Generative AI · Education leadership

Actionable companion · Instructional improvement

From evidence to district action: Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

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

Abstract Generative artificial intelligence (GAI) has attracted attention in education as a tool to help college students learn mathematics. This study analyzed the factors influencing their use of GAI by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) and focusing on mathematics motivation. This study involved 331 Chinese college students and used partial least squares structural equation modeling (PLS-SEM) for data analysis. The results showed that college students’ behavioral intention to use GAI to support their mathematics learning was directly influenced by performance expectancy, social influence, personal innovativeness, and mathematics motivation. Mathematics motivation, facilitating conditions, individual demand, and behavioral intention, had direct effects on college students’ use of GAI in mathematics. The most significant factor influencing both intention and behavior was mathematics motivation. Effort expectancy and individual demand did not affect the intention to use GAI in mathematics learning. In addition, there were important positive moderating effects, including individual demand, of mathematics motivation in the structural model on usage behavior and behavioral intention regarding usage behavior. The results of this study could help to identify the key influences on college students’ use of new technologies in mathematics learning and provide informative insights for the application of AI technologies in mathematics learning in the future. Keywords: generative artificial intelligence, mathematics learning, usage behavior, SEM

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 instructional unit and a specific teacher-supervised AI use. Compare it with the existing approach using a common learning objective, equivalent tasks and a shared rubric. 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. Measure unaided learning on a new task, factual errors, student workload and teacher review time. Disaggregate results where group sizes permit responsible interpretation. 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

Wenqian Lin; Peijie Jiang. Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behavioral Sciences (2025-03-02). https://doi.org/10.3390/bs15030295

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.