Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Career adaptability under AI-related employment pressure: a multi-method study of relational structure and configurational pathways

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. Verify the participant population and task before transferring the findings.

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

Objective This study examines the factors, relational structure, and exploratory configurational associations related to career adaptability among university students in the school-to-work transition under artificial intelligence-related employment pressure. Methods Grounded primarily in Social Cognitive Career Theory and supplemented by Career Construction Theory, this study draws on questionnaire data from 564 Chinese university students and integrates structural equation modelling, mediation analysis, multigroup analysis, necessary condition analysis, and fuzzy-set qualitative comparative analysis. Results Career adaptability was directly associated with multidimensional cognitive, behavioural, and contextual factors, as well as with indirect pathways involving outcome expectations and goal reengagement intention. However, the serial indirect effects were generally weak, indicating that career adaptability was not explained primarily by a single sequential cognition–action pathway. The overall relational structure was largely stable across undergraduate and postgraduate groups, with differences in the strength of only a few paths. Necessary condition analysis identified no single necessary condition with a substantial practical effect, whereas the exploratory configurational analysis identified multiple combinations of conditions associated with higher career adaptability, suggesting substitution and compensation among different resources. Artificial intelligence threat perception was not associated exclusively with poorer adaptation; under specific configurations of conditions, it coexisted with higher career adaptability. Conclusion Career adaptability in an employment context shaped by artificial intelligence is not determined by a single factor; rather, it represents a complex adaptive structure jointly associated with multiple cognitive, behavioural, and contextual resources. By combining the explanation of the relationship between career cognition and action offered by Social Cognitive Career Theory with the multi-resource perspective on adaptation provided by Career Construction Theory, this study advances understanding of career adaptability in contexts where artificial intelligence-related pressure and opportunities for collaboration coexist. The findings suggest that career education and employment support in universities should not be confined to training in a single skill. Instead, they should integrate capability beliefs, career judgements, technology-related practice, goal adjustment, and external support to provide diverse career preparation pathways for students with different resource profiles.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education. 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

This study understands the “school-to-work transition” as a continuous developmental stage in which individuals gradually move from student to occupational roles and progress from general career exploration to career choice, career preparation, and labour-market entry. On this basis, the study surveyed university students in China, focusing on third- and fourth-year undergraduates and postgraduate students, including master’s and doctoral students. Compared with students in the first two undergraduate years, third- and fourth-year students have entered the later stage of higher education. Their learning tasks gradually shift from foundational coursework to the integration of professional capabilities, internships and practical experience, and decisions about post-graduation destinations; career choice has therefore become a practical task requiring concrete judgement and preparation. Postgraduate education has a more explicit emphasis on specialisation and career direction, and postgraduate students likewise need to address occupational positioning, skill matching, and employment choices after completing their degrees. Although the sample covers university students at different stages of study, it is therefore concentrated among senior undergraduates and postgraduates whose career-development tasks have shifted from general exploration to concrete employment decisions and preparation.

Limitations and uncertainty · source passage

Although this study systematically analysed the relational structure and configurational associations of career adaptability among university students in the school-to-work transition through an integrated multi-method framework, it has several limitations. First, the study used cross-sectional questionnaire data. The results therefore primarily represent statistical associations and cannot establish the temporal ordering of outcome expectations, goal reengagement intention, and career adaptability or capture dynamic changes in career adaptability. The SEM also did not include demographic variables such as gender, discipline, and type of institution as controls, and potential confounding cannot be completely excluded. Future research could use longitudinal or multiwave designs and a more comprehensive set of controls to examine the direction of the relationships. Second, convenience sampling limits the generalisability of the findings. The postgraduate sample was relatively small and group sizes across educational stages were unequal, which may have affected the stability of path estimates and the statistical power of group-difference tests. Future studies could replicate the analyses with larger and more balanced samples. Third, the study examined career adaptability primarily from the perspective of individual university students and did not incorporate assessments by employers or other hiring organisations. Future research could introduce data on job requirements and organisational contexts and extend the analytical framework from the perspectives of supply–demand fit or cross-level interactions. Fourth, although the representative configurations exhibited relatively high individual consistency and the principal patterns were retained in the robustness analysis, the overall solution consistency remained comparatively limited. The configurational findings should therefore be interpreted as exploratory associations observed within the current sample rather than as definitive sufficient pathways. Future research could examine the stability of these patterns using independent samples, alternative calibration and threshold settings, and longitudinal or multiwave designs.

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.

  • 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

Dan Chen; Jinling Wang. Career adaptability under AI-related employment pressure: a multi-method study of relational structure and configurational pathways. Frontiers in Psychology (2026-09-09). https://doi.org/10.3389/fpsyg.2026.1923965

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.