The impact of educational digitalization on the creativity of students with special needs: the role of study crafting and creative self-efficacy

The study addresses how educational digitalization influences the creativity of students with special needs and through what mechanisms. With rapid advances in cloud computing, the Internet, big data, and virtual reality, higher education practices are transforming, yet students with special needs face added challenges (economic poverty, physical and psychological difficulties, learning delays, sociophobia). Creativity—encompassing problem-solving, creative thinking, and creative learning—benefits students by enhancing motivation, higher-order thinking, collaboration, and cognitive load management. Prior work shows specific digital tools can foster creativity, but often overlooks heterogeneous responses among students with special needs and the mechanisms involved. Drawing on information theory, the authors posit that digital education can reshape access to resources, technologies, and methodologies, prompting students to adapt and redesign their study patterns (study crafting), and that creative self-efficacy may strengthen these effects. The study proposes and tests a mediated moderation model where educational digitalization affects creativity directly and indirectly via study crafting, with creative self-efficacy as a moderator.

The paper develops hypotheses grounded in information theory, arguing that transforming educational resources, technologies, and methodologies into digital forms changes how students access, disseminate, and use knowledge. For students with special needs, digitalization can mitigate disparities in access due to economic, physical, or psychological barriers. Prior studies indicate digital tools and activities (e.g., science fiction films, online problem-solving, gaming) can enhance creativity, and that dispositions like lifelong learning can mediate effects of digital education on creativity. Extending this, the authors conceptualize study crafting (adapted from job crafting) as a behavioral mechanism by which students increase study resources, engage with challenging demands, and decrease hindering demands. Creative self-efficacy, shaped by creative role identity and teachers’ creativity expectations, is proposed to moderate the effects of digitalization on creativity and on the pathway through study crafting. The hypotheses: H1 educational digitalization enhances creativity; H2 study crafting mediates the effect; H3 creative self-efficacy moderates the digitalization–creativity link; H4 creative self-efficacy moderates the digitalization–study crafting link; H5 creative self-efficacy moderates the study crafting–creativity link.

Design: Cross-sectional survey with complementary case study using grounded theory. Sample and procedure: Online questionnaires were distributed across various cities in China targeting university students with special needs (economic poverty, physical difficulties, psychological vulnerability, learning delays, sociophobia). Of 211 responses, 173 were valid (effective response rate 81.99%). Demographics: 45.1% male, 54.9% female; 87.3% aged 18–24; 83.8% undergraduates; 91.3% from Double First-Class universities. A pilot test with 30 respondents preceded data collection; reliability assessed with Cronbach’s alpha. Measures (5-point Likert scales): - Educational digitalization: 9-item scale adapted from Rafferty and Griffin (2006), across three dimensions (digitization of resources, dynamization of technology, innovation in methodologies). Example: use of multimedia, Internet, VR, digital libraries. Cronbach’s alpha = 0.919. - Study crafting: 15-item adaptation of Tims et al. (2012) job crafting (increase study resources; increase challenging demands; decrease hindering demands). Example: “I try to develop my capabilities.” Cronbach’s alpha = 0.879. - Creative self-efficacy: 4-item scale (Tierney and Farmer, 2002). Example: confidence in solving problems creatively. Cronbach’s alpha = 0.870. - Creativity of students with special needs: 8-item scale (Zhou and George, 2001). Example: proposing new ways to achieve goals. Cronbach’s alpha = 0.935. Data analysis: Conducted with AMOS, IBM SPSS 25.0, and PROCESS. Steps: (1) Common method bias checked via Harman’s single-factor test (anonymous responses; instructional remedies); (2) Confirmatory factor analysis (CFA) to assess convergent and discriminant validity across alternative factor structures; (3) Descriptive statistics and correlations; (4) Hypothesis testing with PROCESS: Model 4 for mediation (5,000 bootstrap samples) and Model 59 for moderated mediation. Case study: Harbin Institute of Technology (HIT), Weihai district, selected due to ongoing digital transformation under resource constraints. Grounded theory approach with two interview waves (Jan 3–7, 2024; Feb 1–6, 2024). Participants were students with special needs (age 17–23). Double-blind coding, creation of a data bank, typicality-based sampling, and verification of textual content. Open and axial coding yielded 9 subcategories within 4 central categories (education digitalization, study crafting, creative self-efficacy, creativity), followed by selective coding to articulate the mechanism storyline. Validity and reliability results: Four-factor CFA showed best fit (CMIN/DF = 2.107, GFI = 0.838, CFI = 0.929, TLI = 0.917, RMSEA = 0.080). All constructs showed acceptable reliability (Cronbach’s alpha > 0.70), composite reliability > 0.70, factor loadings > 0.5, and AVE > 0.50. Harman’s test: first factor explained 35.59% (<40%), suggesting CMB not a major concern.

