Actionable companion · Motivation & engagement
Learning with agency: The influencing factors, diversification trends, and motivation-enhancement strategies of learning motivation among undergraduate students in Chinese application-oriented universities in the digital age: a grounded theory analysis based on Y University
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.
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:
Artificial intelligence technology is accelerating the transformation of the teaching ecosystem in universities, and as a result, the learning motivation of college students is profoundly affected. To reveal the core influencing factors and mechanism of learning motivation for Chinese application-oriented university students in the digital era, using grounded theory, 30 undergraduate students from Y University were selected as the research subjects. Through semi-structured interviews, original data were collected. Through open coding, axial coding, and selective coding, concepts and categories were refined, and a theoretical saturation test was conducted. The study found that the learning motivation of undergraduate students in application-oriented universities in the digital era is driven by four main categories: the core driving layer (AI-specific dimension, professional cognition and expectations, self-awareness, self-achievement, internal drive), the basic guarantee layer (school resource conditions, course evaluation methods), the relationship influence layer (teacher and peer influence, family influence), and the external constraint layer (student behavior constraints, autonomous time allocation). It presents a diversified trend and ultimately forms a “cognition-context” dual-drive influence mechanism model. Based on this, from the aspects of internal cognitive stimulation, external condition improvement, interpersonal relationship drive, and learning situation creation, practical paths are provided for students in application-oriented universities to adapt to the development trend of the digital era and stimulate their learning motivation.
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.
Limitations and uncertainty · source passage
Based on current research, the field of learning motivation has developed a theoretical system centered on the Self-Determination Theory and the Self-Efficacy Theory. However, there are two limitations in related research: Firstly, the existing studies on the relationship between artificial intelligence and college students’ learning motivation mostly select students from public comprehensive universities as the research sample, and rarely focus on the application-oriented university group. Application-oriented universities focus on vocational training and practical job skills acquisition as their core training orientation. The AI career substitution anxiety, access to digital learning resources, and utilitarian learning demands faced by students in application-oriented universities are significantly different from those in public universities. The existing results cannot fully explain the unique logic of the formation of this group’s learning motivation; Secondly, current research on digital education mostly focuses on the development of AI teaching tools and the optimization of online classroom technologies, lacking qualitative in-depth exploration from the perspective of students’ subjective psychology. Many quantitative studies only verify the correlation of a single variable, lacking a grounded theory that self-organizes from the bottom up to refine a localized motivation model, and failing to fully capture the multi-layer influence paths of AI over-reliance, digital self-efficacy, and other psychological variables specific to the new era on learning motivation. The existing research has not yet established a complete framework for the influence mechanism of motivation in China’s application-oriented university students in line with the digital age.
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
- 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.
- 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.
- 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.
- 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
Menglu Guo; Qiaoxia Wang; Li Zhang; Linzhi He. The influencing factors, diversification trends, and motivation-enhancement strategies of learning motivation among undergraduate students in Chinese application-oriented universities in the digital age: a grounded theory analysis based on Y University. Frontiers in Psychology (2026-09-01). https://doi.org/10.3389/fpsyg.2026.1902017
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.