AI时代背景下，家庭教育决策的最优模型

Family's academic cognition influences the family's academic concept, rearing fashion, and academic participation. It is no longer solely associated to kid's bodily and intellectual development; however, additionally associated to household concord and social progress. With the development of the times, the complicated traits of training proceed to pose new challenges to parents. Exploring the composition and operation mechanism of family training decision-making cognition is envisioned to stop up the key to promote parents' orderly coaching participation and home university cooperation. However, the associated lookup of usual cognitive mannequin has terrible steadiness and prediction charge in focus results. This paper constructs a cognitive model of family training decision-making principally based totally on neural network. Through the assessment of relevant data, they have an effect of the cognitive model of family coaching decision-making mainly based totally on neural neighborhood evaluated from the accuracy, root suggest rectangular error RMSE, and AUC curve. The experimental effects exhibit that the prediction accuracy of the cognitive mannequin of household training decision-making primarily based on neural community is 15% greater than that of the standard model, and the cognitive balance of the mannequin is 8.2%. This paper demonstrates the effectiveness, feasibility, and practicability of the mannequin in realistic teaching.

This study aims to explore the role of parents in guiding early childhood interactions with artificial intelligence (AI)-based technology within the framework of digital parenting. Using a descriptive qualitative approach, data were collected through in-depth interviews, participant observation, and documentation involving ten parents of children aged 4–6. The findings indicate that active mediation was the most commonly applied strategy (8 out of 10 parents), though restrictive mediation remains dominant in limiting access and screen time. Co-using was the least practiced due to time constraints and low digital literacy. A key finding highlights parents' limited understanding of AI principles, which hinders reflective and educational engagement during children’s AI use. This study introduces a novel contribution by integrating parental AI literacy into the parental mediation model and proposes a theoretical modification of digital parenting. Practical implications include the need for parental AI literacy modules and child technology design that fosters active family involvement. These findings underscore the shift in digital parenting from mere technical control toward reflective guidance grounded in AI literacy. Keywords: artificial intelligence, digital parenting, early childhood

… We look at strategies for parents with an aim to create a balanced digital environment, as … of parents actively monitoring their children with respect to interactions made with AI-driven …

Artificial intelligence (AI) and digital technologies are increasingly influencing family life, parenting practices, and pediatric health and mental health services. Applications include AI-enabled parenting support tools, mobile health interventions, digital therapeutics, remote monitoring systems, and algorithm-based platforms for neurodevelopmental and psychiatric assessment. While these technologies offer opportunities to enhance parental support, personalize care, and improve access to services, they also raise concerns related to ethics, equity, data privacy, and parent–child relationships. This narrative review offers an integrative overview of AI and digital technologies in family and parenting contexts, focusing on key domains such as AI-supported parenting interventions, digital health literacy, pediatric and perinatal care, neurodevelopmental and mental health assessment, digital media use, and family communication. Reported benefits, emerging risks, and unintended consequences are discussed with particular attention to parental roles, family-centered design, and implementation challenges. Overall, evidence suggests that AI-enabled tools are most effective when they are transparent, co-designed with parents and clinicians, and integrated into existing health and social care systems. Nonetheless, gaps remain regarding long-term outcomes, equitable access, ethical governance, and sustainable implementation. AI in digital parenting should therefore be viewed as a complementary resource that augments, rather than replaces, parental judgment and professional care.

… of AI-driven educational tools on parental involvement and communication between parents … utilization trends of AI-enabled smart parenting strategies among Palestinian households. It …

Parenting in the modern world presents unique challenges as families navigate the complexities of digital life while striving to uphold moral, ethical, and faith-based values. With the rapid advancement of Artificial Intelligence (AI), there is an unprecedented opportunity to integrate intelligent systems into parental guidance, offering tailored, accessible, and spiritually aligned support. This chapter explores the development of a faith-centered AI framework for parental guidance, focusing on how AI can be designed to enhance parenting strategies while preserving religious, ethical, and cultural values. By combining cutting-edge AI capabilities—such as Natural Language Processing (NLP), machine learning, and personalized recommendations—with faith-based teachings, this framework seeks to provide holistic, value-driven parenting assistance.

