ArticlePloS one2026
Cognitive load and teachers' innovative behavior in AI-enhanced English language instruction: A mediation analysis of technological adaptability.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This investigation examines the complex interrelationships between teachers' cognitive load, technological adaptability, and innovative teaching behavior in AI-enhanced educational environments. Through the integration of Cognitive Load Theory, Technology Acceptance Model, and Innovation Diffusion Theory, we develop a sophisticated theoretical framework for understanding how cognitive demands influence teaching innovation through adaptive mechanisms. Employing exploratory structural equation modeling with WLSMV estimation (N = 600), we analyze data collected through rigorously validated instruments measuring AI-assisted Teaching Cognitive Load (ATCL), technological adaptability, and innovative teaching behavior. Results reveal a significant negative relationship between cognitive load and innovative teaching behavior (β = -.134, p < .001), mediated by technological adaptability (indirect effect β = -.171, p < .001). The measurement model demonstrates exceptional psychometric properties (α = .91-.93; AVE = .64-.68) and establishes measurement invariance across teacher subgroups (ΔCFI ≤ .001). These findings advance theoretical understanding of cognitive-adaptive mechanisms in technology-enhanced teaching while providing empirically validated pathways for enhancing pedagogical innovation. The study contributes methodologically through the development of the ATCL scale and analytically through sophisticated mediation analysis techniques. Implications extend to professional development strategies, institutional policy formulation, and the theoretical conceptualization of cognitive load in AI-enhanced educational environments.
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