Family Relations最新文献

{"title":"AI robots promote South Korean preschoolers' AI literacy and computational thinking","authors":"Boram Lee,&nbsp;Seulki Ku,&nbsp;Kwangman Ko","doi":"10.1111/fare.13189","DOIUrl":"https://doi.org/10.1111/fare.13189","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Using a mixed-methods approach, we explored the role of AI humanoid robots in the development of child AI literacy and computational thinking. We also examined teachers' and parents' experiences and perceptions of AI robots.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The rapid development of AI has fundamentally transformed the way families live, learn, and work. Thus, it is crucial to examine the experiences of young children and their families with AI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The study involved 73 five-year-old children from low-income, rural areas in South Korea, along with their teachers and parents. Children were divided into two groups: one exposed to an AI robot for 6 months and the other for 18 months. Data were collected through assessments of children's AI literacy and computational thinking, as well as interviews with eight teachers and eight parents.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The initial exposure group showed an improvement on the perceived intelligence dimension of AI literacy. Children in both initial exposure and extended exposure groups increased computational thinking over time. Teachers and parents noted that interactions with AI robots promoted cognitive skills and knowledge, such as problem-solving and technology familiarity. However, they also expressed concerns about the potential impacts of AI on holistic development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study highlights the developmental benefits and challenges of AI robots in early childhood education for underserved populations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Such efforts are essential to ensure equitable opportunities for all children, contributing to school success and preparing them for a future society where AI is prevalent in daily life.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1354-1375"},"PeriodicalIF":1.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging natural language processing to deepen understanding of parent–child interaction processes and language development","authors":"Wonkyung Jang,&nbsp;Diane Horm,&nbsp;Kyong-Ah Kwon,&nbsp;Kun Lu,&nbsp;Ryan Kasak,&nbsp;Ji Hwan Park","doi":"10.1111/fare.13198","DOIUrl":"https://doi.org/10.1111/fare.13198","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The current study aimed to analyze the fine-grained processes of parent–child interactions using modern machine learning and natural language processing algorithms.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Although many studies have used audio samples to predict children's language development, they have primarily focused on the frequency of language exposure rather than complex semantic relationships and the effects of context and learner variability.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Method&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study examined whether children exhibit greater syntactic development when parents engage in semantically relevant conversations during mealtime and toy play, using semantic network algorithms. Additionally, it investigated gender differences in conversational topics during toy play using topic modeling and word embedding algorithms. Data from the Home-School Study of Language and Literacy Development Corpus, focusing on a subset of 62 children, were analyzed.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Key findings revealed the clustering coefficient for semantic networks during mealtime was positively associated with children's syntactic development. Furthermore, Bidirectional Encoder Representations from Transformers and Word2Vec algorithms showed that boys and girls had different conversational focuses during toy play, with boys gravitating toward action verbs and physical activities, and girls toward social and relational themes.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Implications&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;These findings highlight the importance of incorporating semantically relevant conversations into daily routines to support children's syntactic development. They also emphasize the need for tailored interventions that consider context and gender differences in parent–child interactions. Future research should leverage artificial intelligence (AI)-driven language processing to refine interventions, strengthen parent engagement, and inform policies that promote equitable early language learning.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Semantically relevant conversations during mealtime significantly enhanced children's syntactic development, and gender differences in conversational content during toy play reflected distinct linguistic focuses. This study confirms and extends existing literature, suggesting that AI-driven measures could provide a more granular and nuanced understanding of children's language learning environments.