How might emerging modalities (e.g., NLP) be leveraged to transform the provision of parenting support? To explore the role of AI technologies in supporting parenting behaviour—and child-well-being—we surveyed 92 parents to gather their perspectives on nine future-oriented scenarios. We used Design Fiction and Speed Dating to understand parents needs and preferences around the design of agent-based supports. We explore the perceived benefits of AI assistants (i.e., receiving objective feedback, managing emotions and personalised guidance) and the most voiced concerns (i.e., AI undermining parental authority, replacing human interactions, and promoting lazy parenting). Finally, we highlight a number of plausible design directions based on the scenarios that parents were positive about.
1 Introduction
Children’s well-being is greatly influenced by their family environment [304]. Parenting behaviour is one of the most modifiable ways of supporting children’s well-being [126, 200, 215] compared with other risk factors (e.g., poverty, single-parent household and parent depression) [67, 84, 111, 113, 315]. In light of this, various parenting programs have been developed to support and guide parents in cultivating positive parenting practices and improving family well-being [296]. However, despite their availability and promising outcomes [39, 102], parent training (PT) programs often fall short due to low-engagement, lack of personalisation and limited contextual understanding [28, 72, 176, 302, 323]. Human-Computer Interaction (HCI) research has made important contributions towards designing for supporting well-being, family dynamics and parent-child interactions. For example, prior work has considered designing for in-the-moment support, increasing awareness of parent roles, and fostering empathy and reflection (e.g., [146, 270, 326, 366]) yet, only limited work has investigated how new techniques (e.g., Artificial Intelligence (AI), Natural Language Processing (NLP)) could transform the provision of parenting support, skill development and in turn, behaviour change. In this paper, we investigate the opportunities and tensions for designing digital supports with new modalities/techniques (such as those found in agent-based systems) in this sensitive space.
With the increasing presence of AI-mediated technologies across domestic life, AI technologies could potentially support parenting by, for example, supporting knowledge acquisition through access to timely and relevant parenting support [197], increasing productivity by automating certain tasks [337], or by quantifying parent and child technology habits, AI literacies and learning needs [109]. However, with AI becoming more and more embedded in domestic family life, it is important to critically consider how evolving AI technology applications can impact on parenting, both positively and negatively. Given the speed that AI powered systems are being utilised across diverse application areas rather than cautiously introducing and implementing through design [6], it is not always clear what decision making processes inform what is prioritised. Little research has considered how different AI functionalities directly relate to evidence-based parenting approaches, whilst also considering the parent perspective of how AI-mediated parenting support could impact on their daily lives. This points to the opportunity to lay the groundwork for understanding if and how we can employ AI and NLP techniques to begin to address parenting needs. While keeping in mind that any potential AI-mediated system may still have considerable technological limitations, in this project we were specifically interested in examining if there was an opportunity for:
(1)
AI technology/conversational agents to be utilised for parent training and support; and
(2)
designers to explore what these applications might look like so that their design could potentially enable behaviour change through situated support.
We begin this process by synthesising what is already known in the parenting literature—grounded in psychological theories—to create fictional accounts of future technologies that might support parenting. Through design fiction [50, 218], we aimed to better understand how we can situate AI based systems in the parenting domain. Similar approaches have been utilised by HCI researchers in educational, developmental, and health domains (e.g., [54, 60, 62, 123, 277, 290, 309, 340, 349, 367]), suggesting that fictions can take a key role in envisaging how HCI could engage and design these technologies (e.g., [3, 48, 116, 143, 281, 286, 314, 350]). To foster conversation about supporting future parent-centred AI-mediated interactions, we conducted a mixed method study where we explored how AI techniques (e.g., NLP) might be drawn on to create new applications that support parenting (i.e., optimizing delivery of intervention components in accordance to user needs and acceptance).
To this end, we sought to answer the following research questions:
RQ.1
What are some of the plausible functionalities that an agent-based system could potentially possess to effectively meet parenting needs and provide in-situ support and learning?
RQ.2
How do parents react to design fictions of AI assistants for parenting support?
RQ.3
What do parents perceive as potential benefits and frictions exist in this emerging design space?
To answer the research questions, we present a three-step study where we created future-oriented design fictions and explored the possibilities and implications of these emerging AI technologies in the context of parenting support. As a first step, to theoretically ground our fictional scenarios, we drew on prior empirical and theoretical work and attempted to establish a framework for understanding the specific needs of parents and how technology could support them (RQ1). We then iterated this framework through interviews with experts (in the psychology and parenting domain). Second, using this framework we designed nine fictional scenarios illustrating parenting needs and the potential effectiveness of agents/NLP techniques in addressing them (RQ1). Finally, we used the speed dating with storyboards/scenarios method [99, 370] and operationalized our scenarios through survey with 92 parents who reacted and provided feedback toward the technologies we envisioned (RQ2 and RQ3).
Our survey findings showed that parents expressed mixed reactions to the potential benefits of AI in the presented parenting scenarios, as well as skepticism and concern about how specific AI functionalities would impact on genuine parent-child interactions and family dynamics. Our paper contributes:
i.)
an expert-informed and evidence-based conceptual framework for identifying what parenting support scenarios should incorporate, and
ii.)
empirical research on parent reactions to the envisaged opportunities of AI-mediated technology.
2 Background and Related Work
2.1 Parenting Education and Technology
Parent training (PT) is a widely recognized as an effective evidence-based treatment for addressing disruptive behaviors in children. Through PT, parents are equipped with the knowledge, skills, and strategies (e.g., praise, time-out) to effectively manage their child’s challenging behaviors [247, 354]. This form of training empowers parents to understand the underlying causes of their child’s behavior and implement appropriate strategies that promote positive change. It has consistently shown positive results in improving both child and parent outcomes, including enhanced child behavior; reduced parental stress; and improved overall family functioning [293, 322]. PT is typically delivered in-person, through manualised curricula (e.g., theory, practical examples) and involves activities like role plays, discussions, video demonstrations, and practice of parent-child interaction techniques during sessions or as homework [20, 182]. Although these interventions have positive effects on parent and child outcomes, in-person programs are resource-intensive and face challenges in reaching, engaging, and retaining parents [12, 112, 130, 235]. In addition, parents—who do make it to the sessions—report difficulties in applying the learned techniques at home [243]. Varying reasons contribute to lack of skill generalisability [86, 334] such as difficulty in changing established habits; lack of cooperation from their partners in implementing the techniques; allocating sufficient time for joint parenting; and incorporating the techniques into their already demanding lives [243]. Ultimately, a program’s effectiveness depends on parents consistently applying the acquired skills in the appropriate settings and with the intended targets.
Digital parenting interventions have emerged as a solution to some of these challenges [37, 135, 235, 282]. These interventions have been adapted from an existing evidence-based in-person parent training program (e.g., [301]). They vary in delivery, with most focusing on structured teaching methods and skill development (i.e., requiring parents to progress through modules in a sequential pathway) [13, 22, 87, 225], while others focus on psychoeducation (e.g., [11, 24, 256]). In addition, they use a variation of technology components (e.g., audio, video, text, quizzes, skills training or coaching) to deliver the program content [225]; and interactive features (e.g., discussion boards or gamified content) to engage parents, promote self-efficacy, and encourage problem-solving [13, 24, 87, 249, 256]. Despite their promise [39, 102], digital PT programs often fall short in engaging parents as they require significant levels of motivation and commitment to finish the program [57] which can be very taxing on parental time and effort. In addition, lack of accountability (e.g., not having peer support to discuss and motivate, nor having milestones to achieve), limited access to proper technological supports (e.g., not having broad access to the internet and/or computers/phones for individual use) and limited contextual understanding and personalisation (i.e., not directly providing support given the particular dynamics within a family) further contribute to parents low engagement rates [28, 103, 176, 302, 323, 365]. As such, the acquisition and practise of skills is hard to obtain, implement and maintain. While adapting existing interventions into programs delivered online (instead of in-person) might increase scalability and reach (e.g., [25, 58]) there remains a need to design for situated support and learning (i.e., effectively scaffold the implementation of skills to their everyday life) to support long-term outcomes [103, 108, 152, 305].
2.2 Emerging Technologies in Parenting
A growing body of work suggests that technology-enabled supports can effectively scaffold parent-child interaction, well-being and family dynamics (e.g., [124, 146, 188]). Yet, limited work has explored how new modalities (i.e., NLP) might be leveraged –for situated-support and learning– in this sensitive space. The following, provide an overview of the directions HCI research has taken in supporting positive family functioning.
Family Informatics.
