This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored prompts to deliver constructive feedback that encourages rather than discourages students. This methodology focuses on accurately feeding student data into large language models and crafting prompts that enhance the constructive nature of feedback. The effectiveness of this approach was validated through surveys conducted with over 20 mathematics teachers, who confirmed the reliability of the generated reports. This method can be seamlessly integrated into intelligent adaptive learning systems or provided as a tool to significantly reduce the workload of teachers, providing accurate and timely feedback to students. By transforming raw educational data into interpretable tags, this method supports the provision of efficient and timely personalized learning feedback that offers constructive suggestions tailored to individual learner needs.

Everyone has gone a bit ChatGPT bonkers at the moment. Talk is that this brilliant bot can produce blogs, articles, exam essays and editorials so well-written that they are impossible to differentiate from a human-produced text.

You Tube needs to employ AI to help process the 300 hours of video uploaded to the platform every minute by its users. This processing includes removing video deemed inappropriate by YouTube's standards. Some 8.3 million videos were removed from YouTube in the first quarter, 76 percent of those identified and flagged by AI automatically, according to an account in Forbes. Of those, more than 70 percent were never viewed by users. While the AI system is able to review more content than humans, full-time human specialists work with the AI, which of course is not foolproof.