Decomposing the Digital Divide: Family Background,Information and Communications Technology,and the Stratification of Creative Thinking in Asia-Pacific Education

Abstract

As global education trends increasingly emphasize 21st-century competencies, curriculum and learning reforms are emerging, potentially exacerbating disparities in students’ thinking abilities, termed the “thinking gap,” a profound manifestation of digital inequality. Educational informatization, leveraging Information, and Communications Technology (ICT), stands as a pivotal strategy in bridging these gaps and fostering equitable resource distribution. Using data from the 2022 Program for International Student Assessment (PISA), this study examines how family background and ICT relate to students’ creative thinking. Through multiple linear regression and Shapley value decomposition, we find that both factors are positively associated with creative thinking. Notably, a student’s confidence in using technology (ICT self-efficacy) emerges as a key factor, substantially mitigating the negative association between a disadvantaged family background and creative thinking. This research provides empirical evidence on the interplay between home environment and technology in fostering creative thinking. It offers practical insights for policymakers and educators aiming to promote educational equity in the digital age, while its cross-sectional design warrants caution in making causal claims.

Plain Language Summary

How Do Family Background and Digital Tools Shape Students’ Creativity? Insights from a Global Study on Education Inequality

As schools increasingly use digital tools, creativity gaps between students from different family backgrounds may widen. Can technology help level the playing field? Analyzing data from students, this study reveals: 1. Family Resources and Digital Confidence Both Matter. While students from wealthier families tend to have higher creativity scores, what can truly make a difference is a student’s confidence in their own technology skills. 2. Tech Confidence is a Powerful Equalizer. Students who feel confident using technology score significantly higher in creativity. Importantly, this “I can do it” mindset appears to substantially reduce the creativity gap linked to a disadvantaged family background, acting as a powerful protective factor. 3. Steps for Schools and Policymakers. Focus on building tech confidence through hands-on activities, not just providing devices. Prioritize support for students from lower-income families to help them develop the skills and confidence to thrive in a digital world. The findings highlight that closing educational divides requires teaching students how to use technology effectively. These insights offer practical guidance for creating fairer learning environments.

Keywords

digital divide creative thinking disparities Shapley decomposition family background ICT self-efficacy creative thinking 21st-century skills

Introduction

The demand for innovative talent has surged in the context of global economic transformation and the rapid integration of artificial intelligence and digital technologies. Developing creative thinking is now essential for individuals to adapt to societal changes and demonstrate their unique human value. Over the past decade, UNESCO has championed “learning and teaching for a sustainable future,” emphasizing the need to cultivate creative thinking across all educational levels. This focus is critical for educational advancement and central to national strategies to foster innovation-driven progress. The Partnership for 21st Century Skills (P21) further underscores “creative thinking and innovation” as key components of its “21st Century Skills Framework.” However, the PISA 2022 report reveals a concerning trend: students from socioeconomically disadvantaged backgrounds consistently lag in creative thinking and ICT literacy compared to their more advantaged peers (OECD, 2024). This disparity highlights the invisible barriers created by disparities in family social, economic, and cultural capital, which hinder the development of individual thinking literacy. Addressing this divide is urgent, as the integration of Information and Communications Technology (ICT) in education has the potential to expand opportunities and reduce educational inequalities rooted in socioeconomic factors. Research indicates that educational technology not only enhances learning outcomes but also plays a pivotal role in cultivating 21st-century skills (Blackwell et al., 2014; Brečko et al., 2014; Pérez-Sanagustín et al., 2017). Reflecting a shift from disparities in access to differences in outcomes, with increasingly subtle yet profound impacts on individuals (Kalyani, 2024).

In response to these challenges, this study investigates how ICT enhances individual creative thinking and how it interacts with family background to shape this development. While existing research has extensively explored the impact of family background and ICT on academic achievement, few studies have examined their combined influence on creative thinking. This research aims to fill this gap by providing empirical evidence on the roles of ICT and family background in fostering creative thinking, with a particular focus on the moderating effect of ICT self-efficacy. By employing advanced statistical methods, including Shapley value decomposition, this study offers a nuanced understanding of the relative contributions of these factors. Our findings not only contribute to the theoretical understanding of creative thinking development but also provide actionable insights for policymakers and educators seeking to promote educational equity and strengthen national innovation capabilities. This study addresses the following research questions:

(1) Does a disadvantaged family ESCS create a divide in digital and creative thinking among students?

(2) What impact do ICT, as well as family ESCS, have on students’ creative thinking?

(3) What proportion do ICT and family ESCS account for the variations in students’ creative thinking?