Introduction: This perspective article reflects on how innovative technologies, including artificial intelligence (AI) systems like smart voice agents and chatbots, may transform family dynamics and communication. Despite the extensive research on AI’s impact in mental healthcare and education, its influence on family systems remains underexplored. This perspective article aims to draw attention to the possible positive and detrimental effects of using AI in families, highlighting the necessity of fostering AI literacy in this setting. Areas covered: The article delves into integrating AI within family therapy models, focusing on how AI redefines family boundaries, roles, communication, rituals, and narrative creation. It explores AI’s potential to enhance parent training programs and its impact on children’s social and cognitive development. Expert opinion: AI presents both opportunities and challenges for family systems. It can enhance communication, support role negotiation, and promote family cohesion, but it also raises ethical and privacy concerns. The balance between utilizing AI to support family values and avoiding the detrimental effects of over-reliance is crucial. Conclusion: Integrating AI into family systems offers significant potential benefits, but it must be managed carefully to ensure it aligns with family values and strengthens family bonds. Fostering AI literacy within families is essential to navigate the complexities and harness the advantages of AI technologies.

Background: Information and communication technology and AI have become integral parts of the education curriculum and systems in Ghana, requiring all stakeholders to participate in teaching and learning tasks to ensure sustainability and success. Objective: This article examines how parents with limited ICT knowledge can meaningfully support children who show exceptional ability in artificial intelligence, children whom this study terms 'AI prodigies.' Drawing on family AI literacy theory, digital parenting research, and parental mediation frameworks, the study investigates the relational, motivational, and metacognitive strategies available to low-ICT parents. Methods: A sequential explanatory mixed-methods design was employed involving 60 families with children aged 8–14 who demonstrated advanced AI-related skills. Data were collected through a validated Parental ICT/AI Literacy Survey (DCAT + AI Literacy Supplement), semi-structured interviews (60-90 min), structured home observations (two visits per qualitative subsample family), and children's self-reports. An observing-truth protocol, triangulating self-report, behavioural trace logs, and independent observations, was applied to reduce social desirability bias. Quantitative data were analysed using structural equation modelling (SEM) in Mplus 8.10; qualitative data underwent reflexive thematic analysis. Results: Three key findings are anticipated: (a) effective low-tech strategies, including Socratic questioning, modelling learning behaviours, and motivational scaffolding, do not require high ICT competence; (b) joint AI exploration (participatory learning mediation) strengthens both shared family AI literacy and parental agency; and (c) tensions around ethics, screen time, and parental techno-confidence can be mitigated through purposefully designed AI-based supports. Conclusion: A design framework for family-centred AI literacy resources tailored to low-ICT parents is proposed, comprising Conversational Agent Supports, Parent-facing Micro-Interventions, and Child-centred AI Tools with Caregiver Guidance. The framework carries implications for educators, designers, and policymakers committed to equitable AI talent development.

Generative AI is increasingly present in children’s learning environments, yet little is known about how families navigate this technology in middle childhood (ages 7–13), when parental guidance remains strong but children seek independence. Drawing on self-directed learning (SDL), we explore how parents in our exploratory sample perceived children’s emerging self-directness and agency. Through focus groups with 13 parent–child pairs, we examine parents’ views on children’s AI literacy development, readiness factors, and mediation strategies. Parents described emergent pathways shaped by screen time, self-directness, and knowledge growth. They often confined AI to learning-only contexts, positioning it as a tutor while overlooking non-learning uses and risks such as privacy and infrastructural embedding. Many acknowledged limited AI literacy and turned to joint engagement as opportunities for co-learning. Our findings surface possible parental pathways of children’s AI literacy, highlight gaps between pragmatic expectations and critical literacies, and offer situated design considerations for AI systems that scaffold SDL while balancing oversight with autonomy.

