JAMES M CLIFTON

PhD-qualified mathematics expert with over a decade of applied experience in mathematics instruction, quantitative modeling, AI data training, and educational content development. Demonstrates extensive expertise in evaluating mathematical content, designing complex problem sets, and optimizing AI model responses using high-level analytical frameworks. A seasoned academic and practitioner with deep familiarity in linear algebra, statistical inference, data analytics, and generative AI alignment. Proven track record of delivering high-precision feedback on math-related outputs, and constructing robust evaluation rubrics for AI training applications. Recognized for blending mathematical theory with AI innovation to support next-generation machine learning models with factual accuracy and pedagogical rigor.

• Certified Mathematical Reviewer, American Mathematical Association (2023)
• Mathematics for Machine Learning, Imperial College London (Coursera, 2023)
• Advanced Statistics with Python, University of Michigan (Coursera, 2022)
• Data Science and AI Applications, University of Arkansas Professional Training (2021)
• Certified Secondary Mathematics Teacher, Arkansas Department of Education (Valid through 2026)

• Phi Beta Kappa Honor Society – Inducted for outstanding academic achievement in mathematics
• Arkansas Department of Education – Awarded “Outstanding Educator in Mathematics” (2015)
• University of Arkansas Graduate Research Award – Recognized for AI-focused dissertation research (2018)
• Chair, Mathematics Content Panel – National Association of Math Educators (2021–Present)
• President, Math Club – Conway High School; led team to win state-level competitions
• Undergraduate Research Fellowship – Funded project on geometric algorithms (2010)
• Kaplan Internal Excellence Award – For contributions to adaptive test systems for GRE math (2018)
• Member, American Mathematical Society (AMS) – Contributing reviewer for educational initiatives
• Presenter, National Council of Teachers of Mathematics (NCTM) Annual Conference – Presented on mathematical modeling in AI curriculum (2022)
• Contributor, Open Math Projects – Developed open-source problem sets used in AI evaluation frameworks

• Improved AI-generated mathematical content accuracy by 38% through rigorous rubric-based evaluations across over 1,200 examples.
• Spearheaded the creation of 2,000+ K–12 and AP math items used in digital learning environments and standardized test prep systems.
• Designed an AI model scoring framework that increased content review efficiency by 45% for Pearson’s mathematics evaluation team.
• Received the Kaplan Excellence Award for redesigning GRE math practice modules, enhancing learner performance by 22%.
• Led a cross-functional task force at Kaplan to align adaptive test content with learning outcomes, reducing user churn by 15%.
• Delivered workshops on applied statistics and computational logic attended by over 300 undergraduate math students.
• Awarded a $10,000 research grant to study probabilistic modeling in AI learning systems, contributing to PhD dissertation success.
• Certified by leading institutions (Imperial College London, University of Michigan) in math-for-AI and advanced statistics.
• Directed Math Olympiad teams to two consecutive Arkansas state championships, demonstrating leadership in competitive math.
• Authored peer-reviewed academic content adopted by national high school and college math platforms for AI training datasets.