Literature Review and Hypotheses Development

Definition and Influencing Factors of Creative Thinking

Creativity is defined as a crucial cognitive and innovative output, emphasizing its significance in educational psychology (Plucker et al., 2004). Amabile’s 1983 creative thinking model views creative thinking as a “problem processor” in the brain, blending personal traits, cognitive abilities, and social-environmental factors (Amabile, 1983). The model consists of three components: the content of creative thinking (domain-specific skills and creative thinking abilities), internal motivation, and the surrounding social and cultural context (Amabile, 2012; Amabile & Pratt, 2016). This study embraces a multidimensional perspective of creative thinking, highlighting stable internal factors such as relevant knowledge and skills (Baer, 2016; Guilford, 1956), openness to experience and intelligence (Feist, 1998; Kaufman et al., 2016), self-efficacy(Bandura, 1997; Beghetto & Karwowski, 2017), future expectations (Amabile & Pratt, 2016), social-emotional characteristics (OECD, 2023). Furthermore, it also considers external influences, like the creative atmosphere in schools (Rinne et al., 2013; Wong & Niu, 2013) and the classroom (Amabile, 2012; Zhou & Su, 2010), as well as creative activities both inside and outside of school that nurture student creative thinking (Kaufman & Baer, 2004; OECD, 2019; Figure 1).

Figure 1.

Influencing factors of creative thinking.

There has long been a widespread bias against creative thinking, rooted in two misconceptions. Firstly, it is often viewed as an inherent and unchangeable personality trait, perceived as resistant to improvement through educational interventions (Karwowski, 2014); Secondly, many believe that nurturing creative thinking is limited to extracurricular activities (Clarke & Basilio, 2018). However, recent research emphasizes that creative thinking can be cultivated through intentional practices in formal schooling (Lucas & Spencer, 2017). Furthermore, as highlighted in the 2022 OECD study on Fostering and Measuring Creativity, teachers, schools, and families collectively play a crucial role in enhancing students’ creative thinking abilities.

The Impact of Family Background on Students’ Cognitive Achievement

The factors influencing the quality of students’ thinking and the exploration of educational production have received significant attention within academic circles, particularly regarding family background. Research from a social capital perspective highlight how parents’ occupational status and social networks can affect students’ cognitive development (Coleman, 1988; Sirin, 2005). Additionally, from a cultural capital standpoint, the educational background and cultural environment of parents significantly shape students’ critical thinking and innovation (Davis-Kean et al., 2020). The availability of family economic resources significantly plays a vital role in educational investments and cognitive enhancement. Additionally, Lareau (2011) discusses how a family’s economic, social, and cultural status determines access to educational resources and influences students’ motivation and learning engagement. Based on the established literature concerning the role of cultural and economic capital in cognitive development, we first examine the foundational relationship between family background and creative thinking. We posit a clear directional link:

H1: Higher family socioeconomic status will be positively associated with students’ creative thinking scores.

Building on this, we investigate the well-documented digital divide, extending it beyond mere access to cognitive outcomes. We expect that disparities in family resources will manifest in both digital engagement and creative performance:

H2: Students from disadvantaged family backgrounds will exhibit lower levels of both digital competence and creative thinking compared to their more advantaged peers.

The Impact of ICT on Students’ Cognitive Achievement

ICT plays a crucial role in contemporary education, serving as a significant catalyst for student achievement. Research shows a clear link between investment in ICT resources and improved academic outcomes (Li et al., 2022; Ran et al., 2022; Sung et al., 2016). Consequently, the accessibility and effective integration of ICT within schools have become key indicators for evaluating the quality of education (Timotheou et al., 2023; Wang & Wang, 2023). Beyond academic performance, ICT enhances disciplinary literacy and cognitive abilities, fostering innovative thinking, and computational skills (Liu et al., 2022; Lei et al., 2022; Wang et al., 2024). The PISA 2022 assessment further highlights the role of ICT in promoting creative thinking among students.

The ICT-Creativity Paradox

While meta-analyses confirm Information and Communications Technology (ICT) can boost academic achievement (Hillmayr et al., 2020), this techno-optimism dissolves when focusing on creative thinking. A critical body of research challenges the “more tech is better” axiom, revealing a paradoxical relationship where institutional deployment of ICT often stifles the very innovation it promises to unleash. When digital tools are used to reinforce standardized curricula, they risk promoting cognitive conformity over the divergent, exploratory thinking central to creativity (Henriksen et al., 2021). This tension is a core challenge in educational technology, as the potential of emerging technologies to foster creative thinking is highly dependent on pedagogical implementation, which often falls short (Li et al., 2022). Consequently, the academic community remains divided on how to reliably harness ICT to cultivate, rather than constrain, creative development in schools. This study intervenes in this debate by deconstructing the conflicting roles of ICT across home and school environments. Next, we turn to the contested role of ICT. While access to technology is often presumed beneficial, its application in different contexts may yield divergent outcomes. Therefore, we hypothesize that:

H3: The nature of ICT engagement, rather than mere access, will be a significant predictor of creative thinking, with autonomous, home-based use showing a positive association and structured, school-based use showing a less positive or even negative association.

Research Gaps and the Need for this Study

While the educational community has invested significant effort in understanding creative thinking, the literature presents a fragmented and often contradictory picture of technology’s role. Existing studies report a wide spectrum of outcomes, with some demonstrating the positive effects of ICT, while others find null or even negative associations with student creative thinking. This inconsistency suggests that prior research has often examined factors like family background and ICT in isolation, overlooking the complex interplay between them. The critical dynamic between a student’s home environment and their engagement with technology, especially how this interaction shapes innovative thought, remains under explored.

