Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Examining the impact of generative AI on student motivation and engagement: the mediating role of autonomy-support and autonomous motivation in education

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:

The rapid adoption of Generative Artificial Intelligence (GenAI) in higher education has transformed learning experiences; however, limited research has examined how psychological and motivational factors influence student engagement in AI-supported learning environments. Drawing upon Self-Determination Theory (SDT), Expectancy-Value Theory (EVT), and the Technology Acceptance Model (TAM), this study investigates the relationships among perceived autonomy, competence, relatedness, expectancy, value, autonomy support for AI use, autonomous motivation for AI use, student motivation, and student engagement. A quantitative research design was employed, and data were collected from 297 undergraduate and postgraduate students at King Saud University, Saudi Arabia. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived autonomy, perceived relatedness, and perceived value significantly enhanced autonomy support for AI use, while perceived autonomy, competence, and relatedness positively influenced autonomous motivation for AI use. Furthermore, autonomy support and autonomous motivation significantly increased student motivation, which subsequently emerged as the strongest predictor of student engagement. In contrast, perceived expectancy showed no significant influence on either autonomy support or autonomous motivation, while perceived competence did not significantly affect autonomy support. This study extends existing AI-in-education literature by integrating SDT, EVT, and TAM within a unified framework to explain student engagement in Generative AI-supported learning environments. Practically, the study provides valuable guidance for educators, instructional designers, and policymakers seeking to implement Generative AI technologies in ways that enhance meaningful learning experiences and sustainable student engagement in higher education.

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

Aldraiweesh Ahmed; Almutairy Sultan. Examining the impact of generative AI on student motivation and engagement: the mediating role of autonomy-support and autonomous motivation in education. Frontiers in Psychology (2026-06-30). https://doi.org/10.3389/fpsyg.2026.1852265

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.