Overview of Children's Readiness in Mathematics Learning Using AI

Setiyo Utoyo

, Ismaniar Ismaniar

, Nur Hazizah

, Elsy Assari Putri and Sri Cahaya Sihombing

Department of Early Childhood Education, Faculty of Education, Universitas Negeri Padang, Indonesia

Keywords: Artificial Intelligence, Mathematics Learning, Child Readiness, Early Childhood Education.

Abstract: The challenge of mastering mathematics learning is a phenomenon experienced by every early childhood

student, therefore innovation is needed in the implementation of learning, both in terms of media, methods,

and learning approaches. The advancement of artificial intelligence (AI) technology provides new

opportunities for learning innovation, including in mathematics learning in early childhood education. This

study aims to describe children's readiness to participate in AI-based mathematics learning. The study

employs a qualitative descriptive approach with subjects being children aged 5–6 years at an early childhood

education institution that has implemented an AI-based mathematics learning application. Data were collected

through observation, semi-structured interviews with teachers and parents, and documentation. The results of

the analysis show that children's readiness covers three main aspects: cognitive, social-emotional, and

technological. Children show enthusiasm and interest when interacting with AI, but need guidance in

understanding instructions and using the device. These findings indicate that adult involvement is very

important in optimizing the AI-based learning process and ensuring an effective, adaptive, and enjoyable

learning experience for children.

1 INTRODUCTION

1.1 Background

The development of digital technology, particularly

artificial intelligence (AI), has had a significant

impact on the world of education, including early

childhood education. AI is now being used to support

game-based learning, intelligent tutoring systems,

and conversational agents capable of responding to

children's language. These changes not only create

innovations in learning media but also require

students to be prepared to respond to such technology.

In this context, several researchers emphasize that AI

is not merely a tool but a learning partner capable of

fostering active two-way interactions with children

(Su, Ng, & Chu, 2023; Turmuzi & Tyaningsih, 2025;

Wu, 2024). These three studies collectively show that

the success of AI implementation in early childhood

education is highly dependent on children's readiness

https://orcid.org/0009-0004-7093-6864

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https://orcid.org/0009-0007-3411-403X

to understand, respond to, and interact with AI-based

digital systems.

Although AI technology offers great potential for

enhancing early childhood mathematics learning

experiences, its implementation does not always go

smoothly in practice. Many studies highlight that

children's readiness to engage with AI has not been a

primary focus, despite its critical role in determining

the effectiveness of learning. Some challenges

include limitations in understanding digital

instructions, lack of technical support from the

surrounding environment, and disparities in digital

infrastructure. In this regard, studies by Solichah &

Shofiah (2024), Honghu, Ting, & Gongjin (2023),

and Wu (2024) underscore the importance of research

that not only focuses on the effectiveness of tools but

also describes the actual conditions of children's

readiness in the context of AI use. These three studies

confirm the need for a descriptive approach that

comprehensively explores aspects of children's

readiness, from perception to behavior when

interacting with digital media.

Utoyo, S., Ismaniar, I., Hazizah, N., Putri, E. A. and Sihombing, S. C.

Overview of Children’s Readiness in Mathematics Learning Using AI.

DOI: 10.5220/0014069700004935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 7th International Conference on Early Childhood Education (ICECE 2025) - Meaningful, Mindful, and Joyful Learning in Early Childhood Education, pages 177-182

ISBN: 978-989-758-788-7; ISSN: 3051-7702

177

Children's readiness for AI-based learning also

includes cognitive, social-emotional, and

technological dimensions, which interact with each

other in determining the success of the learning

process. Children need to have basic skills in

understanding mathematical symbols, a positive

attitude towards technology, and the ability to operate

devices independently or semi-independently. In

practice, various applications have been developed to

address these needs, such as Finnger, smart tutors,

and AI-based voice agents. Studies by Audibert &

Maschio (2021), the AI Voice Agent team (2023),

and Mdpi-Sciencedirect (2023-2024) indicate that

children respond positively to AI-based media

designed according to their developmental

characteristics. From these three studies, it can be

concluded that child-friendly interface design, real-

time feedback, and multimodal interaction are key

factors in enhancing children's readiness to engage in

AI-based mathematics learning.

In addition to children's readiness, the success of

AI integration in learning also depends on the

readiness of teachers and institutions as providers and

facilitators of the learning process. Teachers need to

understand how AI systems work, prepare

appropriate content, and have a positive perception of

their use in learning. Support from educational

institutions in the form of training, policies, and

infrastructure also plays a crucial role. Research by

Rokhman et al. (2025) and Kong (2024) shows that

teachers' self-efficacy, technological support, and

perceptions of the benefits of AI are key determinants

of their readiness to integrate this technology into the

classroom. Both studies indicate that children's

readiness to use AI cannot stand alone but must be

understood as part of a learning ecosystem that

includes adult support, teachers, and a responsive

environment.