Furthermore, the challenge of accurately assessing creative thinking in large-scale educational surveys has historically limited empirical work in this area. This study aims to address these gaps. By utilizing a nuanced analytical approach on recent PISA data, we move beyond isolated factors to investigate the interaction between family background and various facets of ICT use. Our goal is to help resolve the existing contradictions in the literature by providing a more holistic understanding of the conditions under which technology fosters or hinders creative development. This leads to our central hypothesis on their interplay:

H4: ICT self-efficacy will moderate the relationship between family socioeconomic status and creative thinking, such that the positive association between ICT use and creative thinking will be stronger for students from lower socioeconomic backgrounds.

The complexity of creative thinking makes it challenging to assess accurately in large-scale educational assessments. As a result, empirical research in this area is limited, highlighting an urgent need for innovative methodologies to measure creative thinking across various educational contexts.

Methodology

Data Source

This study adopts a cross-sectional design, utilizing data from the Program for International Student Assessment (PISA) 2022, administered by the Organization for Economic Co-operation and Development (OECD). The dataset comprises responses from approximately 600,000 15-year-old students across 79 economies, ensuring global representativeness. PISA assessments conducted triennially since 2000, employ a rigorous Probability Proportional to Size (PPS) sampling method. This approach assigns each school an equal selection probability based on enrollment size, thereby enhancing data reliability and minimizing selection bias. PISA adheres to stringent ethical standards, including institutional review board approvals and participant anonymity. This robust methodological framework ensures the validity of cross-system comparisons and supports policy-relevant conclusions for the Asia-Pacific region.

Sample Selection and Processing

The study employs a cross-sectional comparison study to select three Special Administrative Regions (SARs) of China from PISA 2022 data: Hong Kong (China), Macao (China), and Taiwan (China). This comparative case study design is predicated upon their paradoxical educational profile: while ranking in the top 10% globally for mathematics, reading, and science achievement, these SARs demonstrated significant creative thinking deficits (scores: Taiwan = 33; Hong Kong = 32; Macao = 32). This schism between scholastic excellence and creative under performance establishes an optimal natural experiment for investigating compensatory mechanisms in digital learning ecosystems.

Methodological rigor was ensured through: (1) Cultural isomorphism control: Socio-culturally cohesive educational governance structures across SARs, minimizing confounding cultural variance (Hofstede VSM14 score <5% CI). (2) Data integrity preservation: Application of OECD’s Missing at Random (MAR) thresholding with listwise deletion (n = 16,148 retained). (3) Population representativeness: The final sample mirrors official enrollment ratios (Female = 48.5%, Male = 51.5%). This carefully selected sample provides a solid foundation for insightful comparisons and contributes to education research in the Asia-Pacific region.

Dependent Variable

The dependent variable is students’ creative thinking, measured by their performance on the PISA 2022 Creative Thinking Test, the PISA creative thinking assessment has undergone extensive field testing and validation by the OECD. Operationalized through the PISA 2022 Multidimensional Creativity Assessment (OECD, 2024), this construct captures students’ ability to: (1) generate original ideas. (2) Iteratively refine prototypes. (3) Ethically contextualize solutions. Scores are derived from 10 plausible values (PVs) generated via Item Response Theory (IRT), with PV1 used for analysis to ensure robustness (OECD, 2024).

Independent Variables

The independent variables examined in this study include factors related to family background as well as ICT. The Economic, Social, and Cultural Status (ESCS) index, derived from the PISA assessment, serves as a comprehensive measure of family background, capturing the diverse influences of economic resources, social capital, and cultural orientations on students’ educational outcomes. Additionally, the assessment of ICT factors is conducted using the PISA 2022 ICT evaluation framework (OECD, 2023). This framework encompasses four key dimensions: the availability of ICT resources, the modes of ICT usage, the frequency of ICT utilization, and students’ self-efficacy in utilizing such technology. Together, these dimensions offer a comprehensive insight into the impact of ICT on students’ learning environments and their creative thinking capabilities. A detailed account of the specific indicators and the corresponding database source items for these variables can be found in Table 1.

Table 1.

Description of Study Variables.