&lt;/p&gt;\u0000 ","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1146-1173"},"PeriodicalIF":1.7,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring fathers' psychological well-being using supervised machine learning random forest analysis","authors":"Kwangman Ko,&nbsp;Matthew R. Rodriguez","doi":"10.1111/fare.13191","DOIUrl":"https://doi.org/10.1111/fare.13191","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Guided by a family systems theoretical framework, the study reported herein explores the utility of using supervised machine learning random forest regression for understanding fathers' psychological well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although fathers' psychological well-being has not received much attention, understanding how familial factors contribute to fathers' mental health will benefit fathers themselves as well as their families, given the interdependence of the family system. Supervised machine learning using a random forest regression can be useful for identifying the hierarchical relationships between factors that shape fathers' psychological well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The study includes 277 U.S. fathers with at least one preschool-aged child as participants. Study variables include fathers' psychological well-being, father involvement, parental competency, parent–child relationships, coparenting relationship quality, work and family conflict, and fathers' demographic information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The supervised machine learning model was trained using a random forest regression. After tuning, the random forest regression with the training data identified parent–child relationship conflict as the most important predictor, followed by father involvement, coparenting relationships, work and family conflict, and parental competency (<i>R</i>² = .62).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This research shows the benefits of taking a supervised machine learning random forest statistical approach to increasing understanding of the complexity of factors related to fathers' psychological well-being.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>To support fathers' psychological well-being, practitioners and family educators may consider addressing familial factors such as parent–child relationship conflict, father involvement, and coparenting relationship quality within a family.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1198-1215"},"PeriodicalIF":1.7,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Everyday Family Life: Issues, applications, and implications","authors":"Brandon T. McDaniel,&nbsp;Fayika F. Nova,&nbsp;Jessica A. Pater","doi":"10.1111/fare.13197","DOIUrl":"https://doi.org/10.1111/fare.13197","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) has redefined how people live and interact with technology. While AI continues to evolve, its integration into everyday life has brought both benefits and challenges. One area that may not have received enough attention is the exploration of AI's impact on interpersonal relationships and family dynamics and processes. Our hope for this multidisciplinary special issue of <i>Family Relations</i> is to provide an opportunity for reflection on the intersection of AI—broadly defined as any technological innovation that simulates human intelligence—and family dynamics as well as the study of or intervention in family dynamics. Although the research studies, reviews, and commentaries in this special issue focus on a variety of subtopics within family dynamics, we feel they can be centered around the following distinct but overlapping themes: (1) how AI is integrated within families and potential impacts on family dynamics and relationships, (2) how AI can support research and assist in identifying patterns or processes within families and relationships, (3) how AI can assist families and children in developing skills relevant to their lives and relationships, (4) how AI tools used by or with families are developed and assessed, and (5) policy and ethical considerations for AI use and family dynamics.</p>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1049-1055"},"PeriodicalIF":1.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging Ideas. A brief commentary on human–AI attachment and possible impacts on family dynamics","authors":"Brandon T. McDaniel,&nbsp;Amanda Coupe,&nbsp;Allison Weston,&nbsp;Jessica A. Pater","doi":"10.1111/fare.13188","DOIUrl":"https://doi.org/10.1111/fare.13188","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>In this brief commentary article, we outline an emerging idea that, as conversational artificial intelligence (CAI) becomes a part of an individual's environment and interacts with them, their attachment system may become activated, potentially leading to behaviors—such as seeking out the CAI to feel safe in times of stress—that have typically been reserved for human-to-human attachment relationships. We term this <i>attachment-like behavior</i>, but future work must determine if these behaviors are driven by a human–AI attachment or something else entirely.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>CAI is an emerging technical advancement that is the cornerstone of many everyday tools (e.g., smartphone apps, online chatbots, smart speakers). With the advancement in generative and conversational AI, device affordances and technical systems are increasingly complex. For example, generative AI has allowed for more personalization, human-like dialogue and interaction, and the interpretation and generation of human emotions. Indeed, AI tools increasingly have the ability to mimic human caring—learning from past interactions with the individual and appearing to be emotionally available and comforting in times of need. Humans instinctually have attachment-related needs for comfort and emotional security, and therefore, as individuals begin to feel their attachment-related needs are met by CAI, they may begin to seek out the CAI as a source of safety or to comfort their distress. This leads to questions of whether human–AI attachment is truly possible and, if so, what this attachment might mean for family dynamics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1072-1079"},"PeriodicalIF":1.