HCI research on personal informatics (PI) has shifted from individual data collection to studying collective and situated experiences – in particular within the realm of family systems [270]. Family informatics research has investigated a myriad of ways in supporting family activities and needs, such as supporting communication of daily activities in the family via a shared message board [262], coordinating family routines through (digital) calendars[100], sharing of health information among inter-generational family members [45], supporting healthy eating practices [88] and exchanging family fitness activity data using mobile apps (e.g., Storywell [291]). Other work has explored how tracking for children has implications within the broader family ecosystem. For instance, Wang et al. [351] and Lupton et al. [222] have demonstrated that implementing PI tools to track children’s data not only improves parents’ understanding of their child’s patterns but also reduces the need for frequent physical check-ups by facilitating the transfer of baby-related information to mobile applications. Nonetheless, by using tracking tools to monitor baby and child habits, these ubitiquitous technologies can increase parents’ anxiety, highlighting the importance of considering their emotional well-being and input when designing parenting supports [351].
Real-Time Parenting Supports.
While the aforementioned examples centered on situated experiences among family members, other work has specifically investigated the integration of active ingredients (i.e., theory-based change methods such as teaching reflection, empathetic listening and emotion-regulation [41, 356]) into the intervention using diverse delivery methods [1, 168, 182, 185, 210, 212, 213]. For example, Kim et al. [188] utilised reflective practice to support parent-child interactions via a wearable smart mirror designed to quickly give parents their child’s view during their interactions (second person view). Giannakos et al. [146] also used reflection to encourage parental awareness of the different roles they can take during collaborative activities with their child through a tangible device (Awareness Object). Separately, Pina et al. [268], investigated just-in-time stress coping interventions for parents with children with Attention Deficit Hyper-Activity Disorder (ADHD). They found that providing prompts right before a full escalation of stress occurs was highly effective, as parents were more receptive to intervention strategies during that critical period. Finally, WAKEY [74] teaches parents communication strategies for conflict resolution during morning routines.
Researchers have also investigated the use of real-time interventions to assist parents during their interactions with children who are impacted by language disorders. For instance, TalkBetter [141] aimed to support parents communication skills through the monitoring of conversational turns – providing personalized feedback on communication strategies. Similarly, TalkLime [329] monitors turns while using real-time visualization on the parent’s phone screen to induce behavior change. Lastly, SpecialTime [171] uses automatic evaluation of parent language during Parent-Child Interaction Therapy (PCIT) and provides real-time feedback on spoken dialogue acts between parent and child in-situ, removing the barrier for clinical visits. These findings suggest that feedback on language use is important in various parenting settings thus, we explore the opportunities and limitations of designing language-delivered supports in various parenting and learning contexts.
Augmenting Parenting Through Conversational Agents.
Language-based technologies, often called "conversational AI", "dialogue systems", "chat-bots", "virtual assistants" or "conversational agents" (i.e., both text-based and speech- or voice-based modalities for input and output) [128, 175, 221, 273, 279], have increasingly been adopted by parents and children alike. AI assistants that use Automatic Speech Recognition (ASR) [38] and Natural Language Understanding (NLU) [151, 359] add a new dimension to how home technology can enhance parent-child interaction and influence parenting behaviors. Whilst conversational agents often have a separate goal (e.g., information retrieval), research has shown that they also impact on family dynamics. For instance, research has recognized potential areas for utilizing conversational assistants to support the educational, developmental, and social well-being of children (e.g., [35, 110, 209, 226, 357, 364]). In addition, Beneteau et al. [35] investigated parents and children’s interactions with Amazon Alexa in-situ. Through a 4-week deployment they found that voice interfaces enhance verbal communication and improve children’s communication skills. Separately, Storer et al. [335] found that smart speakers can support more accessible parent-child interactions for mixed-visual-ability families.
Another line of research has explored the use of Artificial Companions (AC) or robots to support wide-ranging child-rearing activities. For instance, Fink et al. [133] showed that long-term interactions with robots can motivate children to tidy their room; as well as motivate children’s reading habits and support their reading comprehension [239]. Separately, Emobie [8] teaches children social skills and coping strategies when dealing with negative emotions (e.g., guiding them through breathing exercises).
ACs have been also designed to support the delivery of autism interventions [303] and educational interventions [31] Finally, ACs have also been assigned the role of a teacher, trainer, tutor, or coach [70, 71, 76, 77, 150, 244, 368] and demonstrate educational competencies by guiding users through activities, teaching new skills, whilst providing personalized learning and tailored support [29, 285]. To this end, ACs have proven effective in enhancing learner motivation and performance (e.g., [36, 77, 83, 285, 330]).
While these studies provide important insights into how adult carers and children perceive, interact, and learn with AI assistants, they do not address the possible roles these agents can support within the family ecosystem and in particular in the context of parent training. As such, examining parents’ needs and preferences regarding the agents’ role in supporting family functioning can provide valuable insights for designing effective AI-driven parenting supports. In the following section,we discuss the ways in which AI supports can support skill development, scale up and improve the delivery of behaviour change interventions and mental health treatment.
2.3 AI Technologies for Mental Well-being
The development of conversational AI systems that can interact coherently and engagingly with users has been an active area of research within HCI, NLP and Machine Learning (ML). A growing body of literature in HCI and health has designed various technologies (e.g., self tracking, conversational agents, tangible interfaces) to promote mental health and well-being [92, 292, 328]. For instance, several self-tracking tools have been developed to self-monitor people’s stress [81], mood [165, 205] and sleep [79] which can help increase self-awareness and motivate health-related behavior change [186, 278, 280]. In addition, conversational agents (CA) have proven useful in helping people monitor their feelings and behaviors [191], encourage deep self-disclosure in journaling practice [207, 209], and improve well-being [134, 208]. Previous studies suggest that CAs can serve as virtual companions that track behaviors, listen without judgment and offer proactive guidance (e.g., cognitive-behavioral therapy) to improve mental well-being through conversational interactions [134, 307]. Further, CAs can coach users in developing different skills (e.g., [352]) as well as improve their mental well-being by training their thoughts and behavior (e.g., [219, 363]). For instance, Wang et al. [352] developed a CA that coaches people to relieve their public-speaking anxiety via cognitive reconstruction exercises; while Gabrielli et al. [142] proposed a chatbot-based coaching intervention that successfully helped adolescents learn life skills (e.g., strategies for coping with bullying).
Despite ongoing efforts to develop AI for mental health support (as per above), the implementation of the AI vision is a complex endeavor. Specifically, most research often focuses on technical feasibility with less consideration of real-world clinical impact [2, 78, 129, 333]. As such, a substantial gap exists between what has been demonstrated in research settings and what has been validated in clinical practice [107, 274, 276]. In addition, many AI models in healthcare go unused due to the lack of understanding on how and for whom to use them effectively [73, 284]. This leads to a disconnect between AI development and user needs, thereby reducing its effectiveness and adoption. Lastly, identifying the active ingredients of an intervention (i.e., specific components that serve as key drivers of change) is crucial for unpacking the intervention black box to pinpoint the ways in which AI can amplify their use. Therefore, integrating AI into an intervention, should not be simply about deploying a ML model but rather viewing it as an enabling component of a broader solution [217].
2.4 Design Fiction and World-building
Our design research exploration draws on various design futuring paradigms of critical design [17, 18], speculative design [114, 125], and design fiction [51, 59, 65, 169, 361]. Critical design aims to challenge assumptions by enabling users to evaluate concepts and uncover hidden dimensions such as values, ethics, personal beliefs (or disbelief’s), and identify potential flaws [117, 160, 361, 362]. The objective of critical design is to involve users in a constructive debate and envision alternative perspectives [18]. Another approach in exploring future possibilities is through speculative design, which focuses on the use of creative/generative thinking to construct sociological, technical, and unconventional ideas (among others). In this way, speculative design can “make a whole range of viable and not so viable possibilities tangible and available for consideration” [114]. Building on these design approaches, we used design fiction to construct AI assisted parenting futures where our design concepts lived [19, 90]. Design fiction is a useful methodology that enables the envisioning of novel designs and technologies through world building while positioning them within a fictional context. Design fiction is not pure imagination but instead, it (typically) entails conceiving of a relatively plausible new technology based on current trends and encourages users to critically examine potential use cases and consequences [48, 161, 258, 267].
2.5 Summary and Motivation
A number of opinion pieces suggest that Human-AI collaboration might play a role in future parenting interventions ([109, 368] as per above). However, it is not yet clear what the interaction design patterns would be for these systems: we do not yet have models of where and how AI-assisted interactions would support parenting. One approach to better understand how we can situate AI within the parenting domain—accepting the high development cost for any specific feature—is to rely on design fiction methodologies, as per prior work in other domains. Our approach shares similarities with prior HCI research that has argued for exploring the potential roles, values, and implications of emerging technologies by using multiple design proposals to establish a better overall understanding of the design space (e.g., [42, 361]).
In what follows, we start by developing a conceptual framework to synthesise the core under-supported parenting needs (as known in the parenting literature) with a set of plausible roles that AI-driven technology could play (drawing on prior examples and suggested opinions in the literature). Second, we use this framework to develop nine scenarios depicting parenting needs and the potential effectiveness of agents/NLP techniques in addressing them. Finally, we draw on these scenarios to gather perspectives from parents (n=92) through survey to better understand which of the futures envisioned (if any) might be desirable (see Figure 1 for an outline of our process).
