The Effect of AI Robot-Assisted Intergenerational Housework Activity on Young Children’s Competence in Household Chores
Ching‑FenLee1Email062888@yahoo.com.tw
Shain‑MayTang2✉Emailsmtang@mail.nou.edu.tw
1Department of Early Childhood Education and CareMinghsin University of Science and Technology304001Hsinchu CountyTaiwan
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Department of Living ScienceNational Open University247404New Taipei CityTaiwanChing‑Fen Lee 1 and Shain‑May Tang 2,*
1 Department of Early Childhood Education and Care, Minghsin University of Science and Technology, Hsinchu County 304001, Taiwan; 062888@yahoo.com.tw
2,*Department of Living Science, National Open University, New Taipei City 247404, Taiwan
* Correspondence: smtang@mail.nou.edu.tw
Abstract
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This study investigated the effectiveness of integrating artificial intelligence (AI) robot-assisted instruction into “intergenerational household activity” programs conducted in parent–child centers, with the objective of enhancing young children’s competence in household chores. A one-group pretest–posttest design was employed. Participants comprised 50 parent–child dyads recruited from four parent–child centers in Taoyuan City. The intervention, lasting four weeks, consisted of weekly 30-minute sessions. Instructional content was structured into four researcher-designed units of “Intergenerational Household Chores,” supported by AI robot-assisted materials. Data were collected using the Young Children’s Household Chores Preferences and Abilities Questionnaire, administered pre- and post-intervention, supplemented with session-specific parental feedback forms. The findings indicated that AI robot-assisted intergenerational household activities significantly improved young children’s competence in household chores, with particularly pronounced effects among children who initially demonstrated lower levels of competence. These results suggest that incorporating technological media into early childhood education programs can not only enhance children’s household task abilities but also strengthen parents’ skills and motivation in guiding children’s domestic participation.Keywords:
AI robot ‧ Intergenerational household activity ‧ Children’s competence in household chores‧Parent
child center
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1 Introduction
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Early childhood constitutes a critical period of human development [1]. During this stage, engagement in household chores provides opportunities for children to acquire daily living skills, foster independence [2], and promote holistic growth [3]. For instance, activities such as tidying toys, assisting with laundry, or participating in gardening increase physical activity levels within the home [4], thereby contributing to children’s health and developmental outcomes [5]. Empirical evidence has demonstrated that chore participation facilitates advancements in linguistic, motor, cognitive, social, and executive functioning domains, with long-term benefits extending into later life stages [3, 6]. Furthermore, household participation fosters children’s sense of responsibility, enhances socialization [4, 5], and cultivates problem-solving skills [8]. Engagement in such activities also strengthens parent–child relationships [9] and contributes to children’s overall life satisfaction [10].However, disparities exist in the types of chores children can competently perform. Lee and Tang [11] found that young children were generally more proficient in self-care tasks than in family-related chores. Their study highlighted that children’s health status influenced both parental attitudes toward chore participation and children’s task preferences, thereby shaping overall competence. These findings underscore the central role of parental involvement in supporting children’s participation in domestic tasks. From this perspective, the present study integrates an intergenerational program framework to emphasize the importance of parental engagement in household learning contexts.
Children’s competence in household tasks is strongly associated with their motivation to participate and to learn such skills [11]. A pressing question, therefore, is how to design engaging and developmentally appropriate curricula that sustain children’s participation. With rapid technological advancement, artificial intelligence (AI) and related media have become increasingly embedded in daily life. Technological media not only play an informal educational role [12] but also function as essential tools for lifelong learning and community participation [13]. Although some studies caution that premature or excessive exposure to technology may adversely affect children’s neurodevelopment [14, 15], other research highlights potential benefits, including facilitating knowledge exchange, promoting social interactions, and fostering innovation [16]. Specifically, digital and AI-based tools have been shown to stimulate children’s intrinsic motivation for learning [17], support multi-modal integration of knowledge, and improve overall learning outcomes [18]. For example, Adams [19] demonstrated that technology can enhance both cognitive and social competencies while also strengthening communication between educators and families. Similarly, Zhou et al. [20] reported that computer-assisted story creation improved expressive language skills among children with delayed development.
