Actionable companion · Assessment and evidence of learning

From evidence to district action: Research on the application behavior of generative artificial intelligence learning of college students based on self-determination theory

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 Introduction In recent years, the application of generative artificial intelligence (GAI) in higher education has gained increasing prevalence, accompanied by a concerning rise in student “over-reliance”. Such excessive dependence can undermine critical thinking and hinder innovation development. Consequently, guiding college students toward a “deep processing” mode of GAI use has become a crucial issue in higher education. Methods Grounded in self-determination theory, this study constructs a model to examine the factors influencing college students’ GAI usage behaviors. To test the proposed model and its hypothesized relationships, An empirical study has been conducted,including survey instrument distribution and path analysis. Results The main findings emerge: First, among basic psychological needs, perceived competence plays a pivotal role in curbing over-reliance and promoting deep processing, exerting a stronger influence than perceived autonomy or relatedness. Second, all three basic psychological needs (perceived autonomy, perceived competence, and perceived relatedness) indirectly influence deep processing and over-reliance through intrinsic motivation. Discussion The hypotheses proposed in the article has been verified. Based on these findings, practical suggestions are proposed across multiple levels to foster more reflective and self-determined GAI use, including constructing a need-supportive Learning Ecosystem, building a “guided” GAI tool, establishing a hierarchical support and constraint framework, fostering a shift from instrumental to alue rationality. Keywords: deep processing, generative artificial intelligence, learning behavior, over-dependence, self-determination theory

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Xu Fang; Junci Feng. Research on the application behavior of generative artificial intelligence learning of college students based on self-determination theory. Frontiers in Psychology (2026-04-01). https://doi.org/10.3389/fpsyg.2026.1805498

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.