Figure 1:
Figure 1: Overview of the process we followed, structured in three steps, and the resulting outcomes of each step.
3 Methodology
3.1 Phase 1. Conceptual Framework Development
To understand how we might design for this emerging space we first need a rigorous, evidence-based understanding of what are the (parenting) challenges and intervention mechanisms that should be supported. To do this we developed a framework (drawing on prior empirical and theoretical work) in an attempt to identify the specific needs of parents and how technology could support them. We then explored the potential synergy between the design targets—role of technology—in supporting these challenges. This process enabled us to design nine scenarios that were theoretically grounded and relevant, based on the parenting literature.
We developed the conceptual framework in 3 phases: As a first step, we conducted a literature search on parenting needs (e.g., [63, 157, 183, 196, 235, 240, 241, 324]), the learning and behavioural sciences (e.g., [14, 15, 21, 144, 147, 224, 230]) and intelligent system taxonomies/delivery approaches (e.g., [57, 87, 115, 120, 189, 190, 245, 278]). Rather than carrying out an exhaustive literature analysis across these fields, we sought to draw research-based inspiration for our scenarios [48]. The literature survey enabled us to discern specific parenting needs and their relationship to digital parenting support. Based on these findings, we compiled a list of needs that seemed important enough to assess, as well as a catalog of technology approaches and delivery mechanisms that could potentially support them. To further refine our list, we identified gaps in existing digital parenting support literature and opportunities for technology to address parents’ needs effectively. This process enabled us to identify six initial categories of parenting needs and technologies that might support them. Finally, we iterated and validated the categories and descriptors we developed by holding interviews with three psychology experts (two early childhood psychology researchers and one principal clinical psychologist from the UK National Health Service (NHS)). The expert feedback resulted in modifications to the content and structure of the conceptual framework, resulting in the inclusion of three additional parenting dimensions. In terms of the technology dimensions the experts provided insights on the potential implications of these modalities however, without any additions or removals.
Figure 2:
Figure 2: Snapshot of process: a. Populated Framework, b. Combinations of cells for scenario building, c. Overview of nine futures. See supplementary documents for higher-resolution images.
During this phase we used the conceptual framework consisting of a set of (a) parenting theories from psychology literature and (b) technology mechanisms to develop our scenarios. The resulting 2-dimensional design space, consisting of parenting needs on one axis, can be applied to human-centered technologies/mechanisms on the other axis (see Figures 2 a and 2 b for a snapshot of these opportunities which we describe in full in Table 1 & 2 and Appendix A). Specifically, we created a matrix and populated it by identifying the connections between parenting needs and the role of technology in supporting them. This generative approach enabled us to outline various combinations of potential targets (parenting needs) and the corresponding interaction techniques (delivery mechanisms). As a result, we generated 54 potential combinations of needs and mechanisms (see Figure 2 a) to consider for inclusion in our scenarios. To determine which cell combinations we should focus and build our scenarios on, we generated the following three levels/questions:
•
Applications (Possibilities): How might AI assisted technologies support parents needs?
•
Implications (Probabilities): How might a combination of needs and learning mechanisms (technologies) support parenting?
•
Generative (Ideation): How might we design parent-agent interaction for in-situ support and learning?
These levels/questions guided our decision-making process in determining the cells we should prioritise. By following this approach, we identified the combinations (see Figure 2 b) that guided the design of our nine, fictional, theoretically grounded scenarios (see Figure 2 c). In the rest of the paper, we detail each dimension of the framework and the combinations that resulted in the final nine scenarios (cf., 4.1).
3.3 Phase 3. Survey Design
3.3.1 Speed Dating.
We used the Speed-dating [99] with storyboards method to gather parents reactions. Speed dating allows the presentation of information in a snapshot; is helpful in directing participants’ focus towards particular instances of technological behaviors/interactions and enables ease of data collection [370]. To elicit participant reactions and scope our scenarios, we created a Qualtrics-based survey consisting of three parts, taking approximately 15-20 minutes to complete. After consenting to participate in the study, participants were presented with the scenarios in random order. In the first part of the survey, participants were introduced to the three storyboards of each scenario and asked to reflect on the scenario they saw and evaluate whether they would like to reside in the envisioned future. We first, asked participants to: "Consider the scenario you just saw: Would you like to live in this future?" on a five-point Likert item question from "Definitely" to "Definitely Not" (Q1). Once they selected an answer for this question, we prompted them to "Please briefly explain your choice" through an open text field. The second question asked participants the likely-hood of using the technology depicted in the scenario, "How likely or unlikely would you be to use this app to help in your parenting?" on a seven-point Likert item question from "Extremely unlikely" to "Extremely likely" (Q2). The third question gauged the ease or difficulty of understanding the scenario in question, "How easy or how difficult was the scenario to understand?" on a five-point Likert item question from "Extremely difficult" to "Extremely easy" (Q3). The second part of the survey, invited participants to closely inspect the features depicted in the scenario (Q4). We provided a ’zoomed’ in version of the storyboard discussing the technology features and asked participants to indicate whether they like, actively dislike or feel neutral about each of the features in the scenario (see Figure 3). The final part of the survey, asked participants to consider all nine scenarios, "Consider all the scenarios you saw: Which of the futures envisioned is desirable and which less so?" and drag and drop at least two as their favourite (that they’d be willing to consider incorporating in their parenting) and place them on the "I love this box" and at least two that they dislike (that raise concerns/they’d find worrisome and would avoid using to support their parenting) and place them on the "Please don’t ever do this to me box" (Q5).
Figure 3:
Figure 3: Like/Dislike rating example (Q4).
3.3.2 Participant Demographics.
Participants (n=92), were recruited through Prolific1 an online survey research platform and paid at a £10.03 per hour rate for their time. In total, participants took between 7-50 minutes to complete the survey, with a mean completion time of 19 minutes. To be eligible for the study, participants were required to be (1) over 18 years of age, (2) fluent in English, (3) have at least one child aged between 4-10 years old and (4) be based in the United Kingdom (U.K.). The relevant ethics approvals were obtained through the university ethics board and all participants signed consent forms prior to any data collection.
3.3.3 Data Analysis.
To analyse the quantitative data we examined the distribution of opinions that participants expressed through their survey responses. For example, this involved examining participant attitudes (i.e., agreement or disagreement) towards the technology’s potential inclusion in their parenting practices; the likelihood of using the technology; and overall sentiment (i.e., like, dislike or neutrality) concerning specific functionalities (features) as seen in the scenario. The scenarios were intended to be research probes for speculation, rather than independent variables; as a result, we do not make direct comparison between the example technologies in our analysis. Qualitative analysis was conducted using affinity diagramming [220]. We structured the responses to the open-ended survey questions into clusters which represented recurring patterns. Considering the diverse data and moderate volume, we decided to adopt this pragmatic approach as suggested by Blandford et al. [49]. In the case that more than five participants mentioned a particular theme, we have provided the exact number of participants instead of their individual participant number. We account recurring themes and concepts with direct quotes and paraphrases from our participant data.
4 Design of the Conceptual Framework (RQ1)
4.1 Overview
In this section, we present a summary of the conceptual framework outlining each dimension along with the relevant literature (for a comprehensive review of the framework refer to appendix A). As shown in Table 1 the framework characterizes nine parenting needs: Reflection, Self-monitoring, Knowledge Acquisition & Implementation, Practice for Skill Acquisition & Transfer, Dealing with setbacks, Communication, In the moment support, Habit Formation, Conflict Resolution; as well as six technology mechanisms (see Table 2) that might support them: Psychoeducation, Feedback, Parent Surrogate, Decision Making Support, Prediction to explore possible outcomes and Coaching. In what follows, we illustrate how we used the framework to develop our nine fictional scenarios by outlining the combinations of dimensions for each scenario and delivery mechanism (see Figure 4).
Figure 4:
Figure 4: The table shows the combinations of dimensions that are included in each scenario as well as the delivery mechanism depicted in each scenario.
Table 1:
Identifying parenting needs
Reflection. Parental reflection is essential for promoting desired behaviors, appropriately addressing children’s feelings and needs, and foster emotional well-being and resilience [14, 85, 325]. Reflective competences are multilayered [310]. As such, engaging parents with diverse backgrounds and experiences in meaningful reflection remains a challenge [187, 300].
Self-monitoring. Training parents in self-monitoring (i.e., observe and track their feelings, reactions, behaviors, thoughts, and parenting activities) [136, 184] can help improve family functioning by enabling parents to identify their own vulnerabilities and navigate parenting challenges more effectively [251, 297].
Knowledge Acquisition & Implementation. Supporting parental knowledge on child development and positive parenting strategies improves their ability to communicate effectively, set appropriate expectations and respond to challenging behaviors [56, 170, 246]. By gaining and applying knowledge, and witnessing positive outcomes, parents can enhance their self-efficacy and confidence [177, 360].