- Common method bias: Harman’s single-factor test identified eight factors with eigenvalues >1; first factor explained 35.59% of variance (<40%), indicating no serious CMB. - Measurement validity and reliability: Four-factor model fit best (CMIN/DF = 2.107; GFI = 0.838; CFI = 0.929; TLI = 0.917; RMSEA = 0.080). All constructs exhibited acceptable reliability (Cronbach’s alphas: educational digitalization 0.919; study crafting 0.879; creative self-efficacy 0.870; creativity 0.935), CR > 0.70, AVE > 0.50, and loadings > 0.50. - Descriptives/correlations (all positive and significant): educational digitalization correlated with study crafting (0.536), creative self-efficacy (0.342), and creativity (0.355); study crafting with creative self-efficacy (0.627) and creativity (0.670); creative self-efficacy with creativity (0.796). - H1 (direct effect): Educational digitalization positively predicted creativity of students with special needs (standardized coefficient = 0.355, p < 0.001). - H2 (mediation): Study crafting mediated the effect of educational digitalization on creativity. ED → SC significant (p < 0.001; 95% CI [0.315, 0.509]); indirect effect = 0.351, p < 0.001, 95% CI [0.218, 0.497]. - H3 (moderation of ED → creativity by CSE): Interaction significant (β = 0.188, SE = 0.067, t = 2.826, p < 0.01). Greater creative self-efficacy strengthened the positive effect of educational digitalization on creativity. - H4 (moderation of ED → study crafting by CSE): Not significant (β = −0.049, SE = 0.044, 95% CI [−0.136, 0.037]). H4 rejected. - H5 (moderation of study crafting → creativity by CSE): Significant (β = 0.152, SE = 0.072, t = 2.117, p < 0.05). Higher creative self-efficacy amplified the positive effect of study crafting on creativity. - Case study triangulation: Interview-based grounded theory at HIT Weihai provided qualitative support for H1, H2, H3, and H5 via examples of enhanced access to digital resources, technology-enabled learning, innovative methodologies, increased study crafting behaviors, and elevated creative outcomes. (Text also notes H4 narratively, but survey analysis rejected H4.)

Findings address the central question of how educational digitalization influences creativity in students with special needs and under what conditions. Digitalization directly enhances creativity by broadening access to quality resources, energizing educational technology, and innovating instructional methodologies. It also indirectly enhances creativity via study crafting: students actively increase study resources, embrace challenging demands, and mitigate hindrances in response to digital learning environments, which fosters creative thinking and outcomes. Creative self-efficacy strengthens key links, indicating that students who identify as creative and perceive high expectations from teachers better leverage digital tools and information, translating study crafting into stronger creative performance. The results extend prior literature on digital tools and creativity by elucidating mechanisms (study crafting as mediator) and boundary conditions (creative self-efficacy as moderator) in a special-needs higher education context. Practically, integrating digital platforms, interactive technologies, and innovative pedagogies, alongside cultivating students’ creative self-efficacy, can maximize creativity gains and promote educational equity.

The study demonstrates that educational digitalization promotes the creativity of students with special needs both directly and indirectly through study crafting. Creative self-efficacy is a pivotal moderator that strengthens the effects of educational digitalization on creativity and enhances the translation of study crafting into creative outcomes. The work contributes theoretically by clarifying mechanisms and boundary conditions linking digital transformation in education to creativity within a special-needs context and by introducing study crafting as a key mediating construct in education. Practically, the findings guide institutions to prioritize digital resource integration, technology-enabled teaching, and innovative methodologies while fostering students’ creative self-efficacy through teacher expectations and identity-building practices. Future research should broaden samples across cultures and types of special needs, compare the effectiveness of specific digital tools, and examine domain-specific educational contexts (e.g., arts, music) to understand heterogeneous impacts on creativity.

- Generalizability: The sample is limited to students with special needs in China; cross-cultural validation is needed. - Heterogeneity: Future work should differentiate effects across types of special needs (e.g., learning disabilities, sensory or emotional disorders) and examine tailored adaptations. - Tool-specific effects: The study operationalizes digitalization broadly; future research should compare the effectiveness of specific tools (online platforms, VR, intelligent assistive software) for different needs. - Domain specificity: Investigate how digitization uniquely impacts creativity within specific fields (e.g., arts, music).