Hierarchical Cluster Analysis of AI Implementation Constraints: Uncovering Heterogeneous Barrier Patterns in Education
Description
Hierarchical Cluster Analysis of AI Implementation Constraints: Uncovering Heterogeneous Barrier Patterns in Education
Anfal Rababah
Independent Researcher, Jordan Anfal0Rababah@email.com
ORCID: 0009-0003-7450-8907
ABSTRACT
Despite widespread enthusiasm for artificial intelligence in education, implementation barriers constrain effective classroom integration. This study employed person-centered analytical approaches to examine AI integration patterns and constraint profiles among 189 K-12 educators across 32 schools in Jordan. Cross-sectional survey data assessed AI adoption, usage frequency, and four barrier types (cost, availability, technological skill deficits, language limitations), analyzed through co-occurrence analysis and hierarchical cluster analysis. AI adoption was high (84.7%), with teaching subject as the only significant demographic predictor. Among adopters, 78.3% reported constraints, predominantly technological skill (36.5%), cost (27.5%), availability (22.8%), and language barriers (7.9%). Co-occurrence analysis revealed significant inverse cost-skill relationships (φ=-.246, p<.001)—educators facing financial constraints showed 71% lower odds of reporting competency deficits. Hierarchical clustering identified eight distinct profiles: minimal constraints (51.3%), cost-dominant (20.1%), and availability-dominant (16.4%) patterns predominated. Findings demonstrate that barriers cluster into distinct educator profiles requiring differentiated interventions rather than universal support. Person-centered approaches reveal population heterogeneity invisible to conventional methods, enabling targeted implementation strategies. Institutions should conduct local constraint assessments to match resources with actual educator needs—cost-constrained contexts require tool access rather than training, while skill-deficit contexts need professional development rather than funding.
Keywords: artificial intelligence, educational technology implementation, barriers, cluster analysis, K-12 education, teacher development
Note: Survey instrument included as appendix.