Random Talk Theory

It's the dream of every teacher to grade students' homework automatically using AI. Imagine just clicking a button and, voila, you get the results, similar to how computerized multiple-choice exams are scored. Today, tools like ChatGPT, Claude, Gemini, and some open-source LLMs allow us to upload files and pose questions. However, it's impractical to upload each student's assignment individually and paste rubrics for every score. Instead, a simple Python script utilizing APIs can automatically process all assignments in a folder and generate a table of scores in a single file. I've uploaded a test script on GitHub at [ https://github.com/franktfye/GradeBuddy ], where you can download it and run it in Python. The examples in the data folder were generated by GPT-4. I asked GPT to generate five sample student essays about multiculturalism. The examples 4 used Chicago style instead of APA7, while the example 5 contained a lot of grammatical errors. In my experiments wi...

TL;DR Psychologists, particularly those working in industrial domains, should embrace the power of big data and transition from traditional tools to more advanced technologies like SQL and AI. With tools like SQL and AI, analyzing user feedback from online platforms has become more feasible than ever before.    In psychological research, it's common to gather customer feedback on products through surveys or interviews. You can gather hundreds or even thousands of responses and utilize traditional psychometrics to further analyze these responses. These small-sample techniques have been well-established in the field for years, and psychologists are still using such practices to conduct research. However, what happens when you want to analyze hundreds of thousands of user comments? Traditional methods may not be feasible for such a large volume of data. In real-world scenarios, product designers often seek to gain insights from user feedback to improve their products. By analyzin...