The application of AI in early childhood education is theoretically supported by the Cognitive Theory of Multimedia Learning (CTML) [22]. CTML posits that learning occurs through dual processing channels constrained by limited cognitive resources, and highlights the principle of signaling, wherein verbal and pictorial cues guide learners’ attention to essential information. In the context of household tasks, AI robots can provide multimodal prompts that scaffold children’s understanding of critical steps and gestures. Furthermore, AI robots’ use of conversational and personalized communication fosters cognitive engagement and enhances motivation, which in turn sustains children’s persistence in task performance. Previous empirical research further corroborates these mechanisms. Lee and Tang [23], for example, demonstrated that the integration of AI robots in preschool settings significantly enhanced children’s competence in household chores, outperforming traditional teacher-led instruction in some domains.
Intergenerational learning represents another important framework for situating AI robot-assisted activities. Studies suggest that AI robots’ user-friendly design, including appealing voices and appearances, not only engages children but also reduces older adults’ anxiety toward technology [24]. Research has shown that integrating robots into intergenerational activities alleviates depressive symptoms among older adults while simultaneously promoting children’s sensory integration abilities [24]. Despite these promising findings, the extent to which AI robot-assisted intergenerational household activities improve young children’s chore competence remains underexplored. The present study therefore aims to investigate the potential of combining AI robot-assisted instruction with intergenerational household activities to promote children’s household competence.
In addition, intergenerational participation is increasingly salient given the diversification of family structures [25]. In this study, “intergenerational” encompasses both parent–child and grandparent–grandchild interactions. Prior research indicates that intergenerational learning fosters mutual support, reciprocal knowledge exchange, and cooperative caregiving relationships [26]. Empirical evidence highlights that such programs benefit both generations: older adults experience improvements in psychological well-being, cognitive functioning, and self-confidence [27, 28], while young children cultivate empathy, independence, and social competence [29, 30]. In light of aging populations and declining fertility rates, governments have advocated for “intergenerational inclusion” in early childhood and community-based education programs. Parent–child centers, in particular, serve as crucial platforms for facilitating such exchanges.
This study was conducted in parent–child centers in Taoyuan City, which, relative to other municipalities, has maintained higher birth rates in recent years. In alignment with Taiwan’s national childcare policy (“state-supported childcare before age six”), Taoyuan has established a robust network of parent–child centers that provide developmental support, parenting education, and safe spaces for family interaction. These centers emphasize both parent involvement and intergenerational co-learning, offering play-based curricula, family workshops, and community outreach programs. Against this policy and institutional backdrop, the present study examines the implementation of AI robot-assisted intergenerational household activities in Taoyuan’s parent–child centers, with the objective of evaluating their impact on young children’s household chore competence.
2. Methods
2.1 Research Design
This study employed a one-group pretest–posttest design to examine the effects of AI-robot–assisted instruction within intergenerational household activities conducted at parent–child centers.
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The primary aim was to evaluate improvements in young children’s competence in household chores as perceived by their caregivers. Participants’ performance was assessed at baseline (pretest) and following the intervention (posttest), thereby enabling the detection of changes attributable to the program (Table 1).The intervention was implemented over a four-week period, with weekly sessions lasting approximately 30 minutes. Each session incorporated an AI-robot–assisted instructional component, integrated with intergenerational household activity themes. The Children’s Competence in Household Chores Scale was administered both prior to and after the intervention to evaluate differences in young children’s competence in household chores.