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Family and child characteristics in reading achievement milestones using machine-learning-based survival analysis","authors":"Wonkyung Jang,&nbsp;Kwangman Ko,&nbsp;Seulki Ku,&nbsp;Kyong-Ah Kwon","doi":"10.1111/fare.13174","DOIUrl":"https://doi.org/10.1111/fare.13174","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aimed to identify early reading achievers and uncover family and child factors that mitigate reading skill disparities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Literacy standards guide educational policy to prevent literacy issues in at-risk children. Many studies lack accurate methods to measure reading milestones, relying on static approaches that miss dynamic longitudinal processes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>This study used machine-learning-based survival analysis on Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 (ECLS-K: 2011) data to analyze children's time to reach reading milestones, examining how family structure, socioeconomic status, gender, and behavioral problems relate to reading achievements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Being female, from a higher-income family, and not exhibiting behavioral problems increased the likelihood of surpassing reading milestones. Higher socioeconomic status had a stronger positive relation with reading achievement in two-parent families. Externalizing behaviors had a stronger negative relation with reading achievement in girls than boys. The survival tree analysis showed children from two-parent families with incomes at or above 200% of the poverty threshold reached reading milestones earlier. Among these children, those with lower externalizing behaviors achieved them the earliest.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study supports the family systems theory and the bioecological model, indicating family and child factors, and their interplay, relate to children's reading achievement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Machine-learning-based survival analysis enhances the assessment of reading milestones, facilitating early diagnosis, targeted interventions, and effective family policies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1174-1197"},"PeriodicalIF":1.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and opportunities in using interpretable AI to develop relationship interventions","authors":"Daniel J. Puhlman,&nbsp;Chaofan Chen","doi":"10.1111/fare.13172","DOIUrl":"https://doi.org/10.1111/fare.13172","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Although still in its infancy, research shows promise that artificial intelligence (AI) models can be integrated into relationship interventions, and the potential benefits are substantial. This article articulates the challenges and opportunities for developing relationship interventions that integrate AI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>After defining AI and differentiating machine learning from deep learning, we review the key concepts and strategies related to AI, specifically natural language processing, interpretability, and human-in-the-loop strategies, as key approaches needed to develop AI models that can be integrated into interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We explore how AI is currently integrated into family life and explore the literature that has served as the foundation for further integrating AI into interventions. The use of AI in therapy contexts is examined, and we identify key ethical challenges that need to be addressed as this technology develops.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We examine the key challenges and opportunities for using AI, specifically focusing on four key areas: diagnosis of relationship problems, providing autonomous treatment, predicting successful treatment outcomes (prognosis), and using biomarkers to monitor client reactions. Opportunities explored include the development of data-efficient AI training methods, creating interpretable AI models focused on relationships, the integration of clinical expertise during model development, and combining biomarker data with other modalities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Despite the obstacles, integrating AI into interventions has the potential to provide families with personalized support to strengthen bonds and overcome relational challenges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>This emerging intersection of AI and family science can pioneer innovative solutions for diverse relationship needs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1299-1322"},"PeriodicalIF":1.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the efficacy of ChatGPT in understanding and identifying intimate partner violence","authors":"Ying Zhang,&nbsp;Jun Fang,&nbsp;Xiaochen Luo,&nbsp;Danielle Lindsay,&nbsp;Nabiha Madre,&nbsp;Jean Paredes,&nbsp;Alan Penna,&nbsp;Emma Melley,&nbsp;Tatum Garcia","doi":"10.1111/fare.13176","DOIUrl":"https://doi.org/10.1111/fare.13176","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aims to examine the efficiency and consistency of ChatGPT in identifying intimate partner violence (IPV) and the frequency of emotional and informational support ChatGPT provided.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The integration of artificial intelligence–based conversational large language models, such as ChatGPT, in understanding relationship dynamics has sparked both interest and debate within the scientific community. This tool could be valuable in offering immediate, personalized responses to questions about relationships, including those involving conflicts and violence.