… As the first empirical mapping of Indonesian parental … parents as co-navigators of AIdriven educational futures. … a set of policy strategies proposed by parents to facilitate the integration of …

… strategies used to address particular situations or goals (Meuronen et al., 2024). Parents employ various strategies for digital parenting, … platforms, and AI-driven applications) an initial …

This paper explores the growing use of artificial intelligence (AI) in early childhood education (ECE), emphasizing the crucial yet often disregarded role of parents and families. With AI technologies increasingly integrated into educational environments, including households, understanding parental AI literacy is essential for shaping children's learning experiences and developmental outcomes. The review commences by analyzing the current AI applications in ECE, classifying them into six main categories: interactive AI, generative AI, AI prediction, AI literacy, AI-driven personalized learning, and affective AI. Despite the various advantages these advancements offer, they also pose challenges such as potentially worsening digital disparities, concerns regarding data privacy, and ethical dilemmas. The concept of parental mediation in the context of AI technologies is central to the discussion. The review underscores the influence of parents' socioeconomic status, cultural background, age, and educational level on their mediation strategies, which may vary from restrictive to supportive approaches. The effectiveness of parental mediation is intricately linked to parents' AI literacy levels, underscoring the importance of improving parental knowledge and skills in this area. Then this paper presents a novel conceptual framework for parental AI literacy, which encompasses three core dimensions: AI knowledge (evaluation), AI skills (use and guidance), and AI attitudes (understanding). This framework establishes a theoretical foundation for future research by providing a comprehensive model designed explicitly for the family context within early childhood education (ECE). This review synthesizes existing literature and proposes new conceptual models, thereby offering valuable insights to guide policy development, educational practices, and parental training within the rapidly evolving landscape of AI in ECE. It emphasizes the significance of family-centered approaches in leveraging the advantages of AI while effectively mitigating potential risks. Furthermore, it outlines directions for future research and innovation in this critical field.

This chapter explores the development and impact of Teddy AI, a generative AI-powered application designed to revolutionise early learning experiences and parent–child engagement. Teddy AI is an interactive, intelligent companion that teaches, engages, and adapts to the unique learning needs of each child. In this way, it delivers personalised educational content in a playful and natural manner. More than just a digital tutor, it also serves as a bridge between the child’s learning journey and their caregivers and guardians, providing real-time feedback, learning progress insights, and areas requiring support. What sets Teddy AI apart is its ability to subtly guide educational practice without making activities feel forced, preserving a sense of play and exploration for the child. In fact, parents can also prompt Teddy AI to introduce new concepts or reinforce specific skills without the child knowing or feeling pressured, thereby creating a seamless blend of structured and informal learning. The chapter discusses this concept in detail and also backs this up with findings from recent research conducted by the authors using Teddy AI. The research showed strong openness among parents to trust AI-powered educational tools. The majority believed such tools could accelerate learning through tailored content and authentic information. By illustrating the practical applications and ethical considerations of using AI in home-based learning environments, this chapter underscores the potential of AI to make education more equitable, effective, and family-centred.

… parents on the implications of AI in education and the necessity of adapting parenting strategies … engagement, parents can help their children make the most of AI-driven education. This …

… of parents throughout pregnancy, section 4 highlight the impact of smart technology (IoT) in … the overview on proposal for quality parenting with smart technologies and section 7 and 8 …

… The following sections detail how each phase unfolded in our study and contributed to the development of novel smart home IoT features that support energy-conscious parenting …

Introduction: The continuing care period following residential substance use treatment is a time when adolescents are at especially high risk for relapse, yet few families engage in traditional office-based care. Parent SMART (Substance Misuse among Adolescents in Residential Treatment) is a multi-component continuing care intervention for parents that combines three digital health technologies – an “off the shelf” online parenting program, daily phone notifications, and an online parent networking forum – with support from a parent coach. The current study solicited both qualitative and quantitative user feedback about Parent SMART to ensure responsivity to user preferences, refinement, and continuous improvement of the intervention. Methods: Exit interviews were conducted with 30 parents who received Parent SMART, which includes (1) a parent networking forum; (2) daily text messages reminders of skills, (3) an “off-the-shelf” online parenting program; and (4) in-person or telehealth parent coaching sessions. The study collected qualitative feedback using semi-structured interviews and obtained quantitative feedback via a series of ratings of each Parent SMART component on 5-point Likert scale administered at each follow-up assessment. Results: Quantitative feedback suggest that parents rated all four elements of Parent SMART as easy to use. Qualitative feedback revealed that parents valued several aspects of Parent SMART including the brevity and structure of the intervention elements, the reminders to use parenting skills, and the sense of social connectedness fostered by different components. Recommended refinements included a number of strategies to enhance personalization and ease of navigation. Conclusions: Parent feedback informed enhancements to the Parent SMART intervention prior to implementation in a larger, ongoing pragmatic effectiveness trial. The current study serves as a model for applying a staged person-centered approach and eliciting both quantitative and qualitative feedback to refine digital health technologies.