Group | Pre-Test | Intervention | Post-Test | Difference (Post–Pre) |
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One-group design (n = 50) | O1 | X | O2 | O2 – O1 = Δ |
2.2 Sample
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The study sample comprised 50 parent–child dyads recruited from four parent–child centers in Taoyuan City. Each dyad included one young child (aged 4–6 years) and one adult caregiver (parent or grandparent). Recruitment was conducted through centers hosting the “Intergenerational Household Chores” program (Table 2).A
Prior to data collection, the study protocol underwent review and approval by the Institutional Review Board (IRB). Following approval, the research team provided detailed explanations to participating centers and families regarding the study’s objectives, procedures, anticipated outcomes, and ethical considerations. A
Written informed consent was obtained from both the centers and all participating families before the commencement of data collection.Parent–Child Center | Number of Families |
|---|---|
OO Center | 12 |
XX Center | 15 |
ZZ Center | 9 |
WW Center | 14 |
Total | 50 |
2.3 Data Collection
Data collection focused on assessing children’s household participation abilities before and after the intervention. Questionnaires were administered to children’s primary caregivers, who were considered the most reliable reporters due to their daily interactions with the children. A non-anonymous format was adopted to ensure accurate matching between pretest and posttest responses.
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To ensure procedural clarity, the principal investigator conducted briefing sessions with directors of the participating centers. These sessions outlined the research objectives, activity content, and administrative responsibilities of the research team. The research team was responsible for conducting intervention activities, as well as administering and collecting questionnaires on-site. A
This process ensured transparency and obtained formal approval from each center prior to implementation.2.4 Instruments
The principal measurement instrument was adapted from the Children’s Preferences and Abilities in Household Chores Scale developed by Lee and Tang [23]. Items were revised to suit the study context and were subjected to expert review to establish content validity. The finalized instrument was used in both pre- and posttests. In addition, four activity-specific feedback forms were employed to assess children’s willingness to engage in household chores following each session.
2.4.1 Young Children’s Competence in Household Chores Survey
The study variables and measurement approaches are summarized below:
1. Children’s Health Status
Assessed using the item “child’s physical health condition” in the demographic section of the pretest questionnaire. Responses were rated on a five-point Likert scale (1 = very unhealthy, 5 = very healthy). Higher scores reflected better overall health.
2. Willingness to Participate in Household Chores
Evaluated through caregiver-completed feedback forms after each of the four sessions. For example, following Session 1 (It’s Mealtime), caregivers rated the extent of improvement in the child’s willingness to participate in mealtime-related chores. Items were scored on a five-point Likert scale (1 = no improvement, 5 = great improvement).
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Scores were aggregated across sessions, with higher totals indicating stronger willingness to participate.3. Pretest/Posttest Household Chore Competence
Measured using 13 items covering common household tasks (e.g., putting toys away, folding clothes, setting the table, washing dishes, sorting garbage, sweeping, assisting family members). Each item was rated on a six-point scale (0 = never performed, 5 = independently completed). Higher scores indicated greater household participation competence.
4. Improvement in Household Chore Competence
Calculated as the difference between pretest and posttest scores. Positive difference scores indicated gains in children’s competence, while lower or negative scores reflected limited or no improvement.
2.4.2 Application of AI Robots
The AI robot Kebbi Air S, developed by Nuwa Robotics, was employed as an instructional tool (Fig. 1). This robot was selected due to its suitability for early childhood education and its advanced features for interactive engagement. Its functions include:
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Natural conversational interaction to support dynamic learning experiences.
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A user-friendly, highly scalable development environment with strong IoT integration.
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Comprehensive APIs, SDKs, and development tools enabling customized interactive content.
Prior to the intervention, researchers were trained in the robot’s software and programming functions.
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Customized instructional scripts were developed to embed oral guidance, storytelling, and chore-related prompts into the sessions. Trial runs and adjustments ensured smooth implementation during the formal study.Fig. 1
Kebbi Air S Robot
From May 2025, the program was implemented across four consecutive weeks. Each weekly session lasted 30 minutes and followed a structured theme (Table 3). The robot facilitated interaction by introducing household tasks, providing demonstrations, and prompting intergenerational storytelling and reflection.
