Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Learning agility, self-efficacy, and resilience as pathways to mental health in higher education: insights from a mixed-methods study

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:

Introduction This study examines how learning agility, academic self-efficacy, academic buoyancy, and psychological well-being interrelate to influence mental health and factors theoretically linked to academic success in undergraduate students. Methods Using an explanatory sequential mixed-methods design, quantitative data were gathered from 804 undergraduates using validated scales for each construct. Structural Equation Modeling (SEM) tested hypothesized relationships, and multi-group analysis explored gender differences in the model. In a qualitative phase, semi-structured interviews with 30 participants provided deeper insights into the quantitative findings, with thematic analysis of adaptive learning, resilience, and well-being. Results SEM findings showed that learning agility and academic self-efficacy positively predicted academic buoyancy and psychological well-being, with academic buoyancy partially mediating these relationships. Gender differences were non-significant, supporting model generalizability across genders. Qualitative analysis emphasized adaptive learning strategies, resilience in overcoming academic stressors, and psychological well-being as a process, with social support identified as essential in fostering resilience. Discussion The findings underscore the importance of learning agility, self-efficacy, and academic buoyancy in supporting students’ academic resilience and mental health. By enhancing these factors within academic settings, institutions can promote student well-being and engagement, reinforcing the link between psychological well-being and academic achievement.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · 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 adopted an explanatory sequential mixed-methods design, as described by Creswell and Plano Clark (2018). This approach involved collecting quantitative data first, followed by qualitative data to explain the quantitative results more comprehensively. The quantitative component aimed to investigate the relationships between learning agility, academic self-efficacy, academic buoyancy, and psychological well-being, utilizing SEM. The subsequent qualitative phase comprised semi-structured interviews to explore participants’ perspectives on how these constructs function in their academic experiences. The mixed-methods design allowed for both breadth and depth, with the quantitative results providing generalizable findings and the qualitative component offering rich, detailed insights (Creswell and Plano Clark, 2018).

Limitations and uncertainty · source passage

Despite its strengths, this study has several limitations that temper the interpretation and scope of our findings. First, our reliance exclusively on self-report measures for all constructs may introduce response biases, such as social desirability, or inaccuracies due to varying levels of self-perception among participants. While the instruments demonstrated high internal consistency, the subjective nature of self-reporting means that the magnitude of the relationships observed might be influenced by shared method variance or individual reporting styles. Future studies could enhance validity and mitigate these potential biases by incorporating objective measures or by employing multi-source data, for instance, instructor assessments of observable learning behaviors where feasible. Furthermore, while our study’s conceptual framework suggests pathways that may influence academic success, its primary empirical focus and outcome measurement centered on psychological well-being. Consequently, a notable limitation is the absence of comprehensive, objective measurement and in-depth analysis of academic achievement (e.g., grade point averages, standardized test scores, or course completion rates) within the current investigation. This restricts our ability to draw direct, empirically grounded conclusions from this dataset concerning the tangible impact of learning agility, self-efficacy, and academic buoyancy on students’ actual academic attainments. Future research should therefore prioritize the integration of such objective academic achievement data to more fully elucidate the practical implications of the proposed psychological pathways for both well-being and educational success.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • 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

He Huang; Heung Kou. Learning agility, self-efficacy, and resilience as pathways to mental health in higher education: insights from a mixed-methods study. Frontiers in Psychology (2025-07-02). https://doi.org/10.3389/fpsyg.2025.1528066

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.