Cognitive Structure Analysis-based Self-assessment and Remediation Greatly
Improves Educational Performance and Reduces Score Variance in 8th Grade
Science Students

Janat Bajwa; John Leddo

doi:10.46609/IJSSER.2026.v11i10.034

Title: Cognitive Structure Analysis-based Self-assessment and Remediation Greatly Improves Educational Performance and Reduces Score Variance in 8th Grade Science Students
Authors: Janat Bajwa and John Leddo
Journal: International Journal of Social Science and Economic Research (IJSSER) ISSN: 2455-8834 Volume: 11 Issue: 10 Publication Date: October 2026 DOI: 10.46609/IJSSER.2026.v11i10.034
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Janat Bajwa1 and John Leddo1,2,3 1. MyEdMaster, LLC, Virginia, USA 2. METY Technology, Inc., Virginia, USA 3. METY Foundation, Virginia, USA
MLA 8 Bajwa, Janat, and John Leddo. "Cognitive Structure Analysis-based Self-assessment and Remediation Greatly Improves Educational Performance and Reduces Score Variance in 8th Grade Science Students." Int. j. of Social Science and Economic Research, vol. 11, no. 10, Oct. 2026, pp. 5955-5972, doi.org/10.46609/IJSSER.2026.v11i10.034. Accessed Oct. 2026. APA 6 Bajwa, J., & Leddo, J. (2026, October). Cognitive Structure Analysis-based Self-assessment and Remediation Greatly Improves Educational Performance and Reduces Score Variance in 8th Grade Science Students. Int. j. of Social Science and Economic Research, 11(10), 5955-5972. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i10.034 Chicago Bajwa, Janat, and John Leddo. "Cognitive Structure Analysis-based Self-assessment and Remediation Greatly Improves Educational Performance and Reduces Score Variance in 8th Grade Science Students." Int. j. of Social Science and Economic Research 11, no. 10 (October 2026), 5955-5972. Accessed October, 2026. https://doi.org/10.46609/IJSSER.2026.v11i10.034.
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ABSTRACT: Low student performance continues to plague educational systems around the world. The US, for example, has invested billions of dollars in educational reform over the past 50 years in the hope of dramatically raising student achievement but with little to show for this investment. Our previous research shows that Cognitive Structure Analysis-based assessment greatly improves student performance and can bridge the racial achievement gap between Black/Hispanic and White/Asian students. This study investigated whether artificial intelligence (AI)-generated Cognitive Structure Analysis (CSA) self-assessment templates could improve academic achievement and reduce variability in student performance in eighth-grade science. Eighteen students enrolled in a physical science class at a Falcon Academy, a private school in Virginia, were randomly assigned to either a self-assessment condition (n = 9) or a no-self-assessment control condition (n = 9). All students received identical classroom instruction on waves and sound. Students in the experimental condition completed an AI-generated self-assessment template as homework, identifying gaps in their factual, procedural, strategic, and rationale-based knowledge and reviewing instructional materials to remediate those gaps. All students subsequently completed a teacher-developed test. Students using self-assessment achieved a mean score of 96.6%, compared with 82.0% for students in the control condition, a statistically significant difference, Welch's t(12.19) = 5.62, p < .001, Hedges' g = 2.53. Additionally, the self-assessment group exhibited substantially lower score variance than the control group (13.25 vs. 47.06), with a onetailed F-test indicating a statistically significant difference, F(8, 8) = 3.55, p < .05. Eight of nine students using self-assessment scored at least 90%, compared with only one student in the control group. These findings suggest that AI-generated CSA self-assessment and remediation templates can substantially improve academic performance while potentially reducing achievement disparities between higher- and lower-performing students. The intervention required minimal teacher involvement and no changes to classroom instruction or testing procedures, suggesting its potential for scalable implementation in educational settings.

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