Using context-based and AI-enhanced approaches to improve student engagement and achievement in secondary chemistry education

Abdikayumova N. Madybekova G.
2026 Walter de Gruyter GmbH

Chemistry Teacher International
2026

Grounded in the need to modernize science education and promote learner-centred approaches, this study examined the effectiveness of integrating context-based 7E instructional strategies, rooted in constructivist learning theory, with AI-supported tools such as PhET Interactive Simulations and ChatGPT tutoring to improve secondary students’ achievement and engagement in chemistry. A mixed-methods, quasi-experimental design was employed with 93 Grade 10 students assigned to three instructional groups: (a) context-based 7E with integrated AI tools, (b) the 7E model without contextual or AI components, and (c) conventional teaching. Over 12 weeks, the experimental group engaged with digital simulations and AI tutoring embedded within the 7E phases. Quantitative data were collected through pre- and post-tests and engagement scales, while qualitative evidence was gathered from observation checklists, reflection forms, and AI usage logs. ANCOVA results showed that students in the experimental group achieved significantly higher post-test scores than those in the comparison and control groups, with a large effect size. Engagement analysis also indicated that the experimental group reported the highest levels of interest and participation. These findings align with prior research showing benefits of context-based instruction and AI tools independently, but extend the evidence by demonstrating that their integration within a structured inquiry cycle produces even greater gains. Despite these promising outcomes, the study has limitations, including its single-site setting, small sample size, and reliance on self-reported engagement measures. Future research should test the approach with broader samples, examine long-term effects, and address ethical considerations of AI integration. Overall, the results provide evidence that combining contextual activities, inquiry-based learning, and adaptive technologies can foster more meaningful and effective chemistry learning experiences.

7E instructional model , artificial intelligence , chemistry education , context-based learning , student engagement

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South Kazakhstan Pedagogical University Named After Ozbekali Zhanibekov, Shymkent, 160000, Kazakhstan

South Kazakhstan Pedagogical University Named After Ozbekali Zhanibekov

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026