Background: Prenatal diagnosis of life-limiting fetal conditions often leads to counseling focused primarily on therapeutic abortion. Perinatal hospice has emerged as an alternative model of care for families who choose to continue the pregnancy. This paper has two primary aims. First, it discusses structured perinatal hospice programs and their role in supporting parental decision-making after such diagnoses, with attention to ethical and emotional complexities. Second, the paper introduces NOVA-L (Navigating Options & Vital Assistance for Life-limiting conditions), a conceptual Decision Support System (DSS) designed to complement perinatal hospice care. Methods: The paper provides a conceptual and descriptive analysis of the Comfort Care clinical model. It also outlines the proposed architecture of NOVA-L. DSSs combine clinical guidelines, research data, and outcome registries on digital platforms, providing evidence-based information and AI-supported analytical tools. Their potential adaptation to perinatal hospice care is explored. Results: The Comfort Care model involves interdisciplinary counseling, structured communication, and psychosocial support to facilitate clarification of parental values and care pathways. NOVA-L is presented as a complementary tool that may enhance transparency in risk evaluation and option comparison through accessible interfaces under professional supervision. Conclusions: Structured perinatal hospice programs may enhance clarity and compassion in decision-making. The conceptual integration of AI-supported DSS tools, such as NOVA-L, could strengthen ethically grounded, emotionally sensitive parental support.

This study aims to develop and identify the effect of SMART-P training on parenting knowledge and children's cognitive development. This research is included as Mix Method research with an ADDIE development model and experimental quantitative research approach. The research method used is explanatory research with a non-equivalent control group design research involving the control and experimental classes. This research is carried out for three weeks, with the stages covering: analysis, design, development, research instrument development, implementation, and evaluation. The population in this study are 714 parents with children aged four years from posyandu/KB in five sub-districts of Malang City, with a research sample of 150 parents with children aged four years and six experts. Data collection techniques in this study used questionnaires and observation checklists. Data analysis in this study used descriptive percentage analysis and SPSS analysis. The results show that 1) SMART-P application is declared valid and acceptable to be implemented as a training medium, 2) a significant effect of parenting knowledge in parents before and after being given parenting training using SMART-P is identified, 3) a significant effect on the development cognitive development of children before and after parents re given care using SMART-P application is identified, 4) a significant difference in the effect of parenting knowledge on parents between the control group and the experimental group is shown, and 5) a significant difference in the effect on children's cognitive development between the control group and the experimental group is slightly identified. Therefore, parenting research using SMART-P needs to be carried out continuously so that parenting knowledge and children's cognitive development can be maximized

Discovering and addressing skill gaps is key in today’s knowledge-driven economy, enabling economic growth while reducing inequalities. Here, I present a novel approach using Generative AI for resume analysis, skill extraction, and personalized recommendations based on cutting-edge research directions. Utilizing high-end natural language processing and data analytics, this method integrates person-specific skills with evolving industry phenomena.The method is designed to combat urgent issues in education and employment, providing key data on skill gaps and improvement routes. Using practical case studies, it illustrates how bespoke guidance can empower individuals in their pursuit of meaningful careers, contributing to sustainable development. This aligns with SDGs 4 (Quality Education), 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure) and 10 (Reduced Inequalities).The paper highlights the revolutionary role that AI can play in making career guidance available to all. Integrating resources for skill enhancement to provide access allows learners from varying backgrounds to gain knowledge from pioneering research and practice. Furthermore, the proposed system is also centered on lifelong learning, which prepares a future-ready workforce to ensure innovation and resilience in a fast-evolving global economy.The implications encourage the inclusion of generative artificial intelligence in educational systems and professional development to optimize human capital. This can minimize opportunity gaps, empower underserved communities, and increase global productivity. The research offers a practical pathway to advance SDGs and build sustainable futures by mapping the direct impact technology has on education, employment, and equity.

