Assessment Objectives 

The following assignment was part of the course, Big Data Tools for Economists (ECO225, Fall 2023). As with the literature review, the objective for this assignment was to familiarize students with conducting literature reviews using AI tools, enabling them to analyze and synthesize economic research effectively and to formulate research questions.  

Students learned to formulate narrow research questions that address gaps in existing literature and improve their skills in interpreting and presenting data visually.

Assessment Process 

1. Initial Question Generation: Students begin by uploading a sample of their dataset or providing a detailed explanation of the data to ChatGPT. They then prompt ChatGPT to generate ten potential research questions based on the dataset. If they have ideas of their own, they can share it at this stage.
2. Selection and Refinement: From the list of ten questions generated by ChatGPT, students select five questions they find most interesting and relevant. They are required to critically assess these questions by identifying potential challenges or limitations associated with each one. This step encourages students to think about the feasibility and scope of their research. 
3. Final Research Question: After refining the list, students choose one research question to pursue. This exercise saves time and encourages deeper engagement, as students can quickly see a wide range of possibilities before narrowing their focus. Students refine this finalized question through the semester as they work on their paper and adjust based on their data work.

Future-Focused Skill Development 

As with the accompanying literature review assignment described above, this assessment aligns well with the University of Calgary’s STRIVE model. The exercise especially promotes equity by offering a space for personalized learning. In allowing students to generate and refine research questions based on their own datasets or interests, the assessment creates space for diverse approaches to engagement, reflection, and knowledge creation. This approach recognizes that students have unique ways of interacting with information, developing insights, and demonstrating understanding. By tailoring the research process to individual interests and datasets, students can explore topics through methods that resonate with their personal learning preferences, fostering deeper engagement and more meaningful outcomes. In addition, the exercise allows students to revisit and refine their work throughout the semester, accommodating different learning paces and styles, and ultimately supporting an inclusive learning environment.

Student Feedback 

Professor Khazra shares: “This is a direct quote from a student email in August of 2024: “While the programming skills I gained from ECO225 were undoubtedly invaluable [in my internship], I think the most important thing I took away from the course was how to use ChatGPT effectively and efficiently. I think that could a great selling point for the course considering how in demand the usage of gen AI has become (and how bad most are at using it!)””