Variable	Description	Source
Dependent variable
Creative thinking	Students’ creative thinking test scores	Plausible value^a
Independent variables
Family background	ESCS overall scores	ESCS^b
ICT	ICT availability in schools	ICTSCH
	ICT availability in homes	ICTHOME
	ICT use in subject-related activities	ICTSUBJ
	ICT use in inquiry-based learning activities	ICTENQ
	ICT use for support and feedback	ICTFEE
	ICT use in extracurricular activities	ICTOUT
	Frequency of ICT use on weekdays	ICTWKDY
	Frequency of ICT use on weekends	ICTWKEND
	ICT self-efficacy	ICTEFFIC
Control variables
Internal factors	Creative self-efficacy	CREATEFF
	Expectations for the future	BSMJ
	Openness to experience and intellect	CREATOPN
	Socio-emotional characteristics	RELATST
		BELONG
		FEELSAFE
	Relevant knowledge and skills	PERSEVAGR
		CURIOAGR
		COOPAGR
		EMPATAGR
		ASSERAGR
		STRESAGR
		EMOCOAGR
		GROSAGR
External factors	School and classroom creative climate	CREATSCH
	In-class creative activities	CREATAS
	Extracurricular creative activities	CREATOOS
Demographic variables	Age	AGE
Demographic variables	Sex	ST004D01T

In PISA 2022, the Item Response Theory (IRT) model is utilized to estimate the probability distribution of each student’s achievement in literacy, generating ten Plausible Values (PVs) per student. For research purposes, the first Plausible Value (PV1) is used along with student and school weights from the PISA data.

In PISA 2022, the ESCS index of families is calculated using Principal Component Analysis (PCA) based on parents’ education, highest parental occupation, and household possessions.

Control Variables

PISA 2022 data indicate that the majority of variance in creative thinking (74%) occurs within schools, highlighting the importance of individual student characteristics. To isolate the effects of family background and ICT, our models therefore account for key individual and environmental factors identified in the literature. Since several of these constructs, such as socio-emotional characteristics and relevant knowledge, were not directly measured by single PISA indices, we derived them through factor analysis.

We conducted an exploratory factor analysis using principal axis factoring with a promax rotation, a method well-suited for identifying latent constructs from the PISA student questionnaire items. For instance, the Socio-Emotional Characteristics factor was derived from items assessing students’ sense of belonging and perseverance. Similarly, the Relevant Knowledge and Skills factor was synthesized from items related to creative problem-solving. All derived factors demonstrated good internal consistency (Cronbach’s α > .70). A complete account of the items is available in the Table 1.

Research Questions

This research, grounded in the education production function model, addresses the following questions:

(1) Does a disadvantaged family ESCS create a divide in digital and creative thinking among students?

(2) What impact do ICT, as well as family ESCS, have on students’ creative thinking?

(3) What proportion do ICT and family ESCS account for the variations in students’ creative thinking?

Measures

Measurement Model

In terms of the selection of econometric models, the analysis framework based on the educational production function model is proposed (Hanushek, 1986). Its function expression is:

Q_{ij} = f (F_{ij}, I_{ij}, S_{ij}, E_{ij}, A_{ij})

Where: “Q_ij” represents the creative thinking score of student “i” in school “j”; “F_ij” denotes the family’s ESCS index for student “i” in school “j”; “I_ij” captures ICT-related factors for student “i” in school “j”; “S_ij” encompasses a range of control variables for student “i” in school “j”; “E_ij” illustrates the interactions terms between family ESCS and ICT-related factors, which are included to assess potential moderating effects; “A_ij” is the random error term associated with the model.

Data Analysis

In this study, we utilized STATA 17.0 to analyze the PISA 2022 dataset, focusing on multiple linear regression analyses with students’ creative thinking scores as the dependent variable. We controlled for individual internal motivation and various external environmental factors to examine the relationships involving family ESCS, ICT availability and usage patterns, frequency of use, self-efficacy, and their effects on creative thinking performance. We included interaction terms between family ESCS and ICT to assess the moderating effects of technology on the relationship with family ESCS. Furthermore, Using the Shapley value decomposition method, we analyzed the contribution rate of each variable in creative thinking. Its core advantage lies in resolving the attribution challenge for correlated variables. When two variables are highly correlated, traditional methods struggle to distinguish their true individual impacts. By systematically excluding combinations, Shapley values precisely isolate the independent contribution of each variable. This approach enabled us to rank the significance of multiple explanatory variables contributing to differences in individual creative thinking. Ultimately, this analysis seeks to identify the key factors that can enhance students’ creative thinking skills.

Results

Examining the Significant Digital and Creative Thinking Divide Among Students from Different Family ESCS

The table below presents the average performance scores of the overall sample, as well as those from various family ESCS, on key variables. The values in the table represent the t-statistics from independent samples t-tests. The average ESCS score is categorized into high and low groups, with the high ESCS group comprising 8,269 individuals and the low ESCS group comprising 7,883 individuals.

Table 2 indicates that individuals from high ESCS significantly outperform their peers from low ESCS in all assessed variables. Specifically, when comparing those from disadvantaged ones, there is a marked difference in ICT self-efficacy and creative thinking scores, with the latter group exhibiting notably lower levels. These findings suggest that a disadvantaged family ESCS may lead to hidden disparities in digital skills and cognitive abilities among students. Therefore, hypothesis H1 and H2 was supported.

Table 2.

The Mean Scores and Differences of Students from Different Family ESCS.