Session | Theme |
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1 | It’s Mealtime |
2 | I Can Clean the House |
3 | I Can Organize Clothes |
4 | I Am a Little Helper at Home |
2.5 Research Ethics
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All researchers completed the Research Ethics Education Program administered by the Taiwan Academic Ethics Education Resource Center prior to implementation. A
The study was conducted in accordance with international ethical standards and national regulatory requirements. A
Approval was granted by the Institutional Review Board (IRB) before data collection commenced.Informed consent
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was obtained from all participating caregivers and, in the case of minors, from their legal guardians. Participants were informed about the study’s aims, procedures, risks, and data usage. Confidentiality was strictly maintained; data were anonymized, securely stored, and restricted to research purposes. Photographs and video recordings of the sessions were collected only with explicit consent and stored on password-protected devices. In compliance with data management protocols, all files will be destroyed by December 31, 2030. Personal identifiers will not be disclosed in any publication or presentation of research findings.3. Results
3.1 Paired-Sample t-Test Analysis
To assess whether participation in AI-robot–assisted intergenerational household activities produced significant changes in young children’s household participation abilities, a paired-sample t-test was performed. Results indicated that the mean pretest score (M = 39.80, SD = 12.77) differed significantly from the mean posttest score (M = 44.76, SD = 12.40), t (49) = − 2.51, p < .05.
These findings demonstrate that after four weeks of participation in AI-robot–assisted intergenerational activities, children’s household participation abilities significantly improved, suggesting the effectiveness of integrating technological support in intergenerational learning contexts (Table 4).
Variable | Pretest M | Pretest SD | Posttest M | Posttest SD | t | p |
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Children’s Household Participation Ability | 39.80 | 12.77 | 44.76 | 12.40 | –2.51 | .015* |
p < .05.
3.2 Hierarchical Regression Analysis
A hierarchical regression analysis was conducted to further investigate whether children’s health status, willingness to participate in chores, and initial household participation abilities moderated the effects of the AI-robot–assisted intervention. Improvement in children’s household abilities (posttest minus pretest scores) served as the dependent variable.
Step 1 (Model 1): Children’s health status exhibited a significant negative effect on improvement in household abilities (β = –.34, p < .05). This result indicates that children with poorer health conditions demonstrated greater gains in household abilities after the intervention.
Step 2 (Model 2): When willingness to participate in household chores was added as a predictor, results revealed a significant positive effect (β = .40, p < .01). This suggests that children with higher levels of willingness experienced greater improvement in household abilities following the intervention.
Step 3 (Model 3): After controlling for pretest scores of household participation abilities, results showed that initial competence exerted a significant negative effect on improvement (β = –.57, p < .001). This finding indicates that the intervention was especially effective for children who initially demonstrated lower household participation abilities, as they exhibited greater gains. Notably, in this model, the effects of children’s health status and willingness to participate—previously significant—were no longer statistically significant, highlighting the overriding influence of initial ability levels (Table 5)
Variables | Model 1 | Model 2 | Model 3 | ||||||
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β | t | VIF | β | t | VIF | β | t | VIF | |
1.Children’s Health Status | − .34 | -2.50* | 1.00 | − .33 | -2.63* | 1.00 | − .19 | -1.90 | 1.07 |
2.Willingness to Participate | .40 | 3.19** | 1.00 | .45 | 4.63*** | 1.01 | |||
3.Initial Household Ability (Pretest) | − .57 | -5.66*** | 1.07 | ||||||
F(△F ) | 6.25*(6.25) | 8.81***(10.18) | 20.42***(32.03) | ||||||
R2(R2 adjusted) | .12(.10) | .27(.24) | .57(.54) | ||||||
ΔR2 | .12* | .16** | .30*** | ||||||
*p < .05. **p < .01. ***p < .001.
4. Discussion & Suggestions
4.1 Discussion
4.1.1 Significant Learning Effects of AI-Robot–Assisted Intergenerational household activities
The results of this study demonstrate that AI-robot–assisted intergenerational household activities conducted in parent–child centers significantly enhanced young children’s household participation abilities. These findings align with prior research indicating that the integration of AI robots in preschool contexts can effectively improve children’s engagement in household tasks and related learning outcomes [23]. The use of technological media as an instructional tool appears to diversify children’s learning pathways, thereby reinforcing household competence.
Furthermore, feedback from participants indicated that the AI robot’s interactive and dynamic features created a lively and enjoyable learning environment. The robot’s multimodal instructional style not only sustained children’s attention but also increased their motivation to engage in chores. Importantly, the joint involvement of grandparents and other family elders provided additional encouragement and emotional support, which further amplified children’s willingness to participate. Collectively, these findings underscore the pedagogical potential of technology-enhanced intergenerational learning activities in fostering children’s domestic skills.