Practice for Skill Acquisition & Transfer. PT is effective for skill acquisition (e.g., [121, 181, 231]). Through regular practice and application of skills parents develop a repertoire of responses to various parenting challenges (e.g., limit setting, communication and discipline) – improving the parent-child relationship [140, 182]. However, parents need support with consistently practising and applying the skills in their daily lives [122, 224, 334].
Dealing with Setbacks. Experiencing adversity can disrupt parenting abilities, increase stress, and lead to child behavioral difficulties [23, 139, 331]. To increase resilience and help parents deal with setbacks means supporting parents to recognize that past skills can be applied to future challenges [223, 311]. In this way, increasing preparedness towards possible setbacks is a way to compensate potential setbacks [289, 339].
Communication. Family communication and socialization processes are pivotal for children’s social development [193, 194]. Communication training helps parents understand and respond to their children’s needs, emotions, and behaviors [201, 295]. However, less focus has been paid to support co-parenting relationships—that is the collaboration and communication between parents—an important predictor of family functioning and child well-being [96, 158, 236].
In the moment support. PT interventions aim to extend behavior change beyond the training setting to new situations and circumstances [40, 255]. While PT programs are effective, they have not always shown success in generalising skills to the home or impacting child behavior in the long term [61, 316]. As such, training parents "to generalise" skills [86, 334] in daily life through in-the-moment,situated interventions [326] can remove the transfer barrier and support natural learning opportunities that fit into everyday routines[82, 355].
Habit Formation. Supporting parents in establishing family routines helps structure children’s environment whilst creating stability (e.g., establishing bedtime routines to reduce sleep problems) [131]. This helps children develop self-regulatory skills by teaching them that events are predictable and there are rewards for waiting [153, 272]. Finally, habits established in childhood can influence a child’s lifelong behaviors, shaping their approach to responsibilities and relationships [261].
Conflict Resolution. Marital conflict is typically categorised as destructive (e.g., violence, threats) [95, 233] or constructive (e.g., promoting positive resolution and communication) [97, 148, 233]. Destructive conflict heightens the risk of child adjustment issues, while constructive conflict improves positive development [119, 263]. Training parents to resolve conflict constructively can support the development of children’s adjustment, social skills and overall well-being [166, 336].
Table 1: Summary of the Conceptual Framework for parenting needs, including dimension descriptions and related literature.
Table 2:
Identifying role for technology
Psychoeducation. Providing parents with knowledge of child development stages and positive parenting practices is crucial for promoting positive outcomes in children’s health, education, and well-being [182, 271]. However, for timely and appropriate information delivery [127] it is pivotal to understand the needs and preferences of various parent groups (e.g., high vs. lower Socioeconomic status (SES)) [34, 241].
Feedback. Feedback in PT is an essential component for enhancing positive parenting practices [182, 353]. Training techniques include modeling, reinforcement, and correction [121, 313]. Programs typically use delayed feedback [178, 179] which however, effective for parenting and attachment [89, 299] limitation in access (due to its personalized nature and reliance on expert support) persist [173].
Parent Surrogate. Assistive technology, such as artificial companions (AC) or social robots, could provide an alternative solution for parenting support (e.g., [180, 364, 368]). These intelligent companions can perform various tasks at home, including communication, information exchange, and care-giving [137, 154]. By acting as a "surrogate," technology can automate low-level parenting tasks like homework support and meal planning (e.g., [9, 320]), alleviating parental pressures.
Decision Making Support. Parents face numerous decisions when it comes to caring for their child’s well-being (e.g., deciding how, when, and where to seek support; which parenting strategies to use) [69, 167, 242]. While some decisions can be planned for, unexpected situations can increase emotional challenges [32, 252]. As such, supporting parents deliberative decision-making is pivotal in fostering effective child rearing practices [145, 164].
Prediction to explore possible outcomes. Parenting and parent characteristics can predict childrens’ problem behavior [7, 64, 260]. Various models to predict parenting behavior have been developed (e.g., [26, 33, 324]) which (in most cases) suggest the determinants of antisocial and aggressive behavior in children. However, these models carry significant limitations (e.g., accuracy) due to limited data. Technological advancements (i.e., ML) could create more accurate models of parenting determinants and predict/prevent behaviors that cause child maladjustment [75].
Coaching Coaching parents during parent-child interactions is an important mechanism of change [313]. Coaching utilises a more non-directive approach in supporting parents behavioural changes [138] treating parents as collaborative partners rather than passive recipients of expert advice [288, 341]. The coach helps the learner (parent) reflect on their actions and practices, evaluate their effectiveness, and develop a plan to improve their skills and use them in the future [256, 345].
Table 2: Summary of the Conceptual Framework for technology mechanisms, including dimension descriptions and related literature.
4.2 Putting the Framework into Practice
4.2.1 Creating nine futures using the framework.
We used this framework as a conceptual model to facilitate design thinking for the envisioning of our scenarios i.e., emerging technologies that might support parenting. It offered an explanatory approach that can be effectively used in the early stages of research and ideation. Specifically, our scenario storyboards depict an AI function (e.g., NLP, sentiment analysis, conversational interfaces etc.) that could deliver parenting support. Figure 4 illustrates the dimensions and delivery mechanism included in each scenario. Each scenario acts as probe in which participants react to. We intentionally focus on the design mechanisms/features of the technologies envisioned to better understand the potential value of the suggested AI application as well as identify the technologies that resonate with parents. We first developed our scenarios as text based narratives amounting to 1,5 page each (length of text). To make the scenarios easily digestible and gather participant reactions, we developed the narratives into simple 3-step visual examples (refer to supplementary documents for all scenario storyboards). Each scenario/storyboard is composed as follows: first image, introduction of parenting need/context; second image, example of specific support/technology features that address the parenting need in question; third image, outcome/impact on family and (parent or child) behaviour. In this way, we were able to preserve the core components of the text-based narratives and translate them into concise visual examples.
4.2.2 Aesthetics and design rationale.
We aimed to produce scenarios that were novel and clearly situated in the future, yet realistic enough for participants to easily envision them happening in their own lives – this juxtaposition enabled the immersion with the provocations more deeply. Each scenario imagined a somewhat different technological capability/delivery mechanism (see Figure 4). Drawing inspiration from Wong et al. [361] we depicted each scenario using a variety of media, such as newsletters, advertisements, FAQ pages, testimonial pages, landing pages, and product pages. We also presented our designs in different contexts, such as on a desktop or a laptop, on an a mobile app, a newsletter spread, and in billboards as adds. We constructed these provocations using free stock images and vectors. In addition, the first author designed the landing pages, UI’s, advertisements and technologies using Figma, Adobe InDesign, Illustrator, Photoshop and Canva. For one of the scenarios (Scenario 5) we used Picsart, an open-source AI image generator as we were unable to source online the imagery we had envisioned. We deliberately designed our scenarios as high-end products/technologies to increase familiarity and easily immerse participants in the envisioned futures.
5 Findings
In this section we discuss the key findings from our survey. We report how participants perceived and reacted to AI assistants in the context of parenting support. In what follows, we report our quantitative findings and supplement them (where relevant) with the most notable themes that emerged through our analysis of the qualitative responses. To protect anonymity, participants are referred to by using ’P’ for participant, followed by a participant number.
We present the results in three sections:
(1)
First, we start by providing a general overview of participants attitudes and reactions across all nine scenarios. In addition, we discuss perceptions of believability and relatability of the futures (RQ2).
(2)
Second, we discuss four scenarios that participants reacted—in majority—more positively to provide a more detailed perspective about the potential ways the envisioned futures could impact parenting life. We discuss, the key themes that emerged from each design concept highlighting both the tensions and (perceived) qualities of the envisioned technologies (RQ3).
(3)
Finally, we discuss the specific features within each of the nine scenarios to capture parents perceptions about the particular mechanisms envisioned.
5.1 How do parents react to design fictions of AI assistants for parenting support (RQ2) and what do parents perceive as potential benefits and frictions exist in this emerging design space (RQ3)?
5.1.1 Consider the scenario you just saw: Would you like to live in this future and how likely are you to use it?
Participants were presented with the scenarios in random order – their responses to the proposed futures varied. When asked to indicate their reactions to the scenarios on a 5-point Likert scale (see Figure 5a), the largest proportion of participants selected the option: ’I am open to the proposed imagined technologies and acknowledged their possible, positive implications’ received the most responses (selected by 29.95% of participants). This was closely followed by the option: ’the technologies unlikely to be helpful for me’ (selected by 29.21% of participants), who expressed reservation, but did not rule them out entirely. Almost a quarter of all participants (23.72%) outright rejected the scenarios as possible future solutions, while a smaller percentage (12.84%) expressed some confidence in utilising the envisioned technologies. Lastly, a small but notable group (4.28%) indicated a willingness to explore the AI assistants further (i.e., would "Definitely" try them to support their parenting).
