Publications & Academic Work

PhD in Statistics Seeking Readmission

University of California, Los Angeles (UCLA)

Planned Dissertation: AI-Powered Cognitive Scaffolding Systems for Statistics Education

Status: Applying for readmission to UCLA Statistics PhD program to complete dissertation research. Active research program ongoing during transition period.

Expected Completion: 2027 (upon readmission)

MA in Statistics

University of California, Los Angeles (UCLA)

BA in Mathematics/Economics, Minor in Statistics

University of California, Los Angeles (UCLA)

AI-Powered Cognitive Scaffolding Systems for Statistics Education

PhD Dissertation (In Progress)

Abstract:

This dissertation investigates how AI-powered cognitive scaffolding systems affect learning outcomes in introductory statistics, with particular attention to diverse student populations across different institutional contexts. Through a mixed-methods randomized controlled trial involving 300-500 students, I examine the effectiveness of adaptive AI tutoring that provides progressive hints and just-in-time support. The research addresses critical questions about trustworthiness, fairness, and equity in AI-enhanced education while building an open-source platform that provides free statistics education.

Keywords: AI in education, cognitive scaffolding, statistics education, educational equity, trustworthy AI, learning analytics

Income Inequality, Not Gun Policy or Mental Illness, is the Strongest State-Level Predictor of Mass Shootings in the United States: A Multi-Method Analysis (2018–2024)

Author: Safaa Dabagh

Journal: BMC Public Health (Springer Nature)

Submitted: February 2026

Status: Under Review

A multi-method analysis of 3,852 mass shooting incidents across all 50 U.S. states and the District of Columbia (2018–2024) using five complementary analytical approaches (bivariate correlations, OLS regression, LASSO regularization, Random Forest, and Gradient Boosting). The study finds that income inequality (Gini coefficient) is the strongest and most consistent state-level predictor of mass shooting frequency, ranking #1 across all five methods. Gun ownership rate and gun law strength showed no significant association. Mental illness prevalence was paradoxically negatively correlated with mass shooting rates, likely reflecting confounding with state-level diagnostic infrastructure rather than a true protective effect.

AI-Assisted Course Design and Outcome Consistency in Community College Statistics: A Longitudinal Analysis Across California's Placement Reform Era

Author: Safaa Dabagh

Target Journal: To be determined (previously submitted to Journal of Statistics and Data Science Education; currently being revised for resubmission to a new journal)

Status: In Preparation

A longitudinal analysis of 25 sections of introductory statistics (MATH 54/STAT C1000; 756 students) at a large California community college over eight academic years (2018–2026), spanning two major placement policy reforms (AB 705 and AB 1705), the COVID-19 disruption, and the integration of AI-assisted course design. The central finding is about outcome consistency: the AI-assisted phase produced a semester-to-semester pass rate range of just 8.3 percentage points — the lowest of any policy era — compared to 52.4 pp in the preceding phase, despite encompassing both a 16-week and a 6-week section.

AI-Assisted Course Design in College Algebra for STEM (MATH 4/4C)

Principal Investigator: Safaa Dabagh

Institution: Santa Monica College

Status: Data Collection in Progress (Spring 2026)

Dissertation-Related: Yes

A comparative study examining the impact of AI-assisted instructor course design on student outcomes in College Algebra for STEM (MATH 4) with corequisite support (MATH 4C). The study compares Spring 2025 baseline data (no AI-assisted design; 38 students, ~50% pass rate) against Spring 2026 (AI-designed course materials; 32 students, data collection in progress). This study extends the AI-as-consistency-mechanism framework from statistics to algebra, testing whether AI-assisted course design can improve outcomes and reduce variability across different mathematical disciplines within the community college context.

• Effectiveness Study: "AI-Powered Cognitive Scaffolding in Introductory Statistics: A Randomized Controlled Trial"
  Target: Journal of Educational Psychology or Journal of Statistics Education
• Equity Analysis: "Does AI Scaffolding Reduce or Amplify Educational Inequalities? Evidence from Community College Statistics Students"
  Target: Community College Review or Educational Researcher
• Trustworthy AI: "Designing Trustworthy AI Tutoring Systems: Fairness, Explainability, and Reliability in Educational Contexts"
  Target: International Journal of Artificial Intelligence in Education
• Platform Design: "Open-Source Platform for Research on AI-Enhanced Statistics Education"
  Target: Technology Innovations in Statistics Education (TISE)

Instructor of Mathematics with Main Focus on Statistics

Santa Monica College (SMC) • 2016-Present

• Math 54: Introductory Statistics
• Math 4/4C: College Algebra and other mathematics classes

West Los Angeles College • Ongoing

• Mathematics and statistics courses

Loyola Marymount University • Current

• Applied Statistics in Biology: Statistics course designed for life sciences students

Joyce University of Nursing and Health Sciences • Current

• Faculty-Adjunct: Statistics and data science courses for nursing and health sciences graduate programs

UCLA • Summer Sessions

• Summer courses in statistics (Statistics Department)

Teaching Approach:

• Hybrid and online course delivery
• Canvas LMS integration and course design
• Focus on educational equity and student success for diverse learners
• Experience across multiple institutional contexts (community colleges, research university)

AI in Education Certificates & Training In Progress

View my AI in Education journey for detailed progress tracking.

• Completed: Google AI for Educators
• In Progress: Coursera AI in Education
• Planned: Lehigh University Certificate in AI & Learning Analytics (Application 2026)

Programming & Statistical Analysis

• R: Advanced statistical analysis, data visualization (ggplot2), modeling
• Python: Data analysis (pandas, numpy), machine learning (scikit-learn), AI integration
• Statistical Methods: Regression, multilevel modeling, causal inference, survey analysis
• Learning Analytics: Educational data mining, student modeling, A/B testing

Educational Technology

• LMS: Canvas, course design, online pedagogy
• Web Development: HTML, CSS, JavaScript, Firebase
• AI Integration: Claude API, GPT API, prompt engineering
• Data Platforms: Google Colab, Jupyter notebooks, Observable

• Free Statistics Learning Platform: Building open-source educational resources accessible to all students regardless of financial means
• Community College Advocacy: Conducting research that centers community college student experiences and needs
• Educational Equity: Focus on understanding and addressing achievement gaps in STEM education
• Mommy Coding Camp: Teaching coding and computational thinking to children (Learn more)