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Using Context-Based and AI-Enhanced Approaches to Improve Student Engagement and Achievement in Secondary Chemistry Education

● high · 2026-08-19 · chemistry-education, k-12, constructivist, personalized-learning, student-engagement, motivation

Synthesis: Abdikayumova and Madybekova (2026) use a mixed-methods, quasi-experimental design with 93 Grade 10 students in three instructional groups — (a) context-based 7E instructional model integrated with AI tools, (b) the 7E model without contextual/AI components, and (c) conventional teaching — over 12 weeks of secondary chemistry. The experimental group, using PhET interactive simulations and ChatGPT tutoring embedded within the 7E phases, achieved significantly higher post-test scores (large effect size, ANCOVA) and reported the highest engagement. The authors argue that contextualization, structured inquiry, and adaptive AI act synergistically: contextual activities connect abstract chemistry to everyday life, the 7E cycle structures reasoning, and AI provides real-time clarification and tailored explanations.

Design and intervention

  • Sample: 93 Grade 10 secondary students, three groups: context-based 7E + AI tools (experimental), 7E without context/AI (comparison), conventional teaching (control).
  • Duration: 12 weeks.
  • AI tools: PhET interactive simulations and ChatGPT-based tutoring embedded within the 7E inquiry phases.
  • Measures: pre-/post-tests and engagement scales (quantitative); observation checklists, reflection forms, and AI usage logs (qualitative).

Findings

  • Achievement: ANCOVA showed the experimental group scored significantly higher than both comparison and control groups, with a large effect size.
  • Engagement: experimental-group students reported the highest levels of interest and participation.
  • Synergy of components: the three elements — contextualization, inquiry (7E), and adaptive AI — functioned synergistically. Contextual activities increased relevance and intrinsic motivation; the 7E cycle structured exploratory reasoning; AI provided real-time clarification and tailored explanations, reduced misconceptions, and supported self-paced learning.
  • Teacher dependence: qualitative data showed the benefits relied on teacher guidance and careful lesson planning, not on the tools alone.

Implications and limitations

The study extends prior evidence that context-based instruction and AI tools each help, by demonstrating that their integration within a structured inquiry cycle produces greater gains than either alone. Limitations include the single-site setting, small sample, reliance on self-reported engagement, and potential novelty effects of AI tools. Recommendations: targeted professional development for teachers on AI literacy and inquiry Pedagogy; curriculum/administrative support for real-world contexts and adaptive technologies; and equitable access and clear ethical-use guidelines from policymakers.

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Citation

Abdikayumova, N., & Madybekova, G. (2026). Using context-based and AI-enhanced approaches to improve student engagement and achievement in secondary chemistry education. Chemistry Teacher International, 8(1), 37–51.