The development of artificial intelligence (AI) in

early childhood education continues to show a

progressive trend. AI not only serves as a learning aid

but has also evolved into a system capable of

responding and adapting learning to a child's abilities

in an adaptive manner. In the context of mathematics

education, AI is used to introduce basic numeracy

concepts, patterns, and logic in an interactive and

visual manner. Studies by Kurian (2025), Liu et al.

(2025), and Marzano et al. (2025) indicate that AI

designed with an expressive, multimodal, and

personalized approach can support children's learning

processes in a more meaningful and developmentally

appropriate manner, while also fostering

technological literacy from an early age.

However, amid the rapid integration of AI in

children's education, the fundamental issue lies in the

lack of attention to children's readiness itself. Many

studies focus on the effectiveness of AI in improving

learning outcomes but have not detailed how

children's readiness—cognitively, socio-emotionally,

and technologically—to respond to such technology.

This creates a gap between AI's potential and

classroom practice, particularly among younger age

groups who remain highly dependent on direct

guidance and stimulation. Findings from Honghu,

Ting, & Gongjin (2023), Neugnot-Cerioli & Laurenty

(2024), and Wu (2024) collectively underscore the

importance of field-based studies that describe

children's readiness for AI-based learning in a

comprehensive and contextual manner.

Based on this background, this study aims to

describe the readiness of early childhood to

participate in artificial intelligence-based

mathematics learning. The study focuses on three

main aspects: cognitive readiness (the ability to

understand instructions and basic mathematical

concepts), social-emotional readiness (enthusiasm,

self-confidence, and adaptation to digital media), and

technological readiness (the ability to use devices and

respond to interactions with AI systems). This study

does not aim to measure learning outcomes but rather

to provide a factual mapping of children's readiness

through a descriptive approach. This objective aligns

with the push to expand studies on the readiness of

young users in the context of learning technology, as

emphasized by Marzano et al. (2025) and Liu et al.

(2025) within the framework of AI integration in

child-centered education.

The urgency and contribution of this research lie

in its effort to bridge the gap in the literature, which

has traditionally focused more on the technological

aspects than on children's readiness. By providing a

factual picture of children's readiness, the findings of

this study are expected to serve as an important

foundation for teachers, media developers, and

policymakers in designing humanistic and contextual

AI-based learning approaches. Furthermore, this

research contributes theoretically to the development

of a model of children's readiness in digital learning

environments, as well as practically to strengthening

adaptive teaching strategies in the era of artificial

intelligence. This study emphasizes the need for an

educational approach that not only prioritizes

technological sophistication but also aligns with the

developmental needs of young children (Wu, 2024;

Neugnot-Cerioli & Laurenty, 2024; Kurian, 2025).

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1.2 Problem Statement

Based on the background, the development of AI in

early childhood education brings both opportunities

and challenges, especially in the context of children's

readiness as active users. Mathematics learning that

uses AI-based technology requires children to have

not only cognitive abilities but also social-emotional

and technological abilities. However, the limited

research mapping children's readiness to respond to

AI-based learning systems necessitates descriptive

research to describe the actual conditions in the field.

Therefore, the problem statement in this study is:

“What is the picture of early childhood readiness to

participate in AI-based mathematics learning from

cognitive, social-emotional, and technological

aspects?”

2 LITERATURE REVIEW

2.1 The Development of AI in Early

Childhood Education

AI in early childhood education has evolved toward

personalized and adaptive learning systems, enabling

interactions tailored to each child’s learning style.

Technologies such as virtual tutors, educational

chatbots, and adaptive learning platforms provide

mathematics content tailored to children’s abilities in

real-time. Xu (2024) states that while AI can support

children’s development, its effectiveness heavily

depends on designs grounded in appropriate learning

principles. In addition, a review by Honghu, Ting,

and Gongjin (2023) illustrates that AI technology

applied in a multimodal manner can increase

children's engagement and motivation globally

(Honghu, Ting, & Gongjin, 2023).

On the other hand, Isaacs et al. (2025) highlight

that AI-based learning platforms that combine audio,

images, and direct interaction can strengthen

children's digital literacy while expanding the

possibilities for cognitive stimulation from an early

age (Isaacs et al., 2025). The results of an evaluation

of adaptive programs in the United States show that

children from low-income families who use adaptive

digital mathematics programs demonstrate significant

improvements in basic numerical understanding

(Study, 2020). These findings confirm that AI

designed to be inclusive and accessible can provide

equal educational benefits to children from diverse

backgrounds.

While many studies assess the effectiveness of AI

on learning outcomes, empirical studies that

explicitly evaluate children's readiness as technology

users remain scarce. Neugnot Cerioli and Laurenty

(2024) emphasize that children's early interactions

with AI can influence cognitive and socio-emotional

development, yet the empirical literature on readiness

in the context of early childhood education remains

incomplete. Kristiansen et al. (2025) also note that

most global research emphasizes learning outcomes

rather than exploring the psychological and technical

readiness of young children.

