Where do I start, how do I create a clear line of reasoning, and what components are essential in a good written response in economics?
The path from a good idea in your head to a well-structured written response can be long. Especially with open-ended questions, which are common in economics, it can be challenging to organize your thoughts meaningfully and build arguments effectively. This is where the econArgueNiser can provide valuable support to students.
With the econArgueNiser, students do not write a complete answer to an open question, but instead compose individual texts for predefined argumentation components. As part of the DeepWrite research project, an argumentation structure specifically tailored to economics has been developed. It is based on the Toulmin model and consists of four key components: claim, preferences, constraints, and conclusion. Once students have written a short text for each of these components, they receive personalized feedback from an AI system that highlights strengths and areas for improvement in each element. In this way, the econArgueNiser helps students learn an argumentation structure that is essential to their discipline while also allowing them to target and improve their weaknesses.
The econArgueNiser can be used interactively during in-person lectures or asynchronously for self-paced learning. The online tool is integrated into the audience response system classEx. To use the econArgueNiser, students only need a computer or a mobile device such as a smartphone or tablet.
The most important tool for any lawyer is language.
In law studies, this tool is developed through the use of the legal reasoning style ("Gutachtenstil") and the associated argumentation skills. Mastering this somewhat unusual form of knowledge presentation can be challenging for students at first. However, the legal reasoning style is not an end in itself—it serves to structure legal thinking clearly and logically.
This is where the legalArgueNiser comes in: This AI-based application presents students with a short case scenario, which they are then asked to analyze using the legal reasoning style. The structure is already technically predefined—divided into issue, definition, application, and conclusion—with a separate text field provided for each element in which students enter their response.
After submission, students receive individualized AI-generated feedback on each of the four components. The feedback takes into account both the content and the argumentative quality of their answers, highlighting strengths and identifying areas for improvement.
The legalArgueNiser is also integrated into the audience response system classEx and can be used both in the lecture hall and remotely from home.
Would you like to try out the Jura ArgueNiser? Then you can do so here.
