Two 15-row samples of the survey are rendered above. The first leaves out Monthly_Income, which is person-identifying. The second includes it, so I only describe it in aggregate here. The table has 300 rows, one per surveyed student or professional, with 23 columns. **What the sample shows** - **Mix of users:** students (high school, undergraduate, Bachelor's, Master's, PhD) and professionals such as software developers, technical writers, customer support specialists and analysts. - **Countries:** the Philippines, Canada, the US, India, Pakistan, the UK, Germany, Australia, Brazil and France. - **Tools and purposes:** ChatGPT, Claude, Copilot, Gemini, Perplexity and Meta AI, used for coding, studying, writing, research, content creation, customer support, data analysis and office work. - **Scores:** productivity scores run from about 2 to 10, accuracy ratings from 2 to 4, and satisfaction from 3 to 10. **Data quality** - **Duplicate IDs:** 300 rows carry only 288 distinct User_IDs, so some users appear more than once. - **Missing values:** Productivity_Score is filled in 284 of 300 rows, Accuracy_Rating in 285 and Satisfaction_Score in 293. Several sampled rows also have blank Country, AI_Tool, AI_Purpose, Education_Level, Profession or Monthly_AI_Cost values. - **Mixed units in numeric columns:** the sample has "16 yrs" in Age, "$20" in Monthly_AI_Cost, and "1.6 hrs" or "6.5 hrs" in the hours columns. These columns are stored as text and need cleaning before they can be averaged. - **Inconsistent labels:** - Gender has 12 distinct values, including "F", "FEMALE" and "male". - AI_Tool has 17 distinct values, with "Chat GPT", "chatgpt" and "copilot" as variants. - Education_Level has 17 distinct values, such as "undergrad" next to "Undergraduate". - Country has lowercase entries like "france", "india" and "canada". - Would_Recommend has 4 distinct values, which includes "Maybe" alongside Yes and No. - **Mixed languages:** some professions and tasks are in German, French or Portuguese. - **Implausible entries:** some users look mislabelled, for example a 16-year-old with a Master's and a 17-year-old with a PhD. The sample is mostly useful for seeing the shape of the data. Any aggregate analysis would first need the text-typed numeric columns parsed and the categorical labels standardised.