Teachers as reflective regulators of cognition: Understanding cognitive offloading in AI-augmented practice

Synthesis: Ho and Chen (2026) investigated how in-service teachers perceive, manage, and reflect on the cognitive implications of integrating generative AI into their professional practice. Using a collective case-study design, they interviewed 18 teachers in mainland Chinese and Hong Kong settings and applied the Cognitive Offloading framework to guide data collection and thematic coding. Three interrelated processes emerged: recognizing and evaluating the need to use GenAI, redistributing cognition between human and machine systems, and reflectively re-engaging after use. Teachers generally framed GenAI not as a substitute for thinking but as a partner in cognitive Regulation shaped by institutional and ethical contexts. Building on Risko and Gilbert's framework, the study proposes a metacognitive ecology lens — situated relationships through which teachers monitor cognitive demands, evaluate GenAI as an external resource, regulate reliance, and maintain pedagogical responsibility — for understanding how cognitive monitoring, pedagogical interpretation, ethical boundary-setting, and institutional conditions shape GenAI-mediated cognitive redistribution.

Core Finding

Teachers' engagement with GenAI is not mere tool adoption but metacognitive negotiation: they consciously decide which cognitive tasks to delegate to AI and which to keep human-led, monitoring, evaluating, and reinterpreting the boundary between machine assistance and professional judgment. Crucially, offloading is not a purely internal cognitive act — it is socially scaffolded and institutionally conditioned, shaped by workload pressure, curiosity, school policy, and technological readiness. The study introduces metacognitive ecology as an interpretive lens connecting these cognitive and contextual dimensions.

Three Interrelated Processes

The analysis consolidated an author-developed six-stage operationalization of cognitive offloading (recognising demand → evaluating external resources → selecting tasks → enacting offloading → observing consequences → reflecting on adjustment/drift) into three broader themes.

1. Recognising and evaluating the need to use GenAI — Workload and time pressure operated as direct cognitive-demand triggers. Professional exploration (curiosity), school policy mandates, and technological readiness acted as contextual conditions that shaped teachers' metacognitive evaluation of GenAI as a legitimate, accessible, trustworthy external resource, rather than as triggers themselves.
2. Using GenAI for operational and cognitive redistribution — Four forms of redistribution emerged: procedural task automation (rule-based, e.g., scheduling/formatting), linguistic/Feedback/pedagogical data-analytical delegation (grammar correction, oral evaluation, error summarisation), generative design and ideation expansion (brainstorming, example generation), and — at a broader workflow level — externalization of the teaching cycle (integrating AI across planning, instruction, Assessment, and feedback). In all forms, teachers retained interpretive and evaluative control.
3. Reflectively re-engaging after use — Post-use reflection varied across four orientations (procedural, value-based, affective, identity-anchored) at three depths (surface, intermediate, meta-transformative). Depth was not determined by intensity of AI use but by metacognitive awareness and institutional or collegial support.

The Risk of Professional Drift

The authors warn that AI increasingly externalizes deep epistemic operations — constructing examples, designing tasks, creating assessment rubrics — which Risko and Gilbert term deep offloading. While this enhances productivity, it risks transferring foundational professional cognition to algorithms, contributing to professional drift (adapted from cognitive drift): a gradual erosion of creative and evaluative reasoning and reduced attentional/emotional engagement with students. Teachers mitigate this by monitoring, editing, and interpreting AI outputs, but the ease of offloading can nonetheless erode judgment.

Reflective Depths and Orientations

Reflection was heterogeneous. Some heavy AI users reflected only superficially (satisfied with efficiency, unaware of cognitive/ethical implications), while some light users demonstrated deep, meta-transformative reflection — even redefining authorship and empathy. Four orientations mapped onto three depths: procedural reflection evolved from task audit to epistemic reconsideration ("what makes my thinking different?"); value-based reflection moved from compliance to ethical re-centering ("care is still my responsibility"); affective reflection progressed from emotional detachment ("efficient but cold") to reconceptualising empathy as epistemic strength; and identity-anchored reflection deepened from viewing AI as a design assistant to affirming that authorship and authentic voice are inseparable from professional integrity. Collegial exchange and institutional encouragement enabled deeper reflection.

Contextual Variation

Cognitive offloading varied with teaching experience, school level, school type, and institutional role. Early-career teachers used GenAI for confidence-building and material generation; experienced teachers and leaders focused on workflow redesign, oversight, and boundary-setting. Primary teachers offloaded Scaffolding and age-appropriate materials; secondary teachers restructured disciplinary content and designed assessments. Digitally supported or policy-driven schools legitimised and expected use, while less-resourced settings faced infrastructure and training constraints. Platform ecosystems also mattered: Hong Kong teachers used globally available tools (ChatGPT, Gemini, Copilot, Claude, MagicSchool.ai), while mainland teachers used Chinese platforms (Doubao, Kimi, Ernie Bot, SparkDesk), and these shaped what teachers deemed feasible and legitimate to delegate.

Implications

• AI literacy must extend beyond technical proficiency to reflective awareness and evaluative judgment about when, why, and how to delegate cognitive tasks. Guided reflection cycles (recognition, evaluation, delegation, observation, re-engagement) can support deliberate, ethical AI use.
• Teachers need pedagogical data literacy to interpret AI-generated analytics, identify what is highlighted or obscured, and translate outputs into instructional decisions without over-reliance on automated indicators.
• Schools should cultivate reflective ecosystems — collaborative design labs and professional learning communities that normalise open discussion of the cognitive, ethical, and emotional challenges of AI use.
• Policy should balance efficiency with cognitive wellbeing, moving toward governance that monitors cognitive dependence, promotes reflective use, and safeguards teachers' attentional health (e.g., mandated reflection sessions, workload reviews, continuous ethical training).

Connected Concepts

• Cognitive Offloading
• Generative AI
• Teacher Role
• Metacognition
• AI Literacy
• Higher Ed
• Ethics
• Feedback

Connected Articles

• Cognitive Offloading Metacognitive Review 2026 — Cognitive offloading and metacognition review
• Gerlich AI Tools Cognitive Offloading Critical Thinking — AI tools, cognitive offloading and critical thinking
• Lodge Loble Cognitive Offloading 2026 — Cognitive offloading and learning

Citation

Ho, C. S. M., & Chen, J. (2026). Teachers as reflective regulators of cognition: Understanding cognitive offloading in AI-augmented practice. Computers and Education: Artificial Intelligence, 11, 100670.