… of GenAI on skill development. This review contributes to the ongoing dialogue on AI's role in … research, and optimize GenAI's potential in preparing students for an AI-integrated world. …

In an era where technology plays a significant role in the lives of people of all ages, adapting to rapidly advancing technology is essential. Lifelong learning, which integrates formal, non-formal, and informal education, helps learners develop skills and abilities independently. Programming skills serve as an example of lifelong learning, requiring the development of technical skills such as programming, planning, and testing, as well as communication, teamwork, and systematic thinking. This research focuses on utilizing Generative Artificial Intelligence (Generative AI) to enhance programming skills through self-directed learning combined with interactive learning. The study was conducted with 21 students enrolled in the Higher Vocational Certificate (Diploma) program in Mechatronics and Robotics. The study applied the MAR model, which consists of three stages: The motivation stage, is designed to encourage learners to engage in activities that link prior knowledge or experiences with new knowledge, thereby fostering self-directed learning motivation, such as designing personalized learning activities based on individual interests; the Activity stage, which provides opportunities for learners to use Generative AI technology for self-directed learning according to their goals and plans, enabling them to understand concepts, apply knowledge in various situations, and track their learning progress; and the Reflection and Progress stage, focusing on reviewing, analyzing, and reflecting on learning outcomes, as well as exchanging ideas and skills with peers and receiving constructive feedback. These stages were designed based on theoretical foundations and a literature review. Furthermore, the research emphasized ethical considerations in AI use, such as data privacy and societal impacts, to promote high-quality, beneficial lifelong learning. The findings showed that before the learning process, the learner had no background in programming and had never used AI technology. However, after participating in the learning activities, students became more open to learning and responded positively to the self-designed activities, which aligned with their interests and skills related to their field of study. These findings suggest that this learning process, when implemented effectively, can be applied to lifelong learning for individuals of all genders and ages.

Student creativity in Indonesia remains a serious problem that requires special attention, with the 2022 PISA survey identifying only 5% of students achieving high creativity. Science learning is also still limited in integrating technology to develop creativity, even though this ability is a very important competency in the digital era. This study aims to develop Game-based Learning (GBL) with Generative AI (Gen AI) to improve the creativity of 5th grade elementary school students. The research method uses Research and Development (RnD) with the ADDIE model including five stages: analysis, design, development, implementation, and evaluation. The subjects of this study were 20 5th grade students of SDN Kutakembaran 1. GBL was developed with the ChatGPT-3.5 API to produce adaptive learning content. ChatGPT-3.5 works by analyzing student responses, automatically generating game scenarios, questions, and challenges tailored to the individual student's ability level. This development is enriched with West Javanese cultural elements such as folklore, traditions, and local contexts to increase the relevance of learning. The research instruments include expert validation (n=5), a creativity rubric that measures four indicators (fluency, flexibility, originality, elaboration) through problem-solving tasks, and observation of student engagement using a Likert scale that records active participation. The expert validation results showed a feasibility score of 77.50 (good category) with an Aiken's V coefficient of 0.813 (p<0.001). Implementation resulted in a significant increase in the creativity dimensions: fluency (23%, p<0.05), flexibility (31%, p<0.01), originality (28%, p<0.05), and elaboration (19%, p<0.05). Student engagement reached an average of 78.60 with 80% positive responses, while technology acceptability reached 83.45%. The culture-based GBL-GenAI media has proven effective in developing student creativity and making a significant contribution to innovation in science learning for the transformation of Indonesian basic education.

