Category: prompting AI

Beyond the First Response: Prompting with Purpose in University Classrooms – Faculty Focus
Are you one of the reported 61% of higher education faculty now using AI in your teaching (Weaver, 2025)? A recent survey by the Digital Education Council (2025) found that 88% of AI-using faculty report minimal to moderate use. Further, 83% of faculty question students’ ability to evaluate AI-generated content, and 82% worry about student overreliance on AI tools.

So, while a majority of faculty are incorporating AI, many of us remain cautious about how to use it effectively in our higher education classrooms. This tension is further echoed in a recent 2025 EDUCAUSE AI Landscape Study, which reports 57% of schools, up from 49% last year, now identify AI as a “strategic priority” as they continue to adapt to the expanding impact of AI across teaching and learning (Robert & McCormack, 2025).

Our institutions want us to use AI in our classrooms, but how can we do this well? Research by Zhou and Peng (2025) found that AI supported instruction can enhance both student engagement and creativity, especially in creating personalized and collaborative learning experiences. Similarly, Walter (2024) found that training educators and students in prompt engineering and critical evaluation is a key component necessary to maximize AI’s potential while reducing risks of misuse and over reliance. To enhance our content, we need to think about how to use AI purposefully, training both ourselves and our students to engage with AI tools critically, creatively, and ethically.

This article examines how faculty can incorporate AI tools effectively into their disciplines, while guiding students to use AI to foster critical thinking and creative application. Drawing on my own research, it offers strategies to support thoughtful integration of AI into higher education classrooms, with a focus on ethical awareness and responsive instruction design.

What I Learned From Using AI in My Teaching

Over the past school year, I used AI as a tool in my undergraduate courses and found that students were not as adept at using AI as I had suspected. In fact, when I introduced AI as a required component of the course framework at the start of the semester, many students were uncertain how to proceed. Some shyly offered that they had used AI in courses previously, but many were hesitant, having been repeatedly warned that using AI could jeopardize their academic careers. Without explicit, scaffolded instruction, both students and faculty risk using AI superficially, missing its potential to meaningfully transform teaching and learning.

When AI Becomes the Assignment

In Spring 2025, I led a research project in my classes exploring how university students used AI tools, such as ChatGPT, to support iterative writing and refining complex tasks like lesson planning. The study emphasized ethical AI use and focused on prompt engineering techniques, including the use of voice-to-text, targeted revision, and staged feedback loops to improve idea generation, structure, and differentiation. I wanted students to engage in a critical evaluation of AI outputs, developing greater precision and agency in applying AI suggestions across drafting stages.

What I found was that students did not initially know how to talk to AI, rather they talked at it. At first, students did not get useful results because they were not tailoring their prompts enough. One student offered “I had to ask the same question 50 billion different ways to get the right answer.” What I discovered over those first few weeks was that students needed to learn to dialogue with AI in the right ways. They had to be intentional in what they were asking it and tailor their prompts specifically.

Try this instead:
- Begin broad, then refine. Encourage students to start with a general idea, then narrow their prompts based on assignment goals and relevance of the AI’s output.
- Promote iterative prompting. Teach students to revise their prompts by engaging in an ongoing process of dialoguing with AI, aimed at narrowing down their ideas. Author WonLee Hee (2025) offers the following framework: prompt, generate output, analyze, refine prompt, and repeat.
Why Prompting Is Worth Teaching

Students are using AI, but often without the skills to do so effectively—and that is where we come in. Poor prompting reinforces the very over-reliance that faculty fear, training students to accept whatever results AI delivers, rather than critically questioning them. When prompts are vague or generic, the results are too.

Students need specific instruction on how to prompt AI effectively. In my classes I used a structured, multi-step process that students followed each week. However, after reviewing student feedback and surveys, I realized that the process involved too many steps. If I wanted my students to use AI meaningfully beyond my course, I would need to refine and simplify the approach.

