Evaluating the Impact of AI-Supported Inquiry-Based Learning on Students' Creative Mathematical Performance, Critical Problem-Solving Skills, and Attitudes Toward Mathematics

Synthesis: This quasi-experimental study of secondary students (mean age 12.79) compared AI-supported inquiry-based learning against conventional instruction in mathematics. AI-supported IBL significantly improved students' creative mathematical performance and attitudes toward mathematics — but did not produce a statistically significant improvement in critical Problem Solving skills. Multivariate analysis confirmed a significant overall group effect; the authors conclude AI-supported IBL mainly supports Creativity and affective development, with limited effect on problem-solving.

Key Findings

1. Creative performance and attitudes improved. AI-supported IBL significantly improved creative mathematical performance and attitudes toward mathematics compared with traditional instruction.
2. Critical problem-solving did not improve significantly. The intervention did not produce a statistically significant improvement in critical problem-solving skills — a cautionary result for claims that AI + IBL automatically strengthens higher-order reasoning.
3. Positive inter-correlations. Correlation analysis showed positive relationships among creative performance, problem-solving, and attitudes in both groups; multivariate analysis confirmed a significant overall group effect.
4. Efficiency mechanism. The authors suggest AI-supported IBL may improve instructional efficiency through guided exploration, adaptive feedback, and reduced cognitive load.

What this means for practice

• Instructors. Add deliberate problem-solving scaffolds rather than counting on AI-supported inquiry to raise higher-order reasoning: the intervention improved creative mathematical performance and attitudes, but produced no statistically significant gain in critical Problem Solving skills.
• Instructors. Use AI-supported inquiry for the outcomes this study found reliable — creative mathematical performance and attitudes toward mathematics — and treat critical problem-solving as a separate objective with its own instruction and assessment.
• Designers. Build guided exploration and adaptive feedback into the inquiry sequence, the mechanism the authors credit for improved instructional efficiency and reduced cognitive load — the gains in creativity and affect alone should not be read as evidence of deeper reasoning.
• Researchers. Test whether the creativity and attitude gains transfer to problem-solving in other grades and settings, since the variables were positively inter-correlated but the group effect did not reach critical problem-solving.

Limitations

• The design is quasi-experimental: the 120 Grade 8 students (mean age = 12.79 years, SD = 0.68) were assigned as intact classes, not randomly, and no baseline covariate adjustment was made, so residual selection bias may persist and causality should be read with caution.
• The intervention ran for one academic term, so longer-term effects and sustainability remain unknown.
• Participants came from a single grade level, limiting generalization to other grades, school settings, and demographic backgrounds.
• Attitudes and self-perceptions were captured through a questionnaire using Likert-type items, so those measures are self-report and the critical problem-solving null result rests on the study's own tasks.

Citation

Mujib, M., Suherman, S., & Mardiyah, M. (2026). Evaluating the impact of AI-supported inquiry-based learning on students' creative mathematical performance, critical problem-solving skills, and attitudes toward mathematics. Journal on Efficiency and Responsibility in Education and Science, 19(2), 142–153.