AI in Assessment Example: Level 4

Data Analysis Project (Statistics)

At this level “Full AI” of the five-level AI Assessment Scale (Perkins, et al., 2024) students can use AI to complete any elements of the tasks, with students directing AI to achieve the assessment goals. Assessments at this level may also require engagement with AI to achieve goals and solve problems.

Assignment Title: Comprehensive Data Analysis of [Dataset]

Course: STAT 287: Applied Statistics

Due Date: [Insert Due Date]

Assignment Overview:
In this assignment, you will perform a comprehensive data analysis on a given dataset. You are encouraged to use AI tools throughout the process, from data cleaning and analysis to visualization and interpretation of results. The goal is to leverage AI to enhance your analytical capabilities while maintaining rigorous statistical standards.

Objectives:

To apply statistical techniques to analyze real-world data.
To use AI tools to assist in various stages of data analysis.
To critically interpret AI-generated results and provide meaningful insights.

Instructions:

Data Cleaning and Preparation:
- Use AI to clean the dataset, handling missing data, outliers, and inconsistencies. AI can also suggest initial insights or trends within the data.
- Verify the AI’s data cleaning process, ensuring accuracy and relevance to your analysis.
Exploratory Data Analysis (EDA):
- Employ AI tools to perform exploratory data analysis, generating visualizations, summary statistics, and identifying patterns or anomalies.
- Critically evaluate AI-generated EDA results, selecting the most relevant visualizations and insights to include in your report.
Statistical Analysis:
- Use AI to conduct more advanced statistical analyses, such as regression models, hypothesis testing, or machine learning algorithms. AI can assist in setting up models and interpreting initial outputs.
- Assess AI-generated statistical results, refining models as needed, and ensuring that interpretations align with the research question.
Report Writing:
- Write a comprehensive report (10-12 pages) detailing your data analysis process, findings, and conclusions. AI tools can assist in drafting sections, suggesting interpretations, and ensuring clarity.
- Include AI-generated visualizations and statistical outputs, with appropriate commentary on their relevance to your analysis.
Reflective Commentary:
- Provide a reflective commentary (2-3 pages) on how AI was used throughout the data analysis process. Discuss how AI influenced your approach, the challenges you encountered, and how you critically evaluated AI-generated outputs.
- Attach AI-generated scripts, models, and visualizations as an appendix to your report.

Evaluation Criteria:

– Data Cleaning and Preparation (20%): Effectively using AI to clean and prepare data while ensuring accuracy.

– Exploratory Data Analysis (20%): Conducting a thorough EDA with AI assistance, selecting relevant visualizations and insights.

– Statistical Analysis and Interpretation (30%): Applying appropriate statistical methods with AI support, and critically interpreting results.

– Report Quality (20%): Presenting a clear, well-organized, and insightful analysis in the final report.

– Reflective Commentary (10%): Thoughtful reflection on AI’s role in the analysis process and critical engagement with AI-generated outputs.

Academic Integrity:
All AI-generated content must be critically evaluated, properly cited, and meaningfully integrated into your analysis. Misuse of AI or failure to critically engage with AI-generated results will be treated as a breach of academic integrity.

Support and Resources:
If you have any questions about the assignment or need support in using AI tools, please reach out during office hours or schedule an appointment. Additional resources on critical use of AI in historical research are available on the course website.

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