There are two main relevant terms from data science and AI methods that apply in your case:
Natural Language Processing (NLP). This is a general description of tasks that involve processing inputs that use human language, such as written text. In order to rate the text of your news articles, you will need to use some NLP techniques so that the language in them is converted to forms that an AI can process.
Supervised machine learning. This is a technique that can be used to learn an approximation of an unknown function by processing examples of known inputs and outputs. In your case, you have a function that can take a (maybe pre-processed using NLP tools) news article and output the rating that your group would give it.
There are a wide range of approaches possible using these two things. You will want to search these terms and read around the subjects. Expect to spend weeks to months learning the basics before attempting your project in full.
As a starting point to get a taste for what is involved, I recommend you look into sentiment analysis. Sentiment analysis is used to label text as having certain emotions, or simply as being "positive" ot "negative", and is usually applied to shorter pieces of text than a news article. This makes it a simpler problem that the one you are attempt, and maybe a useful introduction. Here is a step-by-step tutorial for sentiment analysis of Twitter feeds.
Another similar example might be to classify emails as spam vs not spam. Emails are often longer than tweets, so slightly different approaches may be used to help summarise the content before attempting to classify. Here is a step-by-step tutorial for spam classifiers.
Unfortunately, a few hundred examples of human-rated articles is probably not very much data, in case you are expecting anything sophisticated, such as exploring the quality of journalism. It may be enough to rate interest in different subjects. But the only way to find out for sure is to start your project. Do take the time to research a few simple examples and tutorials first, before you dive straight into your own work. This will help you structure and plan your work plus avoid common pitfalls.