Figure 5:
Figure 5: Parent’s ratings of the individual scenarios
Participants provided various reasons for both their positive and negative sentiments toward AI parenting supports. Specifically, some participants recognised the potential benefits of AI assistants in providing suggestions, reminders, supporting positive habits, and assisting with their child’s academic efforts, "I quite like the idea that it can work as a tutor of some sorts and help children engage better in their homework tasks" (P11). Others, expressed that they could envisage that the potential AI assistants could be helpful in serving as virtual companions or support systems, particularly in cases involving single parents or situations where an additional perspective might be needed, "I like that it will listen and give support where needed, sometimes in the moment you need another person to help break up a situation if you and your child are both frustrated" (P30). Interestingly, participants expressed that the designed scenarios appeared believable (refer to Appendix B for the response distribution graph) suggesting that they deeply engaged with envisaging its role in their lives (i.e., the parent reviews within the storyboards offering further social proof and validation of the tool’s (potential) effectiveness: "It seems like it has helped some people based on the reviews so it could be good for others" (P54).
Across the data, participants recognised the potential benefits of AI assistants, but they also expressed concerns regarding the potential impact of technology on genuine parent-child interactions and the bonds that parents form with their children. This was reflected in participants’ hesitation to strongly accept (or reject) the solutions, often indicating that they were "Slightly likely" (30.67%) to use the envisioned technologies. In particular, participants, expressed concern that the technology might undermine the authenticity of parenting experiences and become "too involved...[and] take away valuable parent time with children if parents relied on it" (P72). Additionally, participants expressed discomfort towards the concept of AI assistants taking on parenting roles. In particular, they voiced concerns around AI undermining parental authority, interfering and altering family dynamics; as well as promoting "lazy parenting" (P66). Lastly, we observed that participants expressed privacy concerns and the potential negative impact of constant monitoring and data collection. Specifically, participants expressed fear that monitoring through AI technologies could negatively impact on their confidence as parents while "it could cause your child to become unhappy [if] you are watching them all the time" (P19). This negative sentiment was also evident across participant responses towards adopting the envisioned technologies, with 54.39% (sum of all scenarios) considering it "Extremely unlikely" (see Figure 5b for responses across all scenarios).
In summary, participant responses reflected an interplay of optimism and apprehension regarding the use of AI assistants for parenting support. In order to describe in more detail how these concerns and opportunities manifested, we next focus on four scenarios that received predominantly positive reactions from the participants, i.e., the scenarios that participants could most likely envisage being part of. We unpack the key themes that emerged from each design concept, highlighting the tensions, opportunities and perceived qualities of the envisioned technologies.
5.1.2 Four plausible futures.
We selected four scenarios that received the most positive responses (i.e., assigned a rating of possibly, probably and definitely). This approach enabled us to scope down the scenarios that parents expressed they could envisage, thus producing the most detail about potential ways they could impact on parenting life (RQ3). Specifically, the following four scenarios generated the most positive ratings across all participants: ParentSage (64.13%), SerenityGuidance (60.87%), ParentSense (58.70%) and HarmonyHub (47.82%). These scenarios were also separately ranked as most appealing in a later survey question that asked participants to rate all nine scenarios (see Figure 6), which reinforced the prominence and plausibility of these four scenarios for parents.
Figure 6:
Figure 6: Parent ratings when presented with all nine scenarios (Q5).
Scenario 1: ParentSage.
The first scenario explored parental perspectives towards a wearable app designed to provide feedback on parenting practices, track progress as a way of increasing self-awareness of skill development, and offer helpful reminders to increase engagement with learning (see Figure 7). The majority of participants (64.13%, N=59) indicated a willingness to try the technology, while less participants (35.87%, N=33) expressed negative opinions and skepticism towards the envisioned future. Based on our analysis of the open text field responses, we identified two overarching themes: (1) Potential benefits and practical applications; and (2) Privacy and judgment concerns.
Figure 7:
Figure 7: Scenario 1: ’ParentSage’ storyboards.
Some parents expressed that they were open to receive support and guidance by AI assistants as they acknowledged that being a parent is challenging and there’s always space for improvement "I feel I’m not the best parent I could be, and welcome support to improve" (P07). Specifically, participants cited personalised feedback, skill-mastery and evidence-based advice as the primary factors that would motivate their uptake of the envisioned app. However, despite the potential benefits, participants also expressed reservations about whether AI assistants could truly enhance the parenting experience, questioning the added value and purpose of these tools. For instance, participant (35) expressed caution about AI becoming too involved in family life and detracting from the natural parenting experience "I don’t like the idea of having to be reminded to be a parent as this should be natural". Finally, participants expressed worry about the impact on their privacy and autonomy as parents. In particular, participants expressed discomfort with the idea of constant monitoring and feedback; and emphasised the importance of maintaining control over their parenting decisions as "this feels overly judgmental and will just leave people believing that they are bad parents" (P56).
Scenario 2: SerenityGuidance.
The second set of storyboards probed participant attitudes toward a spoken diary app that encourages parental emotional release. Using advanced speech-to-text technology, the AI assistant identifies potential knowledge gaps, provides personalised guidance and supports data-driven decision-making (see Figure 8). The majority of participants 60.87% (N=56) indicated a willingness to try the technology, while a substantial minority 39.13% (N=36) expressed negative opinions towards the envisioned future. After examining the participant responses provided in the open text field, we identified three overarching themes: (1) Supporting parental emotional well-being; (2) AI assistants for self-improvement and learning; and (3) Concerns around AI emotional support.
Parents reported that they saw the benefits of the scenario as they recognised the emotional challenges that come with raising children, and the need for support in managing their own emotions as well as their child’s. In particular, participants expressed that they could envisage AI for emotional support as a potential solution for those moments when parents need to vent, seek advice, or simply find comfort. For instance P67 reflected on their need for regulating their emotions: "I am aware that I struggle with frustration and that my family around me find that challenging. They don’t know how to deal with it, and being able to vent without being challenged would be welcome". Participants also perceived the envisioned AI assistant as a tool that could complement their existing parenting style, given its less intrusive role towards offering personalised support and guidance, e.g., P83 commented: "Everyone has some frustrations (emotions) to voice out, if this AI can keep a diary of these emotions learning from them with the aim of improving lifestyle will be great".
While most participants expressed positive sentiments toward the envisioned technology some, voiced reservations in trusting AI for parenting advice and emotional support. For instance, some participants worried about the accuracy of the advice provided by an AI system, given that it may not fully understand the nuances of individual family situation. Further, in terms of privacy, participants were keen to understand about the security measures in place to protect their data, and raised concerns around "[...]safeguarding implications. If diary entries are being recorded they cannot be kept confidential if there is a safeguarding issue. What are the protocols for this?" (P41).
Scenario 3: ParentSense.
The third set of storyboards probed parents’ attitudes towards Emma, an app based virtual assistant (or chat-bot) that delivers personalised information and support to frustrated parents (see Figure 9). The survey data suggested that participants mostly expressed a neutral-to-positive attitude towards the scenario (54/92 or 59% of participants) expressed that they could envisage using the technology within this scenario. Conversely, 41.30% of participants (38/92) indicated some level of disagreement or uncertainty in using the technology. Based on the open text field responses in the survey, we were able to explore in more detail what these moderate attitudes entailed. We grouped participant attitudes into three main themes: (1) Perceived benefits of AI; (2) Concerns about AI interference in parenting; and (3) Preference for human interaction.
Figure 9:
Figure 9: Scenario 3: ’ParentSense’ storyboards.
In terms of the perceived benefits of AI, participants acknowledged the convenience and potential advantages that the technology could offer. Namely, they listed benefits in the technology offering tips, feedback, and stress management techniques, and expressed that the child-centered features could support their parenting by being more aware of child mood, e.g., "understanding children’s mood will help me a lot in managing their needs" (P24).
However, some participants also expressed concerns over privacy. This related to the technology listening in on their parent-child conversations; (potentially) disclosing sensitive personal information; and data breaching. As seen in the quantitative data a significant portion of parents expressed negative reactions and explicit concern towards this scenario. For instance, participants expressed that by relying on an AI chat-bot, they may become fixated on external opinions, undermining their confidence as parents. Similarly, parents expressed that the chat-bot might impact on their decision-making skills, as expressed by one participant who stated: "I feel like this would get in the way of parenting. I wouldn’t be able to make decisions myself and have to rely on an app to tell me what to do" (P75). Participants also explain that they wouldn’t want to receive advice from an AI since "it is not the job of an AI to parent children. It seems like a tool for laziness" (P30). Finally, parents emphasised the importance of human interaction and support in their parenting. They explained that parents should seek help and advice from real people or experts rather than relying on AI. Across the data, participants suggested that parenting is a deeply human endeavor, and as such, "children need their parents to take them through life, not AI" (P66).
Scenario 4: HarmonyHub.
The fourth scenario envisioned a parenting platform that promotes effective conflict management, open communication, and positive family functioning by offering feedback, engagement time, parenting performance reviews, child’s point of view, and other functions (see Figure 10). Almost half of all participants expressed positive attitudes towards the envisioned technology (47.82%, or 45/92 participants), with just over half of participants (52.17%, 47/92) who expressed hesitation and disagreement. Participants’ open text responses were grouped into three main themes: (1) Positive implications of AI in resolving conflict; (2) Concerns about AI interference on family dynamics and (3) Privacy and autonomy concerns.