Furthermore, Wah Kok Cha & Daud (2025)

observed that the use of AI without training and

guidance appropriate to the stage of development can

actually cause stress, frustration, or incompatibility in

children's learning processes. Similar research in

Ghana on the use of AI tutors (Rori via WhatsApp)

mentions that although there was an improvement in

mathematical performance, the impact on children's

cognitive readiness and technological adaptation still

needs further exploration (Henkel et al., 2024). This

condition makes a descriptive approach very relevant

to understand the overall readiness of children.

2.2 Research Objectives

This study aims to describe the readiness of young

children in AI-based mathematics learning, focusing

on three main dimensions: cognitive, social-

emotional, and technological. This approach allows

for mapping the actual conditions of children as AI

users, rather than merely assessing learning

outcomes. AI readiness instruments developed in

several European and American studies highlight the

importance of integrating technological literacy,

psychological well-being, and digital empathy in

assessing children's readiness (Study on AI readiness,

2023). By understanding readiness across these three

dimensions, this study aims to provide insights into

the factors influencing children's adaptation to AI.

A longitudinal study in Tanzania (2023) shows

that home environment and socioeconomic status also

determine children's mathematical readiness from an

early age (Svenson et al., 2023). Additionally,

Harvard-MIT research emphasizes the importance of

field experiments in understanding children's

mathematical learning readiness across various

economic and cultural contexts (Spelke et al., 2017).

The objectives of this study were then formulated to

include a learning framework responsive to local and

global contexts, taking into account environmental

variables, technical readiness, as well as children's

cognitive and emotional readiness before using AI in

Overview of Children’s Readiness in Mathematics Learning Using AI

179

learning.

2.3 Urgency and Contribution of

Research

The urgency of this research is based on the fact that

around 30% of children aged 0-8 years have used AI

for learning, but there is still very little academic

literature on their readiness (Common Sense Media,

2025). This creates a gap between the widespread use

of technology and scientific understanding of

children's psychological and technical readiness to

use it effectively. Without proper readiness

measurements, the implementation of AI in children's

education could lead to learning disparities and

unforeseen developmental impacts.

Conceptually, this research contributes to

enriching the literature on AI literacy and digital

intelligence in young children by incorporating a

holistic dimension of readiness. Practically, the

research results are expected to serve as a foundation

for educational media developers, teachers, and

policymakers in designing AI-based learning

approaches that are adaptive to children's needs.

Thus, this study becomes an important reference in

the design of balanced digital education policies that

align technological innovation with children's

developmental needs.

3 RESEARCH METHOD

3.1 Approach and Type of Research

This study uses a qualitative approach with a

descriptive research type. The purpose of this

approach is to systematically, factually, and

accurately describe the readiness of early childhood

to participate in artificial intelligence (AI)-based

mathematics learning. The descriptive approach was

chosen because it is suitable for identifying,

understanding, and describing the phenomenon of

children's readiness in depth based on naturalistic

observations and participant responses. In line with

the opinions of Creswell and Poth (2018), the

qualitative approach allows researchers to explore the

subjects' experiences comprehensively in a real and

dynamic context.

3.2 Research Subjects and Location

The subjects in this study were children aged 5-6

years (group B) who participated in a mathematics

learning program using AI-based media at an early

childhood education institution in West Java,

Indonesia. Subject selection was conducted

purposively with criteria for children who were

already accustomed to using digital devices in

learning activities, as well as institutions that had

integrated AI technology as part of their learning

methods. The involvement of teachers and parents as

supporting informants was also an important part of

data collection.

3.3 Data Collection Techniques

Data was collected using three main techniques: (1)

participatory observation of children's learning

activities with artificial intelligence (AI) media, (2)

semi-structured interviews with classroom teachers

and parents to explore their perceptions and support

for children's readiness, and (3) documentation in the

form of photos, videos, and daily learning notes. The

observations aimed to identify indicators of children's

readiness in cognitive, social-emotional, and

technological aspects, such as responses to AI

instructions, concentration while playing, and the

ability to use devices independently.

3.4 Data Analysis Techniques

Data were analyzed using Braun and Clarke's (2006)

thematic analysis technique, which consists of six

stages: (1) familiarization with the data, (2) initial

coding, (3) theme search, (4) theme review, (5) theme

definition and naming, and (6) report writing.

Observation and interview results were coded

inductively to identify recurring patterns of children's

readiness. Data validity was strengthened through

source triangulation (teachers and parents), technique

triangulation (observation and interviews), and

member verification to ensure that data interpretation

aligned with participants' experiences.

