Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Exploring the influencing factors of academic doctoral students’ academic innovation behavior in the context of generative artificial intelligence: Self-determination theory and motivation-opportunity-ability perspectives

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 Purpose Although generative artificial intelligence (GenAI) has increasingly reshaped academic research practices, existing studies have mainly examined its technical affordances or isolated factors such as AI literacy. Less attention has been paid to how different types of academic motivation are translated into doctoral students’ academic innovation behavior under varying opportunity and ability conditions. Drawing on Self-Determination Theory and the Motivation-Opportunity-Ability framework, this study examines the effects of autonomous motivation, controlled motivation, and amotivation on academic doctoral students’ academic innovation behavior, and further investigates the moderating roles of resource conditions, encouragement and support, creative self-efficacy, and AI literacy. Design/methodology/approach Based on survey data from 502 academic doctoral students, this study tested the direct effects of autonomous motivation, controlled motivation, and amotivation on academic innovation behavior. It also examined whether resource conditions, encouragement and support, creative self-efficacy, and AI literacy moderated the relationships between motivation and academic innovation behavior. Data were analyzed using SPSS 27.0, AMOS 26.0, and PROCESS 4.0. Findings Autonomous motivation and controlled motivation positively predict academic innovation behavior, whereas amotivation negatively predicts such behavior. Resource conditions, encouragement and support, creative self-efficacy, and AI literacy significantly moderate these relationships. Specifically, these opportunity and ability factors strengthen the positive effects of autonomous and controlled motivation on academic innovation behavior and buffer the negative effect of amotivation. Research implications Theoretically, this paper complements the integrated application of SDT and MOA in doctoral education research and enriches the multi-factor moderating mechanism of academic innovation under GenAI context. Practically, universities need to build supportive academic environments that combine motivational support, adequate research resources, responsible GenAI training, and the cultivation of creative self-efficacy. Originality It clarifies how motivation, opportunity, and ability jointly shape academic doctoral students’ academic innovation behavior, thereby providing a more nuanced explanation of doctoral students’ innovation in the GenAI-supported academic environment.

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. 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.
  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, 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

Yanqin Liu; Miao Zhang; Yuwen Peng. Exploring the influencing factors of academic doctoral students’ academic innovation behavior in the context of generative artificial intelligence: Self-determination theory and motivation-opportunity-ability perspectives. PLOS One (2026-08-06). https://doi.org/10.1371/journal.pone.0355186

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.