Actionable companion · Motivation & engagement

Learning with agency: Enhancing the sustainability of cultural identity in science curricula through artificial intelligence as an innovative educational approach

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Adult & professional learning. Verify the participant population and task before transferring the findings.

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

This study investigates the role of AI as an innovative educational approach in promoting the sustainability of cultural identity within science curricula. To achieve this aim, a qualitative research design was employed, using semi-structured interviews and classroom observations as primary data collection methods. The study sample consisted of forty-seven science teachers from the Al-Qassim and Al-Ahsa regions in Saudi Arabia, selected purposively based on their experience with technology-enhanced instruction. The collected data were analyzed using systematic qualitative procedures involving open and axial coding, which facilitated the emergence of key categories related to AI integration, cultural relevance, and instructional practices. Accordingly, the analytical process is best understood as an inductive thematic qualitative approach rather than a full grounded theory methodology, as it does not include theoretical sampling, data saturation, or iterative cycles of data collection. This clarification ensures methodological transparency and alignment between design and analysis. Findings derived from both teachers’ perceptions and classroom observations indicate that AI is not perceived merely as a tool for knowledge transmission, but rather as a supportive instructional mechanism that enables the contextualization of scientific concepts within local cultural settings. In addition, AI-supported instructional practices appear to foster more interactive and collaborative classroom environments, while also contributing to more responsive approaches to assessment. However, it is important to emphasize that these findings are based on teachers’ reported perceptions and observed instructional practices rather than direct measurement of student learning outcomes. Despite this limitation, classroom observations suggest that integrating culturally relevant examples, analyzing cultural texts, and designing collaborative, culturally responsive learning activities may enhance student engagement and strengthen their connection to cultural identity within science learning contexts. Overall, the study highlights the potential of AI to bridge scientific knowledge and local cultural heritage in science education. It contributes to the growing body of literature on AI in education by emphasizing its role in fostering culturally responsive pedagogy and enhancing meaningful learning experiences. The study further underscores the importance of providing teachers with targeted professional development in AI integration, developing culturally responsive instructional materials, and designing assessment practices that address both conceptual understanding and cultural dimensions of learning.

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. Use a bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. 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. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. 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

Jawaher Alrashood; Omayya Al-Hassan; Sarah Alotaibi; Tahani Alrosaa; Rommel AlAli; Ali Al-Barakat; Abdelrahim Ismail; Yusra Aboud; Ali Abdullatif; Ashraf Zaher. Enhancing the sustainability of cultural identity in science curricula through artificial intelligence as an innovative educational approach. PLOS One (2026-07-21). https://doi.org/10.1371/journal.pone.0353777

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.