Figure 10:
Figure 10: Scenario 4: ’HarmonyHub’ storyboards.
Approximately one third of all participants expressed interest in specific functions displayed in the scenario (e.g., conflict resolution feature) and recognised the (potential) benefits of the envisioned technology. For instance, participants acknowledged the potentially helpful role of technology in creating a platform for dialogue, fostering greater understanding among family members, and facilitating the exchange of diverse perspectives, "I love the child’s point of view feature. I believe it’s important for kids to safely express their feelings and thoughts" (P19). Added to this, participants recognised value in having access to better conflict resolution strategies and appreciated the equitable approach of involving the entire family in this process.
However, participants also voiced concerns and hesitation about the impact of the AI interface on family dynamics. This related to uncertainty about how the system would practically operate, or what effect it could potentially have on interpersonal communication in the family – with some being "...on the fence with this" (P20), expressing doubt and apprehension about the capability of AI to deliver practical advice and support. Participants also expressed concerns regarding the potential for the technology to create disagreements or rifts among family members, "reports on each parent are going to make for divided parents" (P04). Furthermore, some participants perceived this scenario as dystopian and criticised the assumed necessity of technology to act as the intermediary during sensitive human interactions, "If conflicts cannot be resolved other than with the use of AI, then we truly live in a scary world. AI analyzing human interactions and giving advice to humans [on] how to be human? It’s like a prologue to the Terminator movie, but unfortunately in the real world this time" (P31). Finally, as in the case of scenario 1, participants expressed worry about the impact on their privacy and autonomy as parents. Specifically, participants voiced concerns about the system tracking and monitoring their parenting time, fearing it could turn parenting into a measurable task and cause unnecessary pressure to meet specific (parenting) quotas. As such, participants expressed mixed views about this scenario, with multifaceted potential benefits and concerns for the technology’s role within their family ecosystem.
5.1.3 Parent reactions to specific AI features.
In contrast to our previous analysis, where we examined parents’ perceptions of the technology at a scenario level, this section considers parent reactions to specific features within each of the nine scenarios. We identify participant perspectives relating to the specific mechanisms within the scenarios. We then identify trends in parent opinions relating to cross-cutting features across all nine scenarios (comprising 42 features total, refer to supplementary documents for full ranking) parents prioritised.
We asked participants to pick the features that they found most helpful within each of the nine scenarios. This was done to gain insights into the mechanisms that parents would find helpful, regardless of their positive or negative reactions towards the specific scenario. Surprisingly, the six features that were ranked highest aligned with the ranking of the scenarios. As seen at the table (see Table 3), the features that resonated with parents were grouped into two categories: emotion-focused (i.e., calming techniques, reflection, emotional release, child’s point of view) and personal growth/skill-development focused (i.e., personalised feedback, mastering new skills). These findings are consistent with prior research highlighting the need for supporting parents emotional [84, 250] and skill-based [122, 196] competences.
Table 3:
Feature
Description of Feature
Positive rating (N)
Calming Techniques & Suggestions
The app suggests techniques like deep breathing to empathetically manage tantrums and exercises to improve communication between parent-child.
56
Emotional Release
Speak your frustrations, fears, and worries without judgment, and experience a sense of relief through our spoken diary feature.
56
Master New Skills
Trust in your ability to grow as ParentSage provides personalized tasks, such as creating a special mealtime routine. Armed with practical advice and support, witness the transformation unfold.
55
Child’s Point of View
Channel where children are encouraged to express their feelings and thoughts on specific parenting approaches or situations. This allows parents to understand their children’s needs and preferences better.
52
Personalized & Reliable Feedback
Using an advanced algorithm and evidence-based research, our system provides objective insights that are tailored to your parenting style and challenges, helping you make informed decisions for your child’s development.
51
Reflect & Grow
Parents input their daily routines, interactions with their children, and notable events or challenges. It also includes a mood recording feature using a simple emoji-based system. Finally, guided reflection is offered to gain insights into reactions, biases, and respond compassionately.
50
Table 3: Five most favourable HCI features for parenting support across 9 scenarios (Q4).
In terms of emotion-focused mechanisms, participants expressed a variety of reasons why this was a desirable feature. For example, the majority of participants ranked the calming techniques and suggestions feature found in ParentSense (scenario 3) and emotional release feature found in SerenityGuidance (scenario 2) as most desirable (56/92 and 56/92 participants). Participants recognised the importance of having tools and systems in place that support their self-regulation skills when dealing with child-related difficulties. For instance, P82 explained that AI could be a valuable tool in supporting them during challenging situations, "the calming techniques feature is great help to manage difficult situations". They also acknowledged the importance of features that assist them in effectively expressing and managing their own emotions, "getting good emotional balance after each day’s work will help for good mental well-being" (P44). The second most preferred feature (55/92 participants) focused on skill development and mastery, e.g., found in ParentSage (scenario 1). This worked by supporting parents in accomplishing specific tasks related to their parenting objectives. Participants explained that such tools could be valuable in improving their parenting skills and expressed willingness in trying the technology, "because it offers personalized features and assists in mastering new skills, it is something I will like to try" (P39). Separately, our findings suggested that it was important to incorporate the child’s perspective when designing to support parent skill development and family dynamics. This was indicated by participant rankings of the child’s point of view feature (52/92) found in HarmonyHub (scenario 4). In particular, participants viewed this function as a means of increasing (child behavioural) understanding, facilitating family dialogue and promoting healthier interactions, "I think this may be a good idea as the whole family has input into it so can be seen as a tool for your family to resolve issues" (P29). Participants further indicated they wanted access to parent-centred functions to complement their existing parenting strategies as expressed through their ranking (51/92) of personalized & reliable feedback feature found in ParentSage (scenario 1). This worked by offering personalised guidance and practical advice. Specifically, participants cited that they often needed access to additional knowledge and guidance for improving their ways of parenting. This was conveyed by P12 who expressed: "I believe it could be useful in aiding people who have a lack of knowledge on how to be a parent. I like it as a guide". The last feature that was highly ranked by participants was the reflect and grow feature (50/92) found in SerenityGuidance (scenario 2). Participants emphasised the need to voice their frustrations, receive feedback and engage in self-reflection whilst preserving their confidence, "I like the idea of a diary that can be used to reflect and help [...]" (P61). Finally, we also observe a trend in terms of features that participants appear to dislike (which were more varied across functions). In particular, participants seem to dislike the monitoring (55/92) feature, which was consistently described as undesirable across all scenarios. Specifically, participants expressed concerns about constant AI monitoring, which they perceived as a form of surveillance, "Its listening and monitoring everything. There is no privacy" (P08).
5.2 Summary
The aim of the scenario probes was to explore with parents a set of possible socio-technical design directions with emerging AI-mediated modalities, and to identify how parents reacted to possible scenarios when designing AI assistants that might support families. Participant reactions at the level of the scenario and more specifically, at the level of the feature captured a mixed picture of both optimism and concern. Participant responses ranged from open enthusiasm about the potential benefits of augmenting and building on their existing parenting practices with AI-mediated support, to skepticism and concern relating to privacy, the AI technology’s impact on genuine or meaningful parent-child interactions, and on family dynamics.
A significant portion of participants recognised the potential advantages of AI assistants, expressing that they could envisage supports in terms of providing suggestions, reminders, and supporting positive habits, as well as acting as virtual companions, support systems or emotional relief mechanisms. Others, did not share this positive outlook with many participants expressing reservations about technology’s potential to undermine the authenticity of parenting experiences and disrupt family bonds. Participants expressed concerns about AI taking on parenting roles, potentially undermining parental authority. Privacy concerns and worries about constant monitoring and data collection further contributed to the apprehension towards (potentially) adopting AI parenting supports. Our findings highlight the need for careful consideration of the potential benefits and risks associated with AI assistants in parenting, as well as the importance of addressing privacy and ethical concerns.
6 Discussion
In our paper, we examined the possible opportunities for AI technology to be utilised in the context of designing for parenting support. This was prompted by limited prior guidance and research on how AI-enabled technologies have incorporated and prioritised different dimensions of evidence-based parenting approaches. We achieved this by first, creating a theoretically grounded, conceptual framework that incorporates different areas of parenting needs and possible technology mechanisms, based on a review of the parenting literature and discussions with psychology experts. Second, based on this framework, we then created a set of nine fictional scenarios for how AI assistants could potentially offer situated parenting support, and finally, tested these scenarios through an online survey study with parents to investigate their initial reactions to the envisaged scenarios. We found that parents expressed mixed reactions to the potential benefits of AI through the scenarios, as well as specific concerns relating to family dynamics and privacy. Through our work, we contribute i.) conceptual knowledge about the parenting need areas and technology-based mechanisms that potential AI technologies for parenting should consider, and ii.) empirical research on parental reactions to the envisaged future scenarios. Focusing on these two contributions, we discuss how our findings raise important considerations for what AI-enabled parenting support has focused on to date and what this means for future research considerations in this area.
