invest the most in startups. Methodology, in this research, 53 documents have been selected, carried out in the period 2018 - 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, the number of startups is increasing rapidly on various continents and is applied in various economic sectors. Artificial Intelligence is having a significant impact on various human activities around the world. The current concern is the ethical use of AI, which is why various governments and international organizations are establishing recommendations and limitations for corporations that carry out such research. The startups that are currently emerging have artificial intelligence as their main component, due to the great advantages it offers. The United States, China and the United Kingdom are leading investment in startups worldwide. Conclusions, about the general objective of the research, to determine the advances related to the startups and artificial intelligence. The number of startups is increasing rapidly on various continents and is applied in various economic sectors. The current concern is the ethical use of AI. The startups that are currently emerging have artificial intelligence as their main component, due to the great advantages it offers. About the first specific objectives of the research, to identify the most successful startups that use artificial intelligence. On all five continents, there are several startups that use artificial intelligence and seek to provide technological solutions in the various fields of human activity. About the second specific objectives of the research, the countries that invest the most in startups. The United States, China and the United Kingdom are leading investment in startups worldwide.

Shared book reading at home is a beneficial language and literacy learning experience for young children. While there has been extensive research on shared book reading in general, more is needed on understanding what this looks like for multilingual refugee families and their preschool children, particularly among Burmese families in the United States. Past research has focused on literacy development among Burmese refugee children in the school context, but few studies exist on shared book reading among Burmese refugees and their preschoolers in the home context. This qualitative study highlights four Burmese mothers and their young children’s use of dual language books in shared book reading while incorporating interactive reading strategies like questioning, pointing and extratextual talk. Findings indicate that these mothers’ capacities to engage in book talk and scaffolding promote children's and caregivers' literacy skills. The study also highlights the need for resources and interventions that might be developed to support these families in their efforts to boost young children’s literacy using shared book reading strategies.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.

As Artificial Intelligence (AI) systems increasingly permeate emotionally sensitive and caregiving domains—such as eldercare, education, therapy, and family life—there is a growing need to assess national readiness beyond infrastructure, research output, or innovation metrics. Existing AI-readiness indices often prioritize technical capacity while neglecting caregiving ethics, emotional integration, symbolic trust, and cultural adaptability. Simultaneously, many national AI policies and strategies articulate ethical aspirations without corresponding real-time implementation in relational contexts. These tensions expose two critical and underexplored gaps: (1) the disconnect between national AI policy and real-time practice in AI–Family Integration (AFI), and (2) the misalignment between conventional AI indices and relational readiness metrics based on AFI. To address these blind spots, this study introduces the AI–Family Integration Index (AFII)—a ten-dimensional benchmarking framework designed to evaluate countries on emotional, ethical, legal, symbolic, and caregiving preparedness. The AFII comprises dimensions such as emotional literacy, caregiving equity, symbolic legitimacy, youth-AI exposure, consent frameworks, and cultural receptivity. Each dimension was scored on a 0–10 scale using mixed-method analysis, including secondary qualitative and quantitative data, policy reviews, and narrative synthesis. Equal weighting was applied across dimensions to ensure conceptual balance and reflect the interconnected importance of each component. This methodological choice mirrors best practices in composite indices (e.g., the Human Development Index), while also maintaining interpretive equity across culturally diverse nations. To bring the framework to life, the study integrates real-world examples—such as Singapore’s emotionally intelligent robotics in eldercare and Japan’s symbolic AI design in companionship—to ground abstract dimensions in lived, culturally contextual practices. These narrative insights enhance both the interpretability and policy relevance of the scores. The AFII was applied to a sample of thirteen culturally and economically diverse countries, including all top 10 countries ranked in the Stanford AI Index (2024). The results reveal striking contrasts between innovation-driven AI adoption and human-centered readiness. While Singapore (9.6) leads globally, nations such as South Korea (8.8) and Japan (8.7) closely follow. The top five countries—Singapore, South Korea, Japan, Sweden (8.6), and the United Kingdom (8.4)—all score above 8.4, reflecting strong alignment between AI policy, emotional intelligence, cultural values, and caregiving integration. In contrast, middle performers like the United States (7.4) and China (7.6), though technologically dominant, exhibit shortfalls in symbolic trust, emotional safety, and caregiving ethics. At the lower end, India (6.0), Brazil (5.2), and South Africa (4.8) show emerging promise but require substantial investment in emotional-AI literacy, inclusive infrastructure, and ethical governance. A key finding is the gap between ethical policy intent and real-time relational deployment. Singapore, South Korea, and Japan exemplify strong alignment between governance vision and practical rollout. Conversely, France and Germany, despite policy sophistication, reveal slower execution in emotionally grounded domains. This disjunction reveals the need for a robust policy–practice alignment matrix, which the AFII introduces as a diagnostic typology for guiding national strategy. Another notable insight is the divergence between AFII rankings and the Stanford AI Index. Countries like the U.S. and China rank in the global top two for AI power, yet place 8th and 9th respectively on the AFII. Conversely, relationally advanced nations such as Singapore and Sweden achieve top AFII scores despite more modest technical power rankings. This contrast underscores that AI excellence must incorporate emotional and ethical dimensions—not merely computational strength or research volume. By embedding emotional and symbolic metrics into a comparative framework, the AFII offers a timely, multidimensional tool for policymakers, ethicists, and technologists. It reframes AI discourse from technocratic performance to relational intelligence, ethical resonance, and caregiving responsibility—defining a path for AI systems that not only think or learn, but relate, care, and belong.

