@article{CHEN2026, 
author = {Ying CHEN and Fang-Hua ZHANG and Lei-Xi WANG and Ye-Ye WANG},
title = {How to Address Cognitive Evasion Induced by Generative Artificial Intelligence?—An Action Research Based on Ecological Intervention},
year = {2026},
journal = {Modern Educational Technology},
volume = {36},
number = {8},
pages = {118-127},
keywords = {GenAI, cognitive evasion, feigned compliance, ecological intervention, action research},
url = {https://www.sciopen.com/article/10.3969/j.issn.1009-8097.2026.08.012},
doi = {10.3969/j.issn.1009-8097.2026.08.012},
abstract = {While generative artificial intelligence (GenAI) empowers higher education practice, it also induces students’ cognitive evasion by dispeling desirable difficulties. Confronted with instructional intervention, such “cognitive evasion” can easily escalate into “feigned compliance”, which can hardly be identified and curbed by traditional outcome-oriented assessment. Accordingly, taking the “BROKE Incident” as its starting point, this paper adopts action research, designs the analytical framework for ecological intervention, and implements three rounds of progressive ecological interventions. The findings reveal that cognitive evasion is essentially learners’ abdication of deep cognitive processing, which can easily evolve into feigned compliance under supervisory pressure. Ecological intervention with three-dimensional collaboration of value, mechanism and power can reduce the benefit space for cognitive evasion and feigned compliance, thereby restoring conditions for desirable difficulties to take place. Under ecological intervention, three typical reaction patterns emerge: patterned transformation, strategic fixation, and compliance adaptation. Based on this, four pathways are proposed to address cognitive evasion induced by GenAI effectively: clarifying cognitive boundaries of human-machine collaboration, preserving process evidence of key nodes, employing diverse questioning, and engaging in reflective revision of works. The research of this paper helps sustain desirable difficulties and foster deep learning in the AI era, also can provide references for bounded human-machine collaborative learning.}
}