Artificial intelligence (AI) has become a tool used in the classroom. The integration of technology in education has historically been slower (Holmes, Bialik, and Fadel 2019). Some educators lack training to effectively use AI in their classrooms, which may limit their ability to design AI-based coursework (Amado-Salvatierra etal. 2024). Furthermore, a lack of understanding of how AI use can be applied can potentially hinder AI integration (Afzaal etal. 2024). The focus of this article is to share examples of AI input prompts to generate case studies as learning tools to help students learn about course topics and learning outcomes.
Case studies provide opportunities for students to learn and/or reinforce what they have already learned. While creating case studies using AI, it is important to use the correct prompts to get the correct output. For example, a single prompt asking AI to provide five cases on five different topics may not provide sufficient details. However, encouraging AI to generate case studies on one topic could generate better cases. Using the details at the initial prompt gives you better output. The example of the AI prompt is “suppose the role of a professor teaching an introductory accounting course. Generate a case study of students that will be used to learn a very basic form of a balance sheet. Make sure the case studies are relevant to the real world. The roles, courses and topics listed can be adjusted to suit the appropriate class. The generated case studies should be reviewed to confirm the alignment of a particular topic with the learning outcome.
The cases should be generated in a form that allows for a measurable assessment of student learning. Specifying this detail at the AI prompt while generating a case study will help you ensure that the AI output is measurable. An example of an additional AI prompt to include in the previous example is, “The case study involves including an allocation portion that allows faculty to measure student performance.” To perform a careful evaluation of the output from this prompt, you must ensure that topic adjustments are made.
Once the appropriate case studies have been developed, AI can provide grading rubrics to AI-generated case studies by encouraging AI to generate rubrics. An example of an AI prompt for generating a grading rubric is “Providing a grading rubric that matches this case study.” We recommend reviewing the use of grading rubrics to measure the right topic and learning.
At any point in this process, you can use AI to modify the output. For example, here is the appropriate AI prompt to change something in a case study: “Update the case study above to include five assets, three liabilities, and two owners' equity accounts. At any point in the revision, you may need to play back evaluations of previously generated outputs (for example, rubrics). It is recommended to pay attention to AI prompts that produce acceptable output. This will allow these prompts to be reused for future AI-generated case studies.
Another output of the case study could be a response sheet for use by faculty and then shared with students. An example of an AI prompt might be: 'Please provide an answer sheet for this case study. Make sure to include details on calculations and definitions of important terms.
To add depth to using AI in the classroom, faculty may want to create two case studies on the same topic. One is done by students without AI, and the other is AI. This two-case study method allows students to learn how to properly use AI. A list of the right AI input prompts for students to use can help you learn how to design the right AI prompts for your students. This effort is useful for students as research shows that the feeling of preparation is reduced when AI applications are inadequately exposed (Hsiao and Han 2023).
An example of an AI prompt for creating a two-case study with or without AI is “suppose that an accounting professor is supposed to be a role in teaching an introductory accounting course. Without AI, the student who uses it to learn a very basic form of the balance sheet generates one case study. Additionally, it generates a second case study in the same format as the first case study that we use to learn a very basic form of the balance sheet using AI. Make sure the case study is relevant to the real world. The case study is to include an allocation portion that allows the faculty to measure the student's performance.
Using this two-case research methodology, faculty can measure changes in student performance and see how AI use can help students and faculty understand the topics of case studies. Provide students with answer sheets to compare performance and critically analyze AI output.
A valuable measure to assess is the time students spent on each case. Case study line items can be added by AI by including prompts such as:
In addition to non-AI and AI-USAGE cases, students can also perform conceptual assessments. Such assessments are qualitative and allow students to critically assess how AI supports topic efficiency and accuracy. Another focus is a qualitative assessment of how AI can be used in future careers based on specified topics in case studies. For example, a conceptual question might explain how someone in their profession benefits from AI and improves performance in their future careers. If these cases were created for use throughout the course semester, students will have a clearer understanding of how AI will be applied to classroom experiences.
In conclusion, faculty can use AI to create tools such as case studies focusing on a specific topic, exposing students to real-world scenarios and enhancing student learning. As faculty and students use AI, it is very important to acknowledge that current AI output is not always accurate. Faculty and students should evaluate the accuracy of the output generated by AI and adjust it as necessary. Experimenting with a variety of AI inputs will help teachers feel more comfortable using AI. Recognizing the role of AI in the classroom does not replace faculty, but it can help students provide excellent learning opportunities and demonstrate how AI can be used effectively.
Rhonda Gilreath is an associate professor of accounting at Tiffin University in northwest Ohio. She explores new opportunities to implement in the classroom and improves her educational approach to preparing students for careers.
reference
Afzaal, M., Shanshan, X., Yan, D. , and Younas, M. 2024. IEEE Access 12:113275–113299. https://doi.org/10.1109/access.2024.3443313.
Amado-Salvatierra, H. R., Morales-chan, M., Hernandez-Rizzardini, R., & Rosales, M. 2024. In “Exploring Educators' Perceptions: Integrating Artificial Intelligence in Higher Education.” 2024 IEEE World Engineering Education Conference (Edunine)1–5. https://doi.org/10.1109/edunine60625024.10500578.
Holmes, W., Bialik, M. , and Fadel, C. 2019. Artificial intelligence in education: promises and implications for education and learning. Boston: Curriculum Redesign Center.
Hsiao, D. , and Han, L. 2023. “The impact of data analysis and artificial intelligence on future accounting professionals: an accounting student's perspective.” Journal of Theoretical Accounting Research 19(1): 70–100.
