Compare Traditional Computational Results to AI-Enhanced Analysis

STATS 7650: Computational Statistics

Elvan Ceyhan, Department of Mathematics and Statistics, College of Sciences and Mathematics

A split image of a computer on a desktop with handwritten calculations on one side and futuristic symbols on the other

Assignment Description

This assignment integrates traditional numerical methods with AI-based computational tools to enhance students' understanding of statistical problem-solving. Students use AI to analyze convergence rates, solve equations, perform symbolic computations, and create plots, while also working through the same problems manually. They will critically compare AI-assisted and traditional methods, reflecting on the differences between them, discuss insights gained, and consider the strengths and limitations of AI in computational statistics. This assignment encourages critical thinking and responsible use of AI in numerical and statistical methodologies.

AI Capabilities Used

Problem Solving, Data Visualization, Statistical Computation, Numerical Analysis

Number of Students

5-10

Level of Students

Graduate

Teaching Modality

In-person

STAT 7650-Homework 2

Instructor Notes

This activity is part of semester-long, multi-activity project to help students to navigate AI.

Project Title: Integrating AI into Data Science Projects for Personalized and Practical Learning

Learning Goals:

1. Develop technical expertise in using AI for data cleaning, visualization, coding, and analysis.
2. Foster critical thinking by comparing AI-assisted outputs with traditional (both manual and computational) methodologies.
3. Promote understanding of ethical considerations in AI applications.
4. Enhance teamwork and collaborative problem-solving skills.

Activity Description:

This semester-long project comprises three phases:

Phase 1: AI Introduction and Exploration
- Students are introduced to tools like ChatGPT and R packages for data manipulation and machine learning.
- Guided examples and demonstrations help explore AI’s capabilities and limitations.

Phase 2: Real-World Applications
- Students analyze datasets using AI tools for parameter estimation and hypothesis testing.
- Ethical concerns, such as bias in algorithms, are discussed to foster responsible AI use.

Phase 3: Assignments and Projects
- Students apply both AI and traditional methods to analyze complex datasets.
- Final deliverables include technical reports, and sometimes where applicable an ethical reflection.

Specific Activities for the Course

Key AI Use Cases in Statistics
- AI simplifies complex tasks like bootstrap methods and optimization algorithms.
- Students explore parameter estimation and optimization through AI-driven simulations and regression models.

Ethics Module
- A focus and reflection on responsible AI use.

Student-Centric Learning
- Interactive projects, such as applying Newton’s method with AI support, enhance engagement and practical understanding.

Mode of Assessment & Deliverables:

- Students submit comprehensive data analyses, including comparisons between AI-assisted outputs, their own manual calculations, and results generated from self-written code.
- Students are required to critically evaluate discrepancies, alignments, and insights gained from these comparisons, fostering a deeper understanding of the analytical process.
- Visualizations and illustrations designed by students depict the solutions, key findings, and exercise results, emphasizing creativity and clarity in presenting complex data or procedures.
- Students reflect on the integration of AI with traditional methods, focusing on its impact on learning and problem-solving.

Notes: The document below contains several components showcasing AI integration in the course. It includes the course syllabus, which outlines the AI policy and its application, along with excerpts from AI-enhanced lecture slides. Additionally, it features a sample R code file that has been annotated and commented with AI assistance.

To further explore AI’s role in education, I administered an initial survey on students’ perceptions and familiarity with AI. This survey, designed as an extra homework assignment with bonus points, has been highlighted separately in the AI in Teaching and Learning Showcase under the title “Student Perspectives on AI in Education Survey.”

Lastly, a sample AI-enhanced homework assignment for this course is provided above—see “STAT 7650-Homework 2.”

STAT 7650 AI_Related_Course_Materials

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