3.5 Research Ethics

This study prioritizes ethical principles, including

obtaining informed consent from parents and schools,

maintaining the confidentiality of children's

identities, and ensuring that all documentation

processes are carried out without harming or

disturbing the children's comfort. Researchers also

ensure that the use of technology in observation does

not interfere with the children's natural learning

process.

Algorithms and Listings captions should be

properly numbered, font size 9-point and no bold or

italic font style should be used. Captions with one line

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should be centered and if it has more than one line

should be set to justified.

4 RESULTS AND DISCUSSION

4.1 Children's Cognitive Readiness in

AI-Based Mathematics Learning

Children's cognitive readiness in AI-based

mathematics learning is evident in their ability to

understand instructions, recognize symbols, and

respond to visual challenges provided by the

application. Based on observations, children showed

enthusiasm when interacting with AI that presented

simple arithmetic problems in a gamified manner.

Some children can understand number sequences and

basic patterns with the help of voice prompts or

digital animations. A study by Sullivan et al. (2021)

shows that the visual and multisensory

representations of AI systems support children's

cognitive processing at the preoperational stage

(Sullivan, Bers, & Mihm, 2021).

However, there is still variation in adaptation

speed among individuals, especially when

mathematical tasks require an understanding of

abstract relationships. Children with prior digital

experience tend to be faster in absorbing basic logic

such as visual addition or recognizing geometric

shapes. Conversely, children who are new to digital

devices require more repetition and assistance from

teachers. This is consistent with the findings of Green

et al. (2023) that cognitive readiness in a digital

context is influenced by prior experience and

exposure to technology from an early age (Green,

Ramirez, & Olmstead, 2023).

4.2 Children's Social-Emotional

Readiness for Interaction with AI

Children's social-emotional aspects play an important

role in determining their success in using AI

technology as a learning tool. Observation results

show that most children display expressions of joy

and curiosity when first using AI-based math

applications. They tend to actively try, explore, and

imitate AI instructions, especially when the AI

responds with appreciative sounds or animations.

This is in line with research conducted by Breazeal et

al. (2022), which found that children's interactions

with empathetic AI can foster self-confidence and

increase learning engagement (Breazeal, Slaney, &

Jeong, 2022).

However, there are also some children who feel

intimidated when AI responses are too fast or when

mistakes are corrected automatically. This creates

pressure for children with higher emotional

sensitivity. Support from teachers and peers has been

shown to reduce tension and help children cope with

frustration. Research by Rader et al. (2020)

emphasizes that the right social approach to

technology use helps children develop emotional

resilience and problem-solving skills (Rader, Cotter,

& Simon, 2020).

Children's Technological Readiness in Using AI

Media

In terms of technology, children's readiness is

assessed based on their ability to operate digital

devices, follow voice/image-based instructions, and

navigate visual displays on AI applications. The

majority of children in this study are already familiar

with the use of tablets or smartphones, enabling them

to perform basic interactions such as selecting

answers, adjusting volume, or restarting games

independently. Children appear to demonstrate high

autonomy in exploring interactive and game-based

math applications. This is supported by the findings

of Lovato and Piper (2015), who noted that

preschoolers can demonstrate high navigation skills

when applications are designed according to child-

centered design principles.

However, technological readiness is also

influenced by access to and habits of technology use

in the home environment. Children from families

with healthy digital habits tend to be more critical and

cautious in using applications. Conversely, children

who are accustomed to watching without supervision

tend to be passive when using AI in an educational

context. This reinforces the view of Livingstone and

Blum-Ross (2020) that the role of parents in

mediating technology use is crucial in determining

the quality of children's learning experiences

(Livingstone & Blum-Ross, 2020).

5 CONCLUSIONS

The results of the study indicate that the readiness of

young children to participate in AI-based

mathematics learning encompasses three main

dimensions: cognitive, social-emotional, and

technological. Cognitively, most children are able to

understand basic instructions, recognize symbols, and

solve numerical challenges with the help of

interactive visual and audio aids. Interaction with AI

enables children to learn actively and contextually,

although differences in digital experience levels

result in variations in the speed of understanding.

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181

In terms of social-emotional aspects, children

demonstrate high enthusiasm and curiosity toward

AI-based media, but still require teacher support to

overcome frustration when facing challenges.

Technologically, children are sufficiently prepared to

operate devices independently, especially if the

application interface design aligns with the

characteristics of early childhood development.

These three aspects of readiness interact with one

another and determine children's success in accessing

and responding to technology-based mathematics

learning.

ACKNOWLEDGEMENTS

The authors would like to express sincere gratitude to

the research team for their dedication and

collaborative effort throughout the study. Special

appreciation is extended to the early childhood

education teachers in Padang City for their invaluable

participation, insights, and support during the data

collection process. The constructive comments and

suggestions from colleagues and reviewers are also

gratefully acknowledged.

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