: As institutions increasingly use predictive algorithms to allocate scarce resources, scholars have warned that these algorithms may legitimize inequality. Although research has examined how elite discourses position algorithms as fair, we know less about how the public perceives them compared to traditional allocation methods. We implement a vignette-based survey experiment to measure perceptions of algorithmic allocation relative to common alternatives: administrative rules, lotteries, petitions from potential beneficiaries, and professional judgment. Focusing on the case of schools allocating scarce tutoring resources, our nationally representative survey of U.S. parents finds that parents view algorithms as fairer than traditional alternatives, especially lotteries. However, significant divides emerge along socioeconomic and political lines—lower socioeconomic status (SES) and conservative parents favor the personal knowledge held by counselors and parents, whereas higher SES and liberal parents prefer the impersonal logic of algorithms. We also find that, after reading about algorithmic bias, parental opposition to algorithms is strongest among those who are most directly disadvantaged. Overall, our findings map cleavages in attitudes that may influence the adoption and political sustainability of algorithmic allocation methods.

One of the long-term lessons from the school closures due to the global pandemic COVID 19, is that technology and parental engagement are the best levers to access education so as to bridge the achievement gap between socially disadvantaged children and their peers. However, using technology is not as simple as bringing equipment into the school and home and initiating its usage; these are just the first steps into a more complex and ambitious achievement of using technology as a catalyst for a shift toward new learning models in remote and hybrid settings. A theoretical framework based on the theory of acceptance and use of technology and social cognitive learning theory was used to analyse data from a survey completed by 4,600 parents from 19 countries during the national lockdowns in 2020. Regression models and thematic analysis of open-ended responses were employed to identify factors that contribute to parental acceptance and use of technology in support of their children’s learning. Our results show that parents are more engaged in children’s learning when well-structured technological tools are provided or suggested by schools, and when parents are socially influenced by the opinions of other parents, teachers, children, the general public, relatives, etc. Conversely, they are less engaged when they perceive the technological tools to be challenging and beyond their knowledge or skills. The study’s findings have practical implications for governments and school leaders, who need to be aware of the factors likely to determine the use of technology at home and take action to meet parents’ needs when using technology to support learning.

Abstract: School from Home is one of the Indonesian government's efforts to minimize the spread of the Covid-19. All teaching and learning activities have been transferred to various online learning applications, including elementary school students. The parents of these elementary school students have to operate multiple online learning applications so that their children can participate in distance learning activities. Researchers have already used the original Technology Acceptance Model (TAM) to determine the acceptance of online learning applications by parents of elementary school students as a means of distance learning. Researchers want to know the acceptance of online learning applications which are carried out in sudden conditions and without prior preparation but must be implemented. Researchers hope that the results of this study can capture the acceptance of parents of elementary students to School from Home during the Pandemic and can be used for evaluation and continuous improvement of the School from Home system. Referring to the literature with 22 questionnaire questions, the minimum number of respondents who must be obtained is 110 people, in this study 155 parents of elementary school students in Jabodetabek participated in filling out the questionnaire using an online questionnaire with a significance level of 5% and data processed using PLS-SEM 3.0 resulted in a strong relationship. There is no significant difference between Perceived Ease of Use and Attitude Toward Using. Then, a significant relationship was generated between Perceived Ease of Use on Perceived Usefulness, Perceived Usefulness on Attitude Toward Using, Perceived Usefulness on Behavioral Intention To Use, Attitude Toward Using on Behavioral Intention To Use. However, the results show a positive value Path Coefficient in Structural Model Result, so this research is consistent and according to TAM. Abstrak: School from Home menjadi salah satu upaya pemerintah Indonesia dalam mengurangi penyebaran virus Covid-19. Seluruh kegiatan belajar mengajar dialihkan dengan menggunakan berbagai aplikasi online learning, begitu pula dengan pendidikan untuk murid Sekolah Dasar. Para orang tua murid Sekolah Dasar ini, bagaimanapun juga harus mendampingi dan mengoperasikan berbagai aplikasi online learning agar putra putrinya dapat mengikuti kegiatan pembelajaran jarak jauh. Peneliti menggunakan Technology Acceptance Model (TAM) asli untuk mengetahui penerimaan aplikasi online learning oleh orang tua murid Sekolah Dasar ini sebagai sarana pembelajaran jarak jauh. Peneliti ingin mengetahui penerimaan aplikasi online learning yang dilaksanakan dalam kondisi mendadak dan tanpa persiapan sebelumnya namun harus dilaksanakan. Peneliti berharap hasil penelitian ini dapat menangkap penerimaan orang tua murid siswa SD terhadap School from Home selama Pandemi dan dapat digunakan untuk evaluasi maupun perbaikan berkelanjutan sistem School from Home. Mengacu pada literatur dengan 22 pertanyaan kuesioner, minimum responden yang harus didapatkan sebanyak 110 orang, dalam penelitian ini 155 orang tua murid SD di Jabodetabek berpartisipasi dalam pengisian kuesioner menggunakan online kuesioner dan tingkat signifikansi 5% dan data diolah menggunakan PLS-SEM 3.0 menghasilkan hubungan yang tidak signifikan antara Perceived Ease of Use terhadap Attitude Toward Using. Kemudian, dihasilkan hubungan yang signifikan antara Perceived Ease of Use terhadap Perceived Usefulness, Perceived Usefulness terhadap Attitude Toward Using, Perceived Usefulness terhadap Behavioral Intention To Use, Attitude Toward Using terhadap Behavioral Intention To Use. Namun, Hasil dari model penelitian ini tetap dikatakan konsisten dan sesuai dengan TAM, karena nilai Path Coefficient pada Structural Model Result yang dihasilkan seluruhnya bernilai positif.

