Research & Publications
AI-Powered Cognitive Scaffolding Systems for Statistics Education
Research Overview
My dissertation investigates how AI-powered cognitive scaffolding systems can improve learning outcomes in introductory statistics, with particular attention to diverse student populations across different institutional contexts.
Core Question: Can AI provide adaptive, personalized support that helps students overcome common conceptual obstacles in statistics while maintaining trustworthiness, fairness, and educational effectiveness?
Why This Matters
- High failure rates in statistics: Especially at community colleges, where students often face additional barriers to success
- One-size-fits-all doesn't work: Students come with diverse backgrounds, preparation levels, and learning needs
- Scalability challenge: Instructors can't provide individualized support to every student at the moment of need
- AI's untapped potential: Modern AI can provide adaptive scaffolding, but we need rigorous research to understand effectiveness and equity implications
- Trustworthiness questions: How do we ensure AI tutoring is accurate, fair, explainable, and beneficial across all student populations?
Research Questions
Primary Research Questions
RQ1: Effectiveness
How does AI-powered cognitive scaffolding affect learning outcomes in introductory statistics compared to traditional online resources?
RQ2: Scaffolding Strategies
What types of cognitive scaffolding (minimal hints, moderate guidance, extensive support) are most effective for different statistical concepts and student populations?
RQ3: Institutional & Demographic Differences
How do diverse student populations across different institutional contexts (community college, research university, open enrollment) engage with and benefit from AI scaffolding?
RQ4: Trustworthy AI
What design features ensure AI tutoring systems are trustworthy, fair, accurate, and explainable across all student demographic groups?
The Platform as Research Instrument
I am building a free online statistics learning platform that serves dual purposes:
Educational Mission
Provide high-quality, accessible statistics education to anyone who wants to learn, regardless of financial means or institutional affiliation.
Research Instrument
Collect rigorous data on how AI-powered cognitive scaffolding affects learning, engagement, and equity in statistics education.
Ethical Research: All research participation is voluntary with informed consent. Students can use the platform whether or not they participate in research. Data is anonymized and protected according to UCLA IRB protocols.
What Makes This Novel
Cognitive Scaffolding, Not Just Q&A
Unlike typical AI chatbots that simply answer questions, this system provides adaptive scaffolding — progressive hints, Socratic questioning, and just-in-time support that fades as students gain mastery.
Multi-Institutional Comparison
By studying students across different contexts (community college, research university, open enrollment), I can understand how institutional factors and student demographics affect AI scaffolding effectiveness.
Trustworthy AI Framework
Working with Professor Guang Cheng's expertise in trustworthy AI, the platform includes monitoring for accuracy, fairness across demographic groups, explainability, and reliability.
Real-World Impact
This isn't just a lab study — real students learn real statistics while contributing to research that will improve education for future learners.
Current Research Projects
Active Research Program: Beyond the dissertation platform development, I am conducting multiple studies examining the impact of AI-assisted course design on student outcomes and instructional consistency across diverse institutional contexts.
AI-Assisted Course Design in College Algebra for STEM (MATH 4/4C)
Institution: Santa Monica College
Status: Data Collection in Progress (Spring 2026)
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).
Multi-Institutional Research Program
Institutions: Santa Monica College (SMC), Loyola Marymount University (LMU), Joyce University of Nursing and Health Sciences
Status: Various Stages of Data Collection
Additional studies examining AI-assisted instructor productivity, course material development, assessment design, and multi-institutional scalability across three distinct institutional contexts.
- Community College (SMC): Open-access institution serving diverse student populations
- Research University (LMU): Private university with selective admissions
- Professional Graduate Program (Joyce): Nursing and health sciences graduate programs with working adult learners
Publications
Under Review
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)
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. The study finds that income inequality (Gini coefficient) is the strongest and most consistent state-level predictor of mass shooting frequency.
In Preparation
AI-Assisted Course Design and Outcome Consistency in Community College Statistics: A Longitudinal Analysis Across California's Placement Reform Era
Author: Safaa Dabagh
Status: In Preparation
A longitudinal analysis of 25 sections of introductory statistics (756 students) at a large California community college over eight academic years (2018-2026). The central finding: the AI-assisted phase produced a semester-to-semester pass rate range of just 8.3 percentage points — the lowest of any policy era.
Planned Publications (From Dissertation)
- Effectiveness Study: "AI-Powered Cognitive Scaffolding in Introductory Statistics: A Randomized Controlled Trial"
- Equity Analysis: "Does AI Scaffolding Reduce or Amplify Educational Inequalities? Evidence from Community College Statistics Students"
- Trustworthy AI: "Designing Trustworthy AI Tutoring Systems: Fairness, Explainability, and Reliability in Educational Contexts"
- Platform Design: "Open-Source Platform for Research on AI-Enhanced Statistics Education"
Theoretical Framework
This research draws on multiple theoretical traditions:
- Cognitive Load Theory: How scaffolding reduces extraneous cognitive load
- Zone of Proximal Development: AI provides support at the edge of student capability
- Metacognition: Scaffolding promotes self-regulated learning
- Equity & Access: How technology can reduce or amplify educational inequalities
- Trustworthy AI: Ensuring fairness, transparency, and reliability
Methodology
Mixed Methods Design
Quantitative Component
- Participants: 300-500 students across multiple institutional contexts
- Design: Randomized controlled trial with multiple treatment conditions
- Outcome Measures: Learning gains, concept mastery, engagement metrics, self-efficacy
- Analysis: Multilevel modeling, causal inference techniques, equity analysis by demographic subgroups
Qualitative Component
- Student interviews (n~30) across different demographic groups
- AI conversation analysis using natural language processing
- User experience surveys and open-ended feedback
- Case studies of students with different learning trajectories
Timeline
| Phase | Timeline | Key Milestones |
|---|---|---|
| Platform Development | Fall 2025 | Build learning platform, AI scaffolding system, data collection infrastructure |
| IRB Approval | Fall 2025 | Submit and receive UCLA IRB approval for human subjects research |
| Pilot Study | Winter 2026 | Test with 30-50 students, refine protocol, validate instruments |
| Main Study | Spring-Fall 2026 | Full data collection across multiple cohorts and institutions |
| Analysis & Writing | Winter 2026-2027 | Data analysis, dissertation writing, manuscript preparation |
| Defense | Spring 2027 | Dissertation defense and graduation |
Researcher & Contact
Principal Investigator
Safaa Dabagh
UCLA Statistics MA | Seeking PhD Readmission
Mathematics Instructor, Santa Monica College
Email: dabagh_safaa@smc.edu
Academic Status: I hold an MA in Statistics from UCLA and am currently applying for readmission to the UCLA Statistics PhD program to complete my dissertation research. My active research program continues during this transition period.
Get Involved
For Researchers & Educators
Interested in collaboration, accessing pilot data, or learning more about the platform architecture? Get in touch — I welcome partnerships and knowledge sharing.