Cognitive offloading—delegating cognitive work to external tools—is basic to human cognition, but generative AI (GenAI) amplifies it radically: entire cognitive products can now be produced on request. This sharpens a question about agency over one’s own thinking: when cognition is habitually offloaded, does its product still feel like one’s own? We surveyed 239 pre-service teachers, operationalizing the felt loss of cognitive ownership as concern over the erosion of teaching-design subjectivity (TSC)—the metacognitive appraisal that AI-assisted work is not genuinely one’s own and that independent capacity is declining. Guided by the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, we tested whether AI anxiety/fear of missing out (affective) and impulsivity (self-regulatory) relate to this concern through behavioral GenAI dependency—habitual offloading. In a structural equation model with bias-corrected bootstrapping, dependency strongly predicted TSC and partially mediated the effect of AI anxiety; impulsivity raised dependency but showed a suppression pattern (a positive indirect effect offset by a null total effect), and the model explained 35% of the variance in the concern. Habitual offloading—that is, GenAI dependency—rather than generic AI use, accompanies the metacognitive loss of cognitive ownership.

Generative artificial intelligence (GenAI) allows university teachers to delegate cognitive work while deciding how students should engage with it. Using codebook thematic analysis, we examined multi-source accounts from 25 teachers recruited through an AI-focused learning community and its professional networks in China. Materials comprised interviews with 22 teachers, three documentary cases, self-narratives and reflective essays. Three themes organize the findings: reconstructing the teacher role, defending professional judgment, and managing teachers’ and students’ cognitive participation. Participants described disciplinary expertise as a basis for evaluating AI outputs and reported practices intended to retain human engagement. Instrumental and partnership orientations overlapped; descriptive comparison with later use profiles did not establish a simple relationship between partnership language and usage intensity. Because the protocol introduced several focal topics, we distinguish prompted positions from volunteered formulations. The proposed belief–strategy–practice cycle is a heuristic interpretation of retrospective accounts, not a demonstrated mechanism. The study contributes an account of how teachers connect their own cognitive offloading with their reported organization of students’ learning opportunities. Its claims concern this AI-engaged sample rather than university teachers generally.

Generative artificial intelligence (GenAI) accelerates programming, but its educational value depends on whether students check generated code before accepting it. This concurrent mixed-methods study examined which beliefs accompany such checking among computer-science students at one Chinese university. Survey responses from 294 students were analyzed with an exploratory structural equation model treating three practices as separate outcomes: checking code line by line before use, practicing without AI, and declining in advance to delegate. Robustness checks included ordinal estimation, acquiescence modeling, split-half replication, and multiverse analysis; eleven students were interviewed. Verification-dispensability belief, endorsing reasons for treating further checking as unnecessary, was the only construct negatively associated with all three practices in every robustness check. Other beliefs were narrower or inert: perceived AI literacy accompanied line-by-line checking alone, generic metacognitive self-regulation none. Delegation breadth, the number of activities handed to GenAI, was unrelated to all three: volume of delegation said little about checking. Interviews exposed a mismatch between self-reported categories and actual workflows: students allocated checking by task purpose, accepted process-level understanding, and delegated verification to other AI models, a practice unmeasured by the survey. The data are cross-sectional and self-reported, so these are associations requiring behavioral and longitudinal confirmation.

Critical thinking (CT) is a core competence that schools are expected to cultivate, yet whether it reaches classrooms depends on teachers’ readiness to teach it. Using an explanatory sequential mixed methods design, we surveyed 312 primary English-as-a-foreign-language teachers in Shandong Province, China, across seven dimensions of readiness and interviewed five teachers. Latent profile analysis identified four profiles organized by two weakly related dimensions—the capacity teachers bring to CT teaching and the constraints they perceive around them: Empowered-Engaged (15%), Willing-but-Constrained (28%), Moderate-Unobstructed (20%), and Committed-but-Under-resourced (37%). Endorsement of CT was near-universal, so the profiles differed in capacity and circumstance rather than in commitment. The central finding is a disconnection between what teachers believe and what they report doing: Willing-but-Constrained teachers wanted further professional learning as much as their Empowered-Engaged peers did, yet reported teaching CT less often. The divergence held under methods that account for classification uncertainty and under alternative measurement specifications, though not under all of them, and profile membership was largely unrelated to teachers’ background characteristics. The interview findings help explain why positive beliefs about CT did not always correspond to classroom practice. Teachers reported limited CT-specific training, examination pressure, time constraints, and norms that discouraged questioning, despite generally recognizing the value of CT. This suggests that the main barriers to CT instruction may lie not in teachers’ attitudes toward CT, but in their opportunities, preparation, and working conditions for implementing it.