Variable	All sample	Low ESCS	High ESCS	Difference test
Creative thinking divide
Creative thinking	31.81	29.77	33.76	−23.77***
Digital divide
ICT availability in schools	3.95	1.94	5.86	−12.46***
ICT availability in homes	4.29	2.26	6.22	−12.67***
ICT use in subject-related activities	7.20	5.81	8.52	−6.57***
ICT use in inquiry-based learning activities	4.09	2.02	6.06	−12.83***
ICT use for support and feedback	4.31	2.38	6.15	−11.96***
ICT use in extracurricular activities	4.37	2.44	6.21	−11.84***
Frequency of ICT use on weekdays	4.58	2.74	6.33	−10.88***
Frequency of ICT use on weekends	5.17	3.43	6.84	−9.86***
ICT self-efficacy	5.99	3.92	7.96	−13.05***

p < .1. **p < .05. ***p < .01.

Examining the Influence of Family ESCS and ICT-Related Factors on Creative Thinking

The table that follows presents the results from a multiple regression analysis on how family ESCS and ICT factors influence creative thinking while accounting for various potential confounding variables. Furthermore, it explores the potential moderating effect of ICT factors on students’ creative thinking, under the assumption of a stable family ESCS (Table 3).

Table 3.

Regression Results: Family ESCS, ICT, and Creative Thinking (N = 16,148).

	Model 1		Model 2		Model 3
	B	SE	B	SE	B	SE
Constant	33.78***	.20	32.21***	0.09	34.03***	0.24
ESCS	.10***	.03	.09***	0.03	1.88***	0.12
ICT availability in schools	×		−.08***	.03	−0.52***	0.13
ICT availability in homes			.05*	.03	0.15	0.27
ICT use in subject-related activities			−.00	.01	0.09	0.20
ICT use in inquiry-based learning activities			−.18*	.05	−0.29	0.25
ICT use for support and feedback			.01	.02	−1.60***	0.25
ICT use in extracurricular activities			.49***	.08	2.25***	0.23
Frequency of ICT use on weekdays			−.11***	.03	−0.09*	0.05
Frequency of ICT use on weekends			−.00	.02	−0.01	0.02
ICT self-efficacy			−.00	.02	0.15	0.23
Control variables	√		√		√
ESCS * ICT availability in schools	×		×		.13	0.12
ESCS * ICT availability in homes					.25	0.24
ESCS * ICT use in subject-related activities					.14	0.18
ESCS * ICT use in inquiry-based learning activities					.40***	0.22
ESCS * ICT use for support and feedback					−.02	0.23
ESCS * ICT use in extracurricular activities					−.14	0.20
ESCS * frequency of ICT use on weekdays					−.00	0.04
ESCS * frequency of ICT use on weekends					−.00	0.03
ESCS * ICT self-efficacy					−.41**	0.20
R-squared	.05		.06		.14

Note. Model 1 includes the independent variable ESCS along with all control variables. Model 2 introduces ICT-related variables into Model 1. Model 3 further adds interaction terms between ICT variables and ESCS based on the structure of Model 2. The values in the table are unstandardized regression coefficients. If standardized regression coefficients are required, please contact the author to obtain them.

p < .1. **p < .05. ***p < .01.

The inclusion of ICT-related factors revealed several significant associations with students’ creative thinking, lending support to Hypothesis H3. Notably, the context of technology access appears to be critical: a greater availability of ICT in schools was linked to lower creative thinking scores, whereas access at home showed a positive association. The nature of ICT use was similarly nuanced. Its application in extracurricular activities correlated positively with creative thinking, while its use for inquiry-based learning, along with more frequent use on weekdays, were both associated with lower scores. While the positive link between family ESCS and creative thinking persisted, its strength was attenuated after accounting for ICT factors, suggesting a potential mediating role for technology.

The analysis further uncovered significant interaction effects, supporting Hypothesis H4. The relationship between family background and creative thinking appeared to be conditional on students’ ICT experiences. Specifically, the negative association between a disadvantaged family background and creative thinking was more pronounced among students who frequently used ICT for inquiry-based learning. In contrast, ICT self-efficacy seemed to play a buffering role. The link between a disadvantaged background and lower creative scores was substantially weaker for students who reported higher confidence in their ICT skills. These results underscore a complex interplay between family background, technology use, and student confidence, pointing toward nuanced pathways that shape creative development.

Exploring the Degree of Contribution of Family ESCS and ICT-Related Factors to Variations in Students’ Creative Thinking

This study utilizes the Shapley value decomposition method to explore the relative contributions of family ESCS and ICT-related factors to variations in creative thinking. Table 4 provides detailed quantitative insights into these contributions.

Table 4.

Results of Shapley Value Decomposition for Differences in Creative Thinking.

Variables	RC (%)
ESCS	5.15
ICT availability in schools	12.24
ICT availability in homes	5.20
ICT use in subject-related activities	10.39
ICT use in inquiry-based learning activities	6.81
ICT use for support and feedback	8.24
ICT use in extracurricular activities	12.67
Frequency of ICT use on weekdays	10.26
Frequency of ICT use on weekends	9.35
ICT self-efficacy	19.70

Note. The results of the Shapley value decomposition illustrate the relative influence of each explanatory variable on the variance in students’ creative thinking, controlling for other variables. The percentages indicate the portion of the model’s total R-squared (R² = .20) attributed to each variable, and thus sum to 100% of the explained variance.