4.1.2 Greater Effectiveness for Children with Lower Initial Competence
Hierarchical regression analyses revealed that the AI-robot–assisted activities were particularly effective for children who initially exhibited lower household competence.
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Prior to the intervention, discussions with parents highlighted a common recognition of the importance of involving children in household chores, yet also revealed a lack of effective strategies to facilitate such participation in daily family life. Through the structured activities, however, parents acquired practical skills for scaffolding children’s engagement in diverse household tasks.These findings not only validate the effectiveness of the intervention but also highlight important applied implications. Specifically, AI-robot–assisted intergenerational activities can serve as a valuable pedagogical model for parents, enabling them to develop concrete strategies for cultivating children’s household competence. This suggests that technology-supported programs may function as a catalyst for both children’s skill development and parents’ role in fostering learning within the family context.
4.2 Suggestions
This study represents an initial attempt to explore the effects of AI-robot–assisted intergenerational household activities on young children’s participation in chores. While the findings are promising, several limitations should be acknowledged. The sample was restricted to four parent–child centers in Taoyuan City, which constrains the generalizability of results to broader populations. Future research should expand the sampling scope across diverse geographic and sociocultural contexts to validate and extend these findings.
Additionally, subsequent studies could investigate potential variations in effectiveness based on the type of intergenerational participants (e.g., parent–child dyads versus grandparent–grandchild dyads) involved in the activities. Comparative studies across different early childhood education settings—such as preschools, parent–child centers, and other community-based institutions—would also contribute to a more comprehensive understanding of how AI-robot–assisted instruction influences household participation learning.
Moreover, although this study confirmed the positive role of AI robots in enhancing children’s chore competence, findings also reaffirmed the critical influence of parental involvement [11]. Some parents demonstrated insufficient skills in guiding and motivating children to engage in household tasks. This highlights the need for parenting education programs that explicitly integrate household-related learning into their curricula. Parent–child centers and family service agencies could incorporate workshops or training modules emphasizing the developmental benefits of chore participation, while simultaneously equipping parents with strategies to effectively scaffold such learning at home.
In summary, the present study contributes to the growing body of literature on technology-assisted intergenerational learning by demonstrating the effectiveness of AI robots in promoting children’s household participation abilities. At the same time, it emphasizes the importance of expanding both research scope and parenting education initiatives to ensure the sustained integration of household learning in early childhood development programs.
4.3 Conclusion
This study examined the effects of AI-robot–assisted intergenerational household activities on young children’s household participation abilities. Results demonstrated significant improvements after four weeks of intervention, particularly among children with initially lower levels of competence. These findings suggest that AI robots can serve as effective instructional tools by creating engaging, interactive learning environments that enhance children’s motivation and skill development, while also supporting parents in acquiring strategies to guide children’s household participation.
Overall, this study highlights the potential of integrating AI technology into early childhood and family education programs. By combining intergenerational engagement with technological support, parent–child centers and similar institutions can strengthen children’s independence, responsibility, and collaboration within the family context. Future research should expand to larger and more diverse samples, as well as explore long-term impacts, to further validate the effectiveness of AI-robot–assisted learning in promoting young children’s household participation and intergenerational inclusion.
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Author Contribution
Conceptualization, S.-M.T.; Methodology, S.-M.T. and C.-F.L.; Software, C.-F.L.; Validation, S.-M.T. and C.-F.L.; Formal Analysis, C.-F.L. and S.-M.T.; Investigation, C.-F.L.; Resources, C.-F.L.; Data Curation, C.-F.L.; Writing-Original Draft Preparation, C.-F.L. and S.-M.T.; Writing-Review & Editing, S.-M.T. and C.-F.L.; Validation, C.-F.L. and S.-M.T.; Supervision, S.-M.T. and C.-F.L.; Project administration, C.-F.L. and S.-M.T.; Funding acquisition, C.-F.L. All authors have read and agreed to the published version of the manuscript.
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