6.1 Research Agendas for AI and Parenting Support
Prior work that has explored designing for AI-enabled mental health support has focused on the possibility of using AI and NLP when designing for population-level mental health interventions [134, 307], as well as targeted areas such as diagnosis and risk detection [106, 254, 257, 343]. However, one focus of prior work to date has been on studying the technical feasibility of these technologies with less consideration for the real-world clinical impact [2, 78, 129]. Consequently, there has been a disconnect between AI development and understanding the intended impact on people in the context of their everyday practices. In the parenting context, some prior research has taken a user-centred approach, which has been helpful for advancing understanding about some of the challenges with delivering effective parenting support [109, 197, 269, 335, 337]. For example, focusing on the delivering of situated support, low parental engagement and a lack of personalisation of existing parenting programmes [28, 103, 176, 302, 323]. However, our study has shown that designing for parenting intervention supports is complex, as it is not always easy to identify the components that can be most effectively supported by technology [168, 211]. This is because individual components (e.g., reflection, self-monitoring etc.) do not always clearly align with technology-enabled delivery mechanisms (e.g., feedback, decision making etc.). Through our conceptual work, we have shown that as well as a need for a user-centred approach, there are a breadth of parenting need areas that are yet to be considered. For instance, prior studies have focused on supporting specific application areas that include: parent-child communication [35, 141, 197, 268, 329], child and adult emotional states [8, 188], and specific daily practices (e.g., motivating children to tidy their rooms, reading habits and specific learning goals) [31, 133, 142, 239, 303, 352]. However, our research suggests that within each of these and many other application areas, AI research should also consider the design of parenting supports on a more granular level. For instance, when designing for parent-child communication, future designs should build in ways of supporting parents with their skill development and maintenance across a range of dimensions that include fostering reflection, self-monitoring, knowledge acquisition and implementation and other areas of need. Our framework is a conceptual model that can help HCI and interaction design researchers to envision what AI assisted parenting support might look like across a range of dimensions. Its primary focus is to guide thinking around the design of emerging technologies that can support the acquisition of parenting knowledge and skill development through the integration of emerging technologies. The framework is not intended to be prescriptive or rigid in nature, but offers ideas for building on current work on AI and parenting through an explanatory lens.
6.2 Parent Perceptions and Implications
Our findings highlighted the importance of considering human factors early on by identifying how AI is interpreted by parents, and how these interpretations might influence their uptake. Based on parent reactions to the scenarios, our findings showed that AI is only one component of a much larger socio-technical system. Implementing AI in families means prioritising parents’ expressed needs as priorities for the ways that AI can and should offer support. That is to say, just because an AI solution is possible does not mean an AI solution is needed. In what follows, we examine potential opportunities and challenges based on what participants in our study expressed. Specifically, based on parent feedback, participants saw value in functions that can provide personalised support, emotional support, and conflict resolution; suggesting that designing to support healthy communication within families is an important direction. However, participants also expressed reservations about AI being fully embedded in–and taking over some actions within–the family ecosystem. For example, participants often interpreted the agents in the scenarios as excessively intrusive and expressed concerns about their potential interference with their personal parenting choices. Consequently, it may be more appropriate to position AI supports as systems that deliver alternative perspectives on parenting matters rather than imposing decisions. Similarly, participants also expressed concern over the trustworthiness of AI systems due to the AI’s limited understanding of the nuances of individual family situations; and doubt about AI’s ability to provide valuable parenting advice. To this end, the transparency and trust of AI systems should also be viewed as a part of the design process [318]. Our participants also expressed concerns regarding potential negative outcomes that might arise from the AI assistants featured in our scenarios. Some participants suggested the negative outcomes they envisaged could likely outweigh the potential benefits. In particular, parents were less accepting of scenarios that included agent roles as surrogates and more accepting of scenario designs with empathetic listening assistants. This builds on prior work that has considered agent roles and how people are motivated to use these technologies, and what people perceive about the technology’s expertise and intelligence [253, 321, 347]. Our findings suggest that future designs should carefully consider the kinds of roles and responsibilities we assign to AI assistants as this could determine how likely the technology is to be adopted and what the implementation and outcomes could look like. Lastly, parents also expressed concern about AI potentially undermining parental authority and altering family dynamics. This was evident in examples where parents objected to the use of AI as an intermediary in situations where two parents might hold different opinions. Considering prior research that has started to consider how AI might ’recognise’ or attribute mental states (e.g., in the context of studying artificial Theory of Mind) (c.f., [203, 204, 216, 348, 358]) or predict behavior based on mental states, caution is needed in the parenting design domain, given the need for parent agency. As such, rather than taking a role in decision-making, AI could amplify [319] or complement the effective strategies that parents are already using to support them in decision making about inter-parental relationships or parent-child interactions.
7 Limitations and Future Work
Several limitations of this work should be noted. We used Prolific as a first step in identifying future directions for AI assisted parenting visions. By using a single recruitment platform and limiting participation to people in the U.K., the perspectives represented in our study are limited to those of a relatively small number of people, and may not reflect those of demographics not represented in our sample. In addition, the implications of using a narrow sample, who are paid to take part in research responding to web-based surveys may be more accepting of technological interventions than parents who do not respond to online surveys, resulting in selection bias. As such, additional work is needed that involves a wider-demographic of parents to a get a more holistic perspective of parent attitudes towards AI assisted parenting technologies.
Second, we deliberately created conflicts in our designs to balance unforeseen friction [18]. Specifically, our scenarios included provocative elements (e.g., depicting technology as a surrogate) and positive undertones (e.g., positive reviews of other parent perspective) to elicit varied reactions [52]. Consequently, our findings showed that this approach effectively prompted both negative and positive reactions from the participants while also invoking their thought processes, values, and needs. However, we acknowledge that the inclusion of mostly positive fictional user reviews may have influenced participant judgement of the scenario (which we also have discussed in the findings). To address this, future work could include more impartial reviews from existing rating platforms (e.g., trust-pilot or google reviews) and incorporate negative, neutral, and positive reviews across the scenarios.
Finally, there are several ways this study can be extended in the future. First, future work might expand one or more design concepts (as ranked by participants) and conduct an additional survey study that reflects iterated scenarios based on parents sentiments to identify further opportunities and limitations. Second, many participants expressed concerns toward AI assisted parenting. To better understand why parents felt this way, engaging in co-speculation activities might provide further insight as well as uncover additional opportunities and concerns. Third, while our findings provide some indications on parental perceptions of AI assistants, additional research is needed to explore how we can help parents understand and use information provided by AI as to support their parenting efforts. To investigate this in context, a complementary qualitative study using situated methods could provide further insights, validate, or modify some of our findings.
8 Conclusion
In our paper, we examined the possible opportunities for AI technology to be utilised in the context of designing for parenting support. We used design fiction as an accessible vehicle through which participants experienced future design concepts and articulated their own views of the future of AI assisted parenting. Our goal was to identify the possibilities and implications of these emerging AI technologies (i.e., NLP) that might support families. Our findings identified mixed reactions to the potential benefits of AI as well as specific concerns relating to family dynamics and privacy. Our paper contributes to the field of HCI in two ways. First, it provides conceptual knowledge about the parenting need areas and technology-based mechanisms that potential AI technologies for parenting should consider. Second, it offers an empirical contribution through parental reactions to the envisaged future scenarios.
Acknowledgments
We would like to thank our participants for their time and the deep engagement with which they took part in the research process. This work was supported by the Engineering and Physical Sciences Research Council [grant number 2552175]; and a UKRI Future Leaders Fellowship [grant number MR/T041897/1].
A Conceptual Framework
A.1 Identifying parenting needs
Reflection.
The ability of a parent to understand both their own and their child’s mental states is considered crucial within the parent-child relationship [85, 325]. Reflecting on one’s actions enables the development of desired behaviors, emotional control, and empathetic responses [14, 16, 118, 325]. Through reflection parents get empowered to recognise when their child’s behavior deviates from normative development, respond respectfully to the child’s feelings and behaviors, and nurture their emotional well-being and resilience [105]. However, there are varying levels of reflective competences [310] and ensuring that parents with different backgrounds, experiences, and abilities can engage in meaningful reflective practices remains a challenge [187, 300].
Self-monitoring.
Training parents in self-management (i.e., self-monitoring, goal setting and problem-solving) can prove instrumental in supporting positive family functioning [297]. In particular, self-monitoring enables parents to (self) regulate through systematic observation and tracking of their feelings, reactions, behaviours, thoughts and parenting activities [184, 297]. Monitoring can provide a simple means to track progress (e.g., measuring parents responses to child behaviour, overall outcomes and family functioning) [46, 136] as well as readiness for different stages of intervention (e.g., the introduction of specific strategies to support regulation) [251, 294]. In this way, the promotion of effective parenting skills and the modification of parental behaviour can be established through supporting parents in adopting a self-regulatory approach [16, 294].