Table 4 highlights that ICT self-efficacy emerges as a significant determinant, accounting for approximately 19.7% of the variations observed in students’ creative thinking abilities. While ESCS contributes less than 6%. Other contributing factors, in descending order, include ICT use in extracurricular activities, the availability of ICT resources in schools, ICT use in subject-related activities, Frequency of ICT use on weekdays, Frequency of ICT use on weekends, ICT use for support and feedback, ICT use in inquiry-based learning activities facilitated, and the accessibility of ICT in homes. The analysis indicates that the student’s proficiency and confidence in ICT are key to individual differences in creative thinking, whereas the impact of family background is relatively limited.

Beyond this headline finding, a distinct pattern emerges: argentic engagement with technology consistently outweighs passive resource availability. For instance, the use of ICT in extracurricular contexts is a far more substantial contributor than is the mere availability of ICT at home. This analysis strongly suggests that the fulcrum of creative thinking in the digital age is shifting. The focus moves from a traditional, resource-based divide defined by what a student’s family can provide, to a capabilities-based divide defined by a student’s own confidence and proficiency in using digital tools.

Discussion

Utilizing the PISA 2022 dataset within the framework of the education production function model, this study conducts an empirical analysis to examine the nuanced effect of ICT and family ESCS on students’ creative thinking during the ongoing transformation of the digital divide. This investigation not only seeks to explore the intricate relationships among these variables but also to unravel the complexities surrounding the “black box” of creative thinking development. In doing so, our research strives to highlight the key factors that influence the cultivation of creative thinking, thereby providing a solid scientific foundation for educators, policymakers, and researchers to explore and implement diverse training strategies designed to enhance students’ creative thinking potential.

The Effect of Family ESCS on the Digital Divide and Creative Thinking Disparity Among Students

Building on previous research concerning child development and social inequality (Broer et al., 2019), this study explores the nuanced effect of family background on both the digital divide and disparity in creative thinking among students. Our findings not only affirm the existence of a digital divide among middle school students from varying socioeconomic backgrounds but also reveal a deeper cognitive divide that transcends mere access to technology. Specifically, students from lower socioeconomic backgrounds often face insufficient ICT resources, a trend that the literature has extensively documented (Hargittai, 2022). This lack of resources hinders their development of digital literacy and exacerbates existing educational disparities. Moreover, our study introduces a new dimension to this discourse by identifying a substantial correlation between family socioeconomic status and students’ performance on creative thinking assessments. This finding supports the broader assertion that creative thinking, a vital component of 21st-century literacy, is essential for individual learning, innovation, and future career success (Amabile, 1983; Plucker et al., 2004).

The intricate interplay between the availability, use, and frequency of ICT in supporting students’ creative thinking presents a multifaceted and nuanced challenge. Recent studies reveal that, despite the extensive availability of ICT in educational settings, this high level of access does not consistently lead to the expected positive outcomes in nurturing students’ creative thinking abilities. Instead, some negative influences have emerged, aligning with the findings reported by Henriksen et al. (2021), Agasisti et al. (2020), and Gorjón and Osés (2022). These insights suggest that simply increasing the presence of technology in schools may not be sufficient to foster creative thinking; rather, they indicate potential misalignments in how these technologies are integrated and utilized. The observation that schools’ strategies may struggle to effectively stimulate and nurture students’ creative thinking through their ICT strategies invites critical reflection on current educational practices. In contrast, the availability of ICT within the home environment appears to have a positive impact on cultivating students’ creative thinking. The home environment serves as a crucial context for individual development, where access to ICT resources provides students with enhanced opportunities for autonomous exploration and innovative experimentation. This dynamic not only bolsters their capacity for creative thinking but also aligns with the assertions made by Dong and Kula (2022). In light of this, policymakers must recalibrate their strategies to prioritize the equitable distribution of ICT resources within households, particularly ensuring that remote areas and low-income families have access to these resources. This strategic shift is essential to bridge the digital divide and ensure that all students can engage in digital learning on an equal footing, thereby addressing the information access disparities faced by disadvantaged families, as highlighted by the OECD (2019).

The Effect of ICT Availability on Students’ Creative Thinking

A central and counterintuitive finding of this study is that greater ICT availability in schools is negatively correlated with student creative thinking. This directly challenges the prevailing policy assumption that technological investment is a straightforward path to educational enhancement. Our analysis suggests that without fundamental pedagogical reform, school-based ICT often functions less as an innovation catalyst and more as a “cognitive straitjacket.” This finding lends large-scale, empirical weight to the cautionary perspective of Henriksen et al. (2021), who described the “uneasy space” where technology’s creative potential is constrained by traditional teaching practices. Our results also echo the concerns of GorjER and Osdj (2022), who documented the detrimental effects of technology overuse on student outcomes, suggesting that quality and context of use trump sheer quantity.

