Power BI Practice: Using Data Visualization to Reveal How AI Reshapes Contemporary Students' Learning and Life

In today's world where AI technology is sweeping the globe, student groups— as digital natives— are at the forefront of AI tool popularization. Indian data analyst Prachi Jain, through a Power BI dashboard project, uses 100 student survey datasets to explore the impacts of tools like ChatGPT and Gemini on students' learning efficiency, academic performance, and daily habits, filling the gap in systematic research on AI's actual impacts on students.

Since 2023, generative AI tools have rapidly penetrated the education sector, yet systematic research on their actual impacts on students is scarce. This project obtains survey datasets of 100 students from Kaggle, covering dimensions such as demographics (age, gender, city, education stage), behavior (AI tools, usage duration, purpose), and outcomes (perceived impact on grades, satisfaction). It aims to answer core questions like the popularity of AI tools, usage differences across groups, and the correlation between usage duration and academic performance.

Data Architecture: Raw CSV data is cleaned via Power Query to form a structured dataset, containing fields across three dimensions: demographics, behavior, and outcomes, covering samples from 5 major cities in India and various education stages.

Dashboard Design: A three-layer page architecture realizes macro-to-micro narration:

1. Executive Overview: KPI cards display key metrics such as total sample size, average age, and usage duration; ring charts present tool distribution; bar charts show city rankings;
2. Usage Pattern Analysis: Interactive slicers support filtering by gender, education stage, and city to explore group differences;
3. Academic Impact & Satisfaction Insights: Cross-analysis of usage patterns and academic outcomes.

DAX Calculations: Core measures include total number of students, average age, average daily usage duration, etc., supporting dynamic filter updates.

1. Tool Echelon Pattern: ChatGPT ranks first, followed by Gemini and Grammarly, forming the first echelon— reflecting students' reliance on general AI assistants and professional writing tools;
2. 3 Hours Daily Usage: Reflects deep AI penetration, triggering thoughts on time management and dependency;
3. Cross-Education Stage Popularization: From high school to postgraduate levels, all actively use AI— AI literacy becomes a basic ability across stages;
4. Geographic Concentration: Samples from 5 major cities suggest AI usage is related to urbanization and infrastructure.

Tech Stack: Uses the Power BI ecosystem, including Power BI Desktop (dashboard development), Power Query (ETL), and DAX (data analysis logic).

Development Process: Follows business intelligence methodology— from requirement understanding, data acquisition and cleaning, to data modeling, visualization development, then documentation and result sharing. The repository directory structure reflects good engineering practices.

Limitations: The 100-sample size is insufficient in statistical significance and generalizability; the dataset is based on self-reports, leading to subjective bias.

Future Optimization: Add analysis pages (trends, user portraits), publish to Power BI Service for collaborative sharing, introduce advanced DAX (time intelligence, prediction), and expand data sources (more samples, long-term tracking).

Project Value: Provides a data-driven research framework for education researchers, a visualization example for data analysts, and triggers students' reflection on AI usage habits.

Deep Insights: After AI popularization, it's necessary to understand its changes to behavior patterns, time allocation, and cognitive styles. Data visualization is an important tool to understand the intersection of technology and human nature.