Knowledge Acquisition and Implementation.
Parenting knowledge is essential for effective parenting (e.g., [30, 56, 170, 246, 360]) and it is positively associated with the quality of parent-child interactions, parental engagement in promoting children’s well-being and development, and the ability to make informed decisions[238]. Acquiring knowledge—about child development and parenting strategies—helps parents communicate effectively with their children, set appropriate expectations, and respond to challenging behavior [149, 170, 172, 264]. Lastly, by gaining knowledge, applying it in their parenting practices, and witnessing positive outcomes – parents’ self-efficacy and confidence in raising children improves [177].
Practice for Skill Acquisition and Transfer.
The efficacy of parent training for skill acquisition is evident throughout the literature (e.g., [121, 181, 231, 332]). Through regular practice (e.g., homework) and application of skills parents develop a repertoire of responses to various parenting challenges (e.g., positive attention, limit setting, communication, discipline, and problem-solving) becoming more proficient in their interactions with their children [140, 182, 248, 308, 342]. However, to encourage adult learning, it is crucial to provide adults with opportunities to apply the information they are taught [224]. This includes supporting parents in consistently practicing and applying skills beyond the training context, reinforcing their use in daily life and transferring them to various situations [86, 122, 334].
Dealing with setbacks.
Parenting rarely goes as planned [94]. Experiencing adversity in parenting creates obstacles that can disrupt parenting abilities, increase frustrations, parent stress levels as well as child behavioral difficulties[23, 139, 331]. Teaching parents how to deal with setbacks or adversity can substantially increase parents’ sense of competence, satisfaction (in their parenting) and resilience [44]. Training parents to be resilient—the ability to “bounce back” after a setback—means supporting them recognise that skills used in the past (to overcome setbacks) can be applied to future challenges [55, 223, 223, 311]. In this way, increasing preparedness towards possible setbacks is a way to compensate potential setbacks [289, 339].
Communication.
Family communication and socialization processes provide the primary mechanisms through which children develop social competencies [193, 194, 306]. The communication and skills children acquire through the family ecosystem (or fail to acquire) will have an impact in the quality of their interpersonal relationships throughout their life [192].Parent training in communication support strategies is common in interventions (e.g., [4, 232, 295]), as effective communication helps parents understand and respond appropriately to their children’s needs, emotions, and behaviours [201]. Despite the emphasis on improving the parent-child relationship in many interventions [91, 132], supporting positive communication practices between parents (i.e., jointly setting parenting goals, reducing misunderstandings, minimizing potential conflicts) is equally important in creating a positive family environment [53, 96, 158, 234, 236].
In the moment support.
The extension of behavior change from the training setting to new situations and circumstances is a universal goal of PT interventions [40, 231, 255]. Research has demonstrated the efficacy of PT programs however, when examining the extent to which those skills generalize to the home or the effect that they have on child behavior have not always proven successful – especially in long-term follow-ups [61, 316]. As such, explicitly training parents "to generalise" (i.e., instructing parents to use the skills in everyday situations) and reinforce the occurrence of generalised performance is necessary [86, 334]. In this way, supporting parents in the moment through the design of situated interventions [326] will remove the barrier of transfer and instead, support natural learning opportunities that fit into everyday routines [82, 326, 355].
Habit Formation.
Supporting parents in establishing family routines helps structure children’s environment whilst creating stability (e.g., establishing bedtime routines to reduce sleep problems) [131]. This helps children develop self-regulatory skills by teaching them that events are predictable and there are rewards for waiting [153, 272]. Finally, habits established in childhood can influence a child’s lifelong behaviors, shaping their approach to responsibilities and relationships [261].
Conflict Resolution.
Marital conflict is typically categorised as destructive or constructive [95, 233]. Destructive conflict (e.g., violence, aggression, and threats) leads to children’s negative emotions and (potential) adjustment problems due to lack of coping skills [98, 233]. In contrast, constructive conflict (e.g., promoting positive resolution and communication) fosters happiness and security in children, enhancing their problem-solving abilities and positive adjustment [97, 148, 155, 233]. Research consistently indicates that destructive conflict heightens the risk of child adjustment issues, while constructive conflict improves positive development [119, 263]. Therefore, training parents to resolve conflict constructively will prove beneficial in supporting the development of children’s adjustment, social skills and overall well-being [166, 336].
A.2 Identifying the role for technology
Psychoeducation.
Parenting education programs typically seek to shape parenting attitudes, beliefs, and practices [182] by empowering parents with knowledge for informed decisions about their children’s health, education, and well-being [271, 287]. Understanding preferences among different parent groups (e.g., higher vs. lower Socioeconomic status (SES)) for parenting information sources is needed to enable tailored delivery [34, 241, 298]. In addition, parenting information can be accessed via digital media and internet applications providing up-to-date content and visually transmitted information delivered through various channels (e.g., podcasts and online forums) increasing parents’ self-efficacy and decision-making abilities [35, 66, 162, 275, 338, 346]. However, significant barriers persist in delivering the right information to the right people at the right time [127].
Feedback.
Feedback is a component used in Behavioral parent training (BPT) and includes techniques like modeling, reinforcement, and correction to increase positive parenting behaviours [121, 312, 313]. BPT programs typically use delayed feedback (i.e., during sessions when parents report attempts to practice skills at home); but in-vivo feedback during training sessions (i.e., therapist observes parent-child interaction and provides immediate feedback via earpiece) [89, 299] or video-feedback (i.e., filming parent–child interactions and reviewing the video with the parent) [178, 179] are also utilised. Despite positive effects in enhancing parenting and attachment [173], these approaches carry limitations in increasing access due to their personalised nature (e.g., video recordings of specific parent-child interactions) and reliance on expert support (e.g., interventionists or therapists actively participating in improving parenting behaviors) [173, 178].
Parent Surrogate.
An alternative solution to (parenting) support may be represented by assistive technology such as artificial companions (ACs) or social robots (i.e., devices that can perform various tasks at home, including daily communication, information exchange, and sharing intimate feelings) [137, 206]. These intelligent companions engage users by performing various roles, including nanny, nurse, peer, facilitator, or caregiver (e.g., [8, 150, 154, 229, 237, 266, 283, 364]). Research has explored their application in parent-child communication [364, 368], entertainment [180], and maintenance of children’s behavioral records [317]. In this way, we explore how ACs (e.g., chatbots, robots or conversational agents) might support parenting. For instance, technology could act as a "surrogate" and automate specific low-level parenting tasks (e.g., provide homework support and meal planning [9, 320]) to relieve parental pressures. Further, AC’s can improve children’s motivation and interest in learning [159, 228], encourage children to engage actively in communication [5], support the development of emotion regulation practices [326, 344] and consult with children about their concerns [43].
Decision Making Support.
Caring for a child’s well-being and making decisions demands significant effort from parents. Parents are confronted with many decisions including how, when, and where to seek support [69, 80, 93, 242]; which strategies to use to effectively to address the issue at hand [167, 198]; as well as agreeing on parenting approaches between parents [94]. While some decisions can be anticipated and planned for, unexpected situations can intensify the emotional challenges parents face [32, 252]. As such, supporting parents deliberative decision making by taking into account their preferences, sharing knowledge and skills is pivotal in fostering effective child rearing practices [145, 163, 164, 287].
Prediction to explore possible outcomes.
Parenting and parent characteristics predict childrens’ problem behavior [7, 64, 101, 260]. Several models to predict parenting behavior have been developed (e.g., [10, 26, 27, 33, 68, 259, 265, 324, 369]) which (in most cases) suggest the determinants of antisocial and aggressive behavior in children. However, to accurately facilitate prediction and prevent parenting behaviours—that contribute to child maladjustment—we need to incorporate various data sources and develop models of the determinants of parenting. For instance, mobile and sensor-based technologies, have been utilised to encourage positive behaviors through monitoring, detection, and (in-situ) feedback [47, 156, 174, 195, 199, 202, 214, 227]. Educational data are being used to predict students at risk and for targeting particular types of intervention strategies [327]. Finally, supervised ML could be used with parent data to automatically predict child mental-health outcomes [75].
Coaching.
Coaching parents during parent-child interactions is an important mechanism of change [313]. A meta-analytic review by Kaminski et al. [182] showed that parent training programs that included coaching produced higher levels of positive parenting behaviors and lower levels of child externalizing problems than programs without those components. Coaching utilises a more non-directive approach in supporting parents behavioural changes [138] treating parents as collaborative partners rather than passive recipients of expert advice [288, 341]. Specifically, the coach encourages the learner’s (parent’s) capacity to engage in self-reflection, thus enabling them to evaluate the effectiveness of their actions and practices [345]. Additionally, this process facilitates the development of a plan for refining and utilising said actions in both present and future scenarios [104, 256].
B Graphs
Figure 11:
Figure 11: Parent rating of their perceived difficulty in understanding the scenario (Q3)
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