This blog describes how I incorporated Artificial Intelligence (AI) tools into student empirical projects emphasizing academic integrity. In my Econometrics class that I teach to sixth-semester economics students at the University of Monterrey, I designed an activity to solve practical problems and communicate results using AI.

AI Predictive Modeling Projects Emphasizing Academic Integrity
In economics, an empirical project entails producing research with observable and verifiable information. Three examples of empirical projects are 1) the evolution of pollution in Monterrey and Mexico City, Mexico, from 1990-2022; 2) co-movements in the price of steel and copper over time; and 3) predicting the share price of a particular company. This information can come from a database, a survey, or an experiment. Since the course involves time series, the data are observations of one or more variables over time; this information is helpful to study facts over a period, make predictions, and develop public policies in the short, medium, or long terms to solve a problem in a region or country.
The class project involved researching economics or finance topics, socio-demographics, or environmental issues in Mexico or any other country; the objective is to attempt to answer a question using actual data. The study was carried out in three stages following the general structure described by Wooldridge (2015):
1. Phase 1. The topic is chosen, the problem to be solved is defined, and the research question is posed.
2. Phase 2. Students use econometric software to program databases, create variables, make statistics, estimate econometric models or predictions.
3. Phase 3. Presentation of results.
The work was carried out in teams using project-based learning methodology.

Method: How can we use AI tools in empirical economics projects?
Since the course involves learning data processing, applying statistical and econometric techniques, model estimation, and predicting variables, the students must learn to program using econometric software. The course teaches students how to program using specific software (Stata). Throughout the semester, they learn various programming codes that allow them to execute tasks such as graphing, performing descriptive statistics, estimating a model, and predicting variables. However, in the research process, students confront actual data and encounter challenges that require additional codes. Searching for programming codes using traditional search engines can take time and only sometimes results in finding the required information. Students sometimes need help using econometric software due to the nature of the data, choice of model to estimate or the variables to predict. Therefore, using GenAI tools for programming could mitigate these difficulties (IBM Education, 2023). Observing this potential to address the problems raised, I suggested the following tools which can help with information searching, saving time, and producing content variety when editing and generating codes:
Elicit: an AI tool used to search for relevant scientific articles and analyze them.
Perplexity: a chatbot that answers questions and can search for information on specific topics, summarize, and produce programming codes.
ChatGPT: like Perplexity, an AI tool that answers questions, generates text, and automates specific tasks.
AIcyclopedia: a directory of AI tools that suggest which can be used to suit the user’s needs.
I allowed the students to use additional AI tools if they informed me.
To observe students’ adoption of GenAI with an emphasis on academic integrity, I designed a form to track the AI tools students used in my class. If they used GenAI, they had to complete the form when submitting the final manuscript.

In addition to the tracking format, I designed three educational resources (detailed below) to inform students about what was expected in each deliverable and introduced AI tools.
1. I showed students how to pose a research question with and without the tools and how to search for a research topic. Using Perplexity AI, the students had to answer, “What can be investigated in Mexico using time series techniques?” The tool gave a series of results, and from there, the students oriented their search according to their interests.
2. I presented students with an example of how to write the empirical project methodology. Additionally, to broaden their understanding, they used ChatGPT or Perplexity AI to write the method for an empirical project related to time series.
3. I showed students how to search for and edit ChatGPT and Perplexity AI programming codes. It minimized search times and improved the information output.

Results
Having adopted and used the tools, the students were both open and cautious about GenAI at the end of the course. Some teams mentioned that although the tools were helpful, they had to maintain critical thinking regarding their use. Some teams said that searching for reliable information motivated them to use the tools.
Moreover, the AI tools suggested some articles for the literature review that students could not find using traditional search tools. These suggestions led them to search for the articles in the university library databases. The AI tools could motivate students to improve their information searches using reliable sources, thus elevating the quality of the literature review and the discussion of the results. However, some stated they could not access the articles suggested by the tools because “many papers were private,” so they had to look for them in the library. Concerning the programming codes, the students indicated that AI saved them time and resolved several of their concerns.

References
Currie, G. M. (2023, May). Academic integrity and artificial intelligence: is ChatGPT hype, hero, or heresy? In Seminars in Nuclear Medicine. WB Saunders. https://doi.org/10.1053/j.semnuclmed.2023.04.00
IBM Education (2023). AI code-generation software: What it is and how it works. https://www.ibm.com/blog/ai-code-generation/
Moya, B., Eaton, S. E., Pethrick, H., Hayden, K. A., Brennan, R., Wiens, J., McDermott, B., & Lesage, J. (2023). Academic Integrity and Artificial Intelligence in Higher Education Contexts: A Rapid Scoping Review Protocol. Canadian Perspectives on Academic Integrity, 5(2), 59–75. https://journalhosting.ucalgary.ca/index.php/ai/article/view/75990
Ta, R & D. West. (2023) Should schools ban or integrate GenAI in the classroom? The Brooking Institution. https://www.brookings.edu/articles/should-schools-ban-or-integrate-generative-ai-in-the-classroom/
Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. 6th ed.
Based on a publication in Observatorio del Instituto para el Futuro de la Educación del Tec de Monterrey https://observatory.tec.mx/edu-bits-2/how-to-address-academic-integrity-in-practical-projects-using-ai/

 

 

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