Try this instead:
- Incorporate guided practice. Use a consistent AI tool at the start of the semester (I used ChatGPT) and model effective prompting and revision to help students build foundational skills.
- Gradually increase student choice. After the initial learning phase, allow students to mix and match AI tools to personalize the process and deepen their engagement.
- Embed critical reflection. Encourage students to treat AI as a thinking partner, not an all-knowing source. Design assignments so that they require ongoing interaction with AI (Gonsalves, 2024), such as using AI to generate counterarguments to their own essays or applying math concepts to real-world problems to identify gaps or misunderstandings in their thinking.
A Simple Framework for Better Prompts

A simple, three-phased framework will be more user friendly.
1. Explore: Encourage students to begin by collecting and thinking through wide-ranging ideas. Start with speech-to-text to brainstorm. Then narrow the focus, identify gaps, and use AI to help fill them.
1. Refine: Have students evaluate the AI outputs and add specific details to further improve clarity, accuracy, and relevance.
1. Revise: Use AI to check if ideas have been clearly communicated. This type of editing involves more than fixing grammar, it is about making sure that their message is clear, focused, and appropriate for the audience.
What Changed for Students

When I incorporated these changes, I saw that my students became more strategic thinkers and were less likely to merely copy from AI. In fact, over 73% of my study participants noted that they stopped accepting AI’s first response and began asking better follow-up questions, indicating that they were dialoguing with AI rather than just copying from it. Repeated practice helped them yield more accurate AI generated support and emphasized their importance in the process. They came to view AI as a support tool not a substitute for their own ideas. At the end of the study, one student noted “You have to be very specific… I have learned how to tweak my prompt to get the result I want.” Another, stated that “I started editing ChatGPT instead of letting it write for me.” These responses indicated a key shift: better prompting had reframed AI as a collaborator, not a crutch.

Final Thoughts

Teaching students how to create effective prompts is not about using technology, it is about teaching them to craft better questions. This practice reinforces critical thinking skills so many of us aim to develop in our disciplines. When students learn how to guide AI, they are also learning how to refine their own thinking. Encouraging reflection throughout the process fosters metacognition; by regularly engaging in this type of analysis of their decisions and ideas, students become more thoughtful, independent learners. By intentionally incorporating AI tools into our coursework, we are reducing the temptation for misuse and overreliance, creating space for more ethical and transparent use in our higher education classrooms.

AI Disclosure: This article reflects collaboration between the human author and OpenAI’s ChatGPT-4 for light editing. All ideas, examples, and interpretations are the author’s own.

Lisa Delgado Brown, PhD, is a current Assistant Professor of Education at The University of Tampa and the former Middle/Secondary Program Administrator at Saint Leo University where she also served on the Academic Standards Committee. Dr. Delgado Brown teaches literacy courses with a focus on differentiation in the general education classroom.

References

Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education, 0(0). https://doi.org/10.1177/02734753241305980

Lee, W. (2025, April 29). Prompt engineering #5: Optimizing AI interactions through iterative prompt adjustments. Medium. https://medium.com/@whee.2013/optimizing-ai-interactions-through-iterative-prompt-adjustments-9ec0974ee821

Robert, J., & McCormack, M. (2025, February 17). 2025 EDUCAUSE AI landscape study: Into the digital AI divide. EDUCAUSE. https://www.educause.edu/content/2025/2025-educause-ai-landscape-study/introduction-and-key-findings

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 15. https://doi.org/10.1186/s41239-024-00448-3

Weaver, J. (2025, February 5). Digital Education Council: Global AI meets academia faculty survey 2025. IBL Education. https://ibl.ai/blog?story=digital-education-council-global-ai-meets-academia-faculty-survey-2025

Zhou, M., & Peng, S. (2025). The usage of AI in teaching and students’ creativity: The mediating role of learning engagement and the moderating role of AI literacy. Behavioral Sciences, 15(5), 587. https://doi.org/10.3390/bs15050587
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September 29, 2025
The Use of Artificial Intelligence (AI) to Generate Case Studies for the Classroom – Faculty Focus

Artificial Intelligence (AI) has become a tool that is used in the classroom. The integration of technology in education has historically been gradual (Holmes, Bialik, and Fadel 2019). Some educators lack the training to effectively use AI in the classroom, which may limit the ability to design AI-based coursework (Amado-Salvatierra et al. 2024). In addition, the lack of understanding of how AI usage can be applied pedagogically can potentially deter AI integration (Afzaal et al. 2024). The focus of this article is to share examples of AI input prompts to generate case studies as a learning tool to help students learn course topics and learning outcomes.