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We extracted 500 posts involving IPV and 80 posts involving nonviolent family tension from online IPV help-seeking forums as prompts for ChatGPT (Version 3.5). We coded ChatGPT's responses and examined their congruence and consistency in identifying IPV compared to human experts. We also examined incidents where ChatGPT misjudged. Lastly, we assessed the presence of informational and emotional support in ChatGPT's responses to prompts involving IPV.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ChatGPT-3.5 was able to identify cases involving IPV (physical violence, psychological violence, and controlling behavior) correctly in 91.2% of the cases. Misjudgment mostly occurred due to community policies or nuanced context information. ChatGPT consistently provided emotional support and informational support to users who presented IPV-related inquiries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>ChatGPT-3.5 could reach a relatively high accuracy and consistency in identifying IPV and can provide supportive responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>ChatGPT can serve as an initial resource for individuals and family members seeking help with IPV, offering immediate, empathetic, and informational support. However, improvements are needed to address its limitations in handling nuanced cases and to ensure ethical use and user safety.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1233-1249"},"PeriodicalIF":1.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A qualitative exploration of parents and their children's uses and gratifications of ChatGPT","authors":"Shirley Zhang,&nbsp;Jennica Li,&nbsp;Bengisu Cagiltay,&nbsp;Heather Kirkorian,&nbsp;Bilge Mutlu,&nbsp;Kassem Fawaz","doi":"10.1111/fare.13171","DOIUrl":"https://doi.org/10.1111/fare.13171","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This Emerging Ideas report explores families' (parents and their children) uses and gratification for ChatGPT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative artificial intelligence–based conversational agents, such as ChatGPT, can be used to accomplish a variety of tasks, yet little is known about how and why parents and their children may use these technologies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted semistructured qualitative and exploratory interviews with 12 U.S.-based families that had experience sharing a ChatGPT account. Families were recruited using social media advertisements, and at least one child and one parent joined the interview. We asked families about what they used ChatGPT for and why they used the platform.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Families reported four main motivators for using ChatGPT: (a) information seeking, (b) enhancing productivity, (c) entertainment, and (d) social bonding. Potential barriers to use included concerns about (a) ChatGPT's credibility and capabilities, (b) being less familiar with using ChatGPT, (c) the platform's ethical implications, and (d) possible privacy risks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Families use ChatGPT for various purposes, but their uses and gratifications sometimes may differ depending on their perceptions of and experiences with the platform.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Our findings suggest that with some improvements, ChatGPT has the potential to be a useful tool for both individual and shared use in families.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1056-1071"},"PeriodicalIF":1.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/fare.13171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the capability of generative artificial intelligence for parent and caregiver information seeking","authors":"YaeBin Kim,&nbsp;Silvia L. Vilches,&nbsp;Sidney Shapiro,&nbsp;Anne Clarkson","doi":"10.1111/fare.13167","DOIUrl":"https://doi.org/10.1111/fare.13167","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study explored the quality of generative artificial intelligence (AI) responses to common parenting questions across diverse sources of digitally available information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The recent rise of generative AI, such as ChatGPT and other large language models (LLMs), which generate answers by synthesizing publicly available information, raises questions about the quality of digital responses and the effect on parenting and outcomes for children.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We hypothesized that querying a professionally prepared parenting newsletter would have higher quality responses than an LLM. We explored this by running 11 tests with five common parenting and caregiving topics about young children across controlled and open data sources. We analyzed three Cs (correctness, clarity, and connection), reliability (artificiality, credibility, and citation quality), and readability to assess the quality of LLM responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ChatGPT largely provided correct and clear answers although citations were frequently absent and inaccurate. LLM responses often lacked emphasis on parent–child connection and developmental context, and reading level difficulty increased steeply.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Generative AI offers reasonably good answers to general parenting questions. However, parents and caregivers need to contextualize the information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Topical experts may help meet nuanced parenting needs with cultural relevance and plain language, but AI can be useful for summarizing open-access content.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48206,"journal":{"name":"Family Relations","volume":"74 3","pages":"1266-1284"},"PeriodicalIF":1.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}