This counterproductive pattern extends to instructional methods. Our data indicate that technology use in inquiry-based learning also negatively predicts creative thinking. This does not necessarily condemn the pedagogy itself, but rather points to the immense implementation challenges in a digital context. As the work of Tondeur et al. (2019) highlights, effective digital pedagogy requires teachers to possess a sophisticated blend of technological and instructional knowledge. Lacking this, teachers may inadvertently guide students toward superficial uses of technology. Simultaneously, from the students’ perspective, navigating open-ended digital inquiry requires strong self-regulatory skills, which, as Valtonen et al. (2021) suggest, many students may not have developed, causing them to default to simple, answer-seeking behaviors.

Ultimately, these results complicate the traditional assumptions of the Education Production Function model. The model typically treats school resources as positive inputs. Our findings, however, align with a growing body of research suggesting that the value of an input is not inherent but is defined by the learning ecology in which it is activated. The positive association we found for home ICT, a finding consistent with studies like Dong and Kula (2022), underscores the importance of learner autonomy. When embedded in a system of control, a school resource can become a counterproductive input. We therefore argue for a refinement of the production function model, moving beyond a simple inventory of resources toward an ecological perspective that accounts for the pedagogical context.

The Effect of ICT Usage on Students’ Creative Thinking

When exploring the impact of ICT usage on students’ creative thinking abilities, our research unexpectedly revealed a counterintuitive trend. Specifically, the finding that ICT use in inquiry-based learning is negatively associated with creative thinking presents a counterintuitive but critical challenge. This finding aligns with the observations made by Li et al. (2022), who noted a divergence between technology’s potential to enhance learning autonomy and its actual influence on fostering innovation. A possible explanation is that technology may foster an “answer-oriented” mindset, where students leverage powerful search tools not for genuine exploration, but for rapidly finding predefined solutions, thereby short-circuiting the creative process.

This shift could lead students to focus on predefined solutions and standardized approaches, ultimately hindering their critical thinking, independent exploration, and creative problem-solving abilities (Wei et al., 2015). In contrast, the use of ICT in extracurricular activities demonstrates a significant positive correlation with students’ creative thinking. Engaging in extracurricular activities allows students to experience a learning environment that is markedly different from the traditional classroom setting. This innovative context, along with the variety of social interactions, not only sparks students’ imagination and creative thinking but also provides them with opportunities to confront real-world challenges and improve their ability to apply acquired knowledge in new situations (Chen et al., 2022; Yousef, 2021).

However, beyond this cognitive shift in students, the negative association may also stem from significant implementation challenges within schools. This single finding could reflect a confluence of factors, including: (1) Poor pedagogical implementation, where inquiry-based learning is structured more like a scavenger hunt for facts than a truly open-ended investigation; (2) The use of restrictive software that, despite being labeled as educational, channels students into narrow, predetermined pathways; and (3) A lack of specific teacher training in how to facilitate creative inquiry with technology. It is plausible that teachers, without adequate support, may struggle to manage the complexities of digital inquiry, leading to learning activities that fail to stimulate the deep, divergent thinking that creative thinking requires.

The Mediating Role of ICT Self-Efficacy

Upon a nuanced examination of the sources contributing to this moderating influence, it becomes evident that simply having access to ICT does not effectively bridge the cognitive disparities arising from differences in family background as one might expect. Instead, it is the ability to leverage ICT, specifically ICT self-efficacy, that emerges as a critical factor in significantly narrowing this gap. Our findings, supported by Shapley value decomposition analysis, underscore the significance of ICT self-efficacy in clarifying the disparities in individual creative thinking, a contribution that merits considerable attention (Christensen-Salem et al., 2021). Engaging with the broader scholarly discourse reveals that ICT self-efficacy, as a positive psychological construct, occupies a central role in creative thinking endeavors. Individuals with high levels of self-efficacy are generally proficient in utilizing ICT tools for effective information retrieval, task management, and idea articulation, thus demonstrating enhanced creative thinking (Compeau & Higgins, 1995). This aligns with previous research that highlights the connection between technological proficiency and cognitive development, suggesting that technological proficiency and cognitive development, suggesting that technological adeptness is not merely an accessory but a catalyst for fostering creative thinking.

Our finding that ICT self-efficacy emerges as a primary predictor of creative thinking prompts a re-examination of Amabile’s componential theory in a digital context. Rather than viewing digital proficiency merely as a contemporary form of domain-relevant skill, our results suggest ICT self-efficacy may function as a crucial moderator for the other components. A student might possess high intrinsic motivation and understand creative processes, but without the confidence to navigate digital tools, their creative potential remains unrealized. This confidence, or self-efficacy, therefore acts not as a standalone component but as the essential psychological gateway through which motivation and skills are translated into creative performance in technologically saturated environments. It suggests a critical extension to Amabile’s model, where such efficacy-beliefs become a pivotal enabler of the entire creative system.

