Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: Adaptive Expertise of College English Teachers in the Era of Artificial Intelligence: A Grounded Theory Approach

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education, Adult & professional learning. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

The rapid advancement of artificial intelligence (AI) and the continuous reform of English education in China have greatly reshaped the professional requirements for college English teachers. In contrast to the predominance of general teacher perspectives in adaptive expertise research, college English teaching offers a discipline-specific context in which adaptive expertise becomes particularly salient in response to AI-related pedagogical change. This study adopts a grounded theory approach to explore the qualities that college English teachers should possess in the era of artificial intelligence. A five-dimensional dynamic model of adaptive expertise was developed, consisting of knowledge expertise, competence expertise, vision expertise, emotional expertise, and development expertise. It shows that knowledge expertise embodies the integration of multiple knowledge domains required in the AI era; competence expertise emphasizes the ability to design and implement AI-enhanced instruction; vision expertise reflects global and future awareness in AI-technology integration; emotional expertise sustains resilience and motivation amid technological change; and development expertise promotes lifelong learning and innovation. These dimensions transfer, enhance, and inspire one another, forming a closed and self-reinforcing loop. It enriches the understanding of teacher professionalism in the AI era and offers a framework for cultivating AI-resilient expertise in higher education.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Higher education; Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The study involved 40 college English teachers recruited from ten universities across different regions of China. A purposeful sampling strategy with maximum variation (Cresswell, 2013) was adopted to ensure diversity in terms of institutional type, professional title, educational background, and teaching experience. Informed consent was obtained prior to the interviews. Ethical approval for this research was granted by the authors’ affiliated institution. Data were collected through semi-structured interviews, conducted either in person or online, depending on the participants’ schedules and locations. The demographic characteristics of the 40 college English teachers are summarized in Table S1 in the Supplementary Materials. Each interview lasted for 25–30 min. All interviews were conducted in Chinese and recorded with the participants’ consent. The recordings were transcribed word-for-word, and the Chinese transcripts were subsequently translated into English by the first author. To ensure accuracy and linguistic equivalence, the translations were checked by a bilingual colleague specializing in applied linguistics.

Limitations and uncertainty · source passage

Nevertheless, this study has its limitations. It focuses on the qualitative identification of the components and relationships within adaptive expertise but does not empirically test their effects on teaching performance. Future research should quantitatively validate this model, explore how adaptive expertise evolves over time, and conduct comparative studies across different cultural and institutional contexts. Such efforts will further clarify how teachers can retain human-centered professionalism while effectively co-evolving with intelligent technologies in higher education.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Qi Zhou; Luming Hu; Xintong Zou; Xuemin Zhang. Adaptive Expertise of College English Teachers in the Era of Artificial Intelligence: A Grounded Theory Approach. Behavioral Sciences (2026-03-24). https://doi.org/10.3390/bs16040476

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.