This study has an objective to identify the development and policies of educational technology application in ASEAN countries. Through the literature review and analysis, this recent study has compared the issue of educational technology development and policies in ASEAN countries. The reviewing country has been chosen based on the Information and Communication Technology (ICT) index amongst the ASEAN countries, that are Singapore (as the highest rank), Thailand & Indonesia (as the middle rank), and Myanmar (as the lowest rank). The result of the study shows that the majority of the countries focused to improve network capabilities in supporting online learning, and the policies of each country showed a similarity in improving the technology equity for the learner. However, Singapore shows more advance technological implementation such as the application of broader Artificial Intelligence in classroom activity, while the use of Artificial Intelligence (AI) in Thailand and Indonesia still in developing progress. In conclusion, the technology education development in ASEAN countries has moved forward through the past year and the policies of educational technology for each country have been similar in strengthening the ASEAN plan.

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science.

Early childhood education is crucial in laying a solid foundation for children's language and literacy development. In an era where digital technology is increasingly pervasive in daily life, it is important to understand how digital reading platforms can influence young children's language development. This study aims to explore the relationship between children's self-efficacy in using digital reading platforms and their English language development. Qualitative research methods were employed, collecting data through participatory observation and interviews with parents and early childhood educators. The research sample consisted of 30 children (aged 4-6 years) who regularly use digital reading platforms in formal and informal learning environments. The results indicate a positive correlation between children's self-efficacy levels in digital reading platforms and their progress in understanding and using English. The practical implications of this research emphasize the importance of strengthening children's self-efficacy and selecting appropriate digital reading platforms to support young children's language and literacy development. This study provides valuable insights for educators, parents, and educational technology developers in designing effective learning strategies for young children in the digital age.

The research paper aims to study The Rise of Artificial Intelligence: A Boon or a Bane under Legal Regime. It will examine how the Artificial Intelligence (AI) in the real world refers in various domains and across various fields to boost innovation with transformative technology. The jurisprudential aspect highlights the philosophy of law concerning Artificial Intelligence along with its fundamental principles and moral values. It will analyse AI as a catalyst for progressing in today’s current era which symbolises the capacity of human intellect with AI through development in the modern day. AI as a positive footprint in the society understands the interaction of humans and AI. It will also examine the AI as a loss of human touch and its values where there is a gap between emotional connection between AI and human. The growing privacy challenges with regard to breach of personal data is a threat to human world. It will analyse the ethical and legal concerns of AI systems with lack of privacy and data protection. The growing dependence of AI poses challenges in security concerns. It will understand the AI in legal profession while providing legal services. It contrasts AI with major countries like The United States, The United Kingdom and The European Union as a comparative analysis. It will examine that how AI is truly a boon or a bane.