Actionable companion · Staff capability and AI literacy

From evidence to district action: Bibliometric analysis of gamification in teacher education during the generative AI era: historical trends, present status, and future trajectories

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.

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:

Gamification has become a relevant pedagogical approach in teacher education, yet its evolution and relationship with generative artificial intelligence remain fragmented. This study maps the development, intellectual structure, and emerging trajectories of research on gamification in pre-service and in-service teacher education between 2016 and 2025. A bibliometric analysis was conducted on an integrated Scopus and Web of Science dataset. After merging records, removing duplicates, and applying predefined eligibility criteria, 1,223 documents were retained. Performance analysis, co-citation analysis, keyword co-occurrence analysis, and thematic evolution mapping were conducted in Biblioshiny/Bibliometrix, with study identification and selection documented through the PRISMA framework. Scientific output increased markedly over the study period, especially from 2020 onward, and was led mainly by institutions and countries with strong educational innovation capacity. The co-citation network revealed three intellectual foundations: motivational theories of gamification, technology adoption and educational innovation models, and empirical work on game-based learning and serious games. Keyword co-occurrence analysis identified seven thematic clusters organized around gamification, education, artificial intelligence, teacher training, and digital learning. By 2025, the field shows a clear thematic shift toward generative artificial intelligence, adaptive systems, and automated personalization. Research on gamification in teacher education has moved from a focus on motivational design and digital engagement toward more intelligent, data-driven, and adaptive ecosystems. The findings highlight a rapidly expanding field and provide an evidence-based agenda for teacher education, institutional policy, and future studies on the pedagogical and ethical integration of GenAI.

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. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. 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 error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. 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

Miguel Alberto Rincón Pinzón; Ana Dolores Vargas Sánchez; Leonardo David Glasserman Morales. Bibliometric analysis of gamification in teacher education during the generative AI era: historical trends, present status, and future trajectories. F1000Research (2026-06-23). https://doi.org/10.12688/f1000research.178620.2

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.