Conclusion

This study reveals a central paradox in the relationship between technology, family background, and creative thinking. Our findings indicate that digital tools hold the dual potential to both deepen existing educational disparities and offer pathways toward greater equity. We found that students from disadvantaged backgrounds often face a double bind: limited access to home technology is compounded by school-based ICT that, in its current form, is negatively associated with creativity.

The key to unlocking technology’s positive potential appears to lie not in the mere provision of resources, but in the context of their use and the cultivation of student agency. The home environment emerged as a more potent incubator for creativity than the school, likely because it affords greater autonomy. This distinction highlights a critical insight: structured, top-down technological integration in schools can inadvertently constrain creative thinking, whereas self-directed engagement, such as in extracurricular activities, fosters it.

Ultimately, our analysis points to ICT self-efficacy as a pivotal factor in this complex dynamic. It emerges as a powerful protective factor, capable of buffering students from the disadvantages associated with their family background. This suggests that a focus on building students’ confidence and capability with digital tools may be a more effective lever for educational equity than a focus on hardware alone. In essence, our research calls for a shift in perspective—from viewing students as passive recipients of technology to empowering them as confident and creative agents in a digital world.

Implications

It is crucial to preface these implications by acknowledging the cross-sectional nature of our data, which identifies strong associations but does not permit causal claims. The following points should therefore be interpreted as speculative avenues for policy exploration and future research, requiring further validation through longitudinal or experimental research.

Our findings suggest that the prevailing focus on equipping schools with ICT hardware may be insufficient, and perhaps even counterproductive, for fostering creative thinking. This raises critical questions for policymakers about the conditions under which technology supports or constrains innovation. Instead of a singular focus on institutional access, a potential area for exploration could be a policy reorientation toward the household digital ecosystem. Our results, which highlight the positive association between home ICT availability and creative thinking, alongside the paramount importance of ICT self-efficacy, suggest that empowering families might be a powerful lever for change. Policymakers could consider piloting programs that support parents in creating technology-rich home learning environments, focusing not just on device access but also on cultivating the digital skills and confidence of both students and caregivers.

Furthermore, the negative association between school-based ICT and creative thinking invites a deeper reflection on pedagogical practices. The data suggest a need to move beyond technology as a tool for content delivery or administrative efficiency. An important direction for educators and school leaders could be to investigate how to create more open-ended, student-driven digital learning experiences. This might involve prioritizing flexible, low-structure software and fostering a classroom culture where technology is used for genuine exploration and creation, rather than for finding predetermined answers.

Finally, the strong predictive power of ICT self-efficacy points toward the importance of cultivating digital competence as a cornerstone of educational equity. Rather than simply assuming that access leads to ability, educational systems might explore developing clear competency frameworks. These frameworks could guide the design of interventions aimed at building students’ confidence and capabilities in using digital tools for complex problem-solving and creative expression. Such an approach would shift the focus from hardware procurement to human-centered capability building, potentially offering a more sustainable path toward equitable innovation in the digital age.

Limitations and Future Research Direction

Several limitations should be noted. First, the cross-sectional design of this study, inherent in using PISA data, precludes causal inference and limits our understanding of the developmental trajectories of creative thinking. Second, while the PISA assessment is robust, its specific measures may not fully capture the multimodal creative capacities of digital natives, particularly those expressed through code or digital media. Finally, pooling data from Hong Kong, Macao, and Taiwan, while justified by cultural proximity, may mask important inter-regional variations in how technology and family background interact.

A second set of limitations pertains to our model’s explanatory power. Our final model explains approximately 20% of the variance in creative thinking, leaving the majority unexplained. This underscores that creativity is a multifaceted phenomenon shaped by factors beyond the scope of large-scale assessments. Key unmeasured variables likely include teacher pedagogy, specific curriculum content, peer dynamics, and a broader range of personality traits. This highlights the need for research that can capture these more granular, context-specific influences.

These limitations chart a clear course for future inquiry. To move beyond correlation, longitudinal studies are essential for tracking students through key developmental transitions and understanding the compounding effects of their digital ecosystems. To capture a more holistic view of creativity, future assessments could integrate digital trace data from creative software with traditional psychometrics. Finally, mixed-methods studies, combining large-scale data with in-depth classroom observations or qualitative interviews, would be invaluable for uncovering the pedagogical and social dynamics that our quantitative model could not capture.

Footnotes

ORCID iD

Xiaochen Luo

Ethical Considerations

This article does not contain any studies with human or animal participants. And this research will not pose a threat to public health and national security.

Author Contributions

All the authors, Xiaowei Tian, Xiaochen Luo, and Jining Han, participated in the conception and design of the research. Xiaowei Tian and Xiaochen Luo primarily carried out the collection and analysis of the data. Jining Han provided guidance and oversight throughout the research process. The initial writing framework and analysis ideas for the research were developed by the authors in collaboration. The first draft of the manuscript was written by one of the authors, and subsequent revisions were completed by all authors, who also commented on and approved the previous versions of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data-set used for this manuscript belongs to the PISA 2022. The authors gratefully acknowledge access to PISA data provided by the .

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