… The Technology Acceptance Model (TAM) was adopted as the framework in the … model used in this study to examine parents’ acceptance of participation in the integration of technology …

… Purpose: This study aimed to investigate Chinese parents’ acceptance and support for their … IDLE) using the Technology Acceptance Model (TAM). Specifically, it examines how parents’ …

The rapid digitalization of education has redefined parental roles in children's learning, yet research has largely focused on children as technology users while overlooking parents' own active engagement. This study explores parental digital involvement among Chinese primary school students using Interpretative Phenomenological Analysis of 20 interviews with 10 parents, supplemented by participants' diaries and visual materials. The findings reveal that parents' roles are shifting from ‘supporters’ to ‘navigators’ within specific parental groups and identify four involvement patterns: integrative, selective, adaptive and minimal, which are shaped by digital capital and technology acceptance. The Integrated Framework of Parental Digital Involvement proposed in the study advances digital capital theory by highlighting child‐to‐parent knowledge transfer and refines the Technology Acceptance Model by incorporating concerns about screen overuse, learning effectiveness and health. The study enriches theoretical understanding and offers practical strategies for equitable and sustainable parental engagement in the digital era.

… Given that parents are the … parental perspectives on EVR can improve VR design for educational purposes. For knowledge, this study will leverage the technology acceptance model (…

This study explores parental perceptions and acceptance of intelligent control within AI-enhanced digital education platforms for K12 education through the Technology Acceptance Model (TAM). The study examines how intelligent control functionalities, such as real-time adaptation and feedback mechanisms, impact perceived value and parental intention to use the platform. Additionally, we investigate the moderating role of AI sophistication, analyzing how various levels of intelligent control affect perceived educational effectiveness and user acceptance. A survey of 354 parents revealed that personalization and intelligent control significantly influence perceived value, which in turn affects parental acceptance. Higher AI sophistication further enhances this relationship. The findings provide valuable insights for educators, platform developers, and policymakers aiming to optimize AI-driven education tools to improve learning outcomes and user satisfaction.

… Video analysis revealed Vovo’s advantages in pedagogical … structured literacy pedagogy into home-based conversational … AI triadic interactions to optimize AI in home literacy learning. …

We present Homeroom, a homeschooling platform that treats parents as reflective partners in collaboration with LLMs, integrates culturally responsive personalization for generating schooling materials, and supports the formation of small, trusted circles. Homeroom provides plan-then-generate story and curriculum creation, alignment, and comparison to local school standards, and resource sharing in invite-only groups. We conducted a summative usability study with 15 Muslim homeschooling parents in the Greater Toronto Area. Findings show that previewable, editable drafts preserve parental agency; values work best as revisable “soft constraints” integrated into the platform; and parents prefer private circles with clear lineage. Parents also requested lightweight infrastructure (e.g., rubric libraries, portfolio builders) to reduce paperwork. We discuss opportunities and challenges in positioning AI as a deliberative partner in family- and community-shaped pedagogy.