Case studies provide opportunities for students to learn and/or reinforce what they have already learned. While using AI to create case studies, it is important to use correct prompts to obtain appropriate output. For example, a single prompt asking AI to give five cases on five different topics may not provide sufficient detail. However, prompting AI to generate a case study on one topic will potentially generate a more appropriate case. Using as much detail in the initial prompt helps provide better output. An AI prompt example might be: ‘Assume the role of a professor teaching an introductory accounting course. Generate a case study for students to use to learn the very basic format of a balance sheet. Make sure the case study is real-world relevant.’ The stated role, course, and topic can be tailored to fit an appropriate class. The case study generated should be reviewed to verify alignment with the specific topic(s) and learning outcome(s).

Cases should be generated in a format that allows for measurable assessment of the students’ learning. Specifying this detail in an AI prompt while generating a case study will help ensure the AI output is measurable. An additional AI prompt example to include with the previous example might be: ‘The case study is to include an assignment portion that allows faculty to measure the student’s performance.’ Careful evaluation of the output from this prompt needs to be performed to ensure topic alignment.

Once an appropriate case study has been developed, AI can provide a grading rubric for the AI-generated case study by prompting AI to generate a rubric. An AI prompt example for generating a grading rubric is: ‘Provide a grading rubric that aligns with this case study.’ A review of the grading rubric’s use to measure appropriate topics and learning is recommended.

At any point in this process, AI can be used to change the output. For example, an appropriate AI prompt to modify something in a case study might be: ‘Update the above-generated case study to include 5 assets, 3 liabilities, and 2 owners’ equity accounts.’ At any point of revision, assessment of the previously generated output (for instance, the rubric) may need to be regenerated. It is recommended to note the AI prompts that generate acceptable output so that those prompts may be reused for future AI-generated case studies.

Another output for the case study could be an answer sheet for faculty to use and to share with students afterward to enable self-evaluation of performance. An AI prompt example might be: ‘Provide the answer sheet for this case study. Make sure to include details of any calculations and definitions of key terms.’

To add additional depth to using AI in the classroom, faculty may want to create two case studies on the same topic: one to be performed by the student without the use of AI, and one with the use of AI. This two-case-study method could allow students to learn how to use AI appropriately. A list of appropriate AI input prompts for the students to use would assist in the students’ learning how to engineer appropriate AI prompts. This effort would help students because research indicates they have a diminished sense of preparedness when they have insufficient exposure to AI application (Hsiao and Han 2023).

An example of an AI prompt to produce a two-case study with and without the use of AI is: ‘Assume the role of an Accounting Professor teaching an introductory accounting course. Generate one case study for students to use to learn the very basic format of a balance sheet without the use of AI. In addition, generate a second case study with the same format as the first case study for students to use to learn the very basic format of a balance sheet with the use of AI. Make sure the case study is real-world relevant. The case study is to include an assignment portion that allows faculty to measure the student’s performance.’

By using this two-case-study method, faculty can measure changes in student performance, allowing students and faculty to see how AI use can assist with case study topic comprehension. Providing the answer sheets to students will enable them to compare their performance and critically analyze the AI output.

A valuable measure to assess would be the amount of time the student spent on each case. A line item on the case study can be added by AI by including a prompt such as: ‘Provide an item at the end of the case to enable students to report the amount of time that they spent on each of the case studies.’

In addition to the non-AI case and the AI-usage case, students can also perform conceptual evaluations. Such evaluations can be qualitative, allowing the student to critically evaluate how AI assists in efficiency and accuracy on the topic. Another focus could be qualitative evaluations on how AI can be used in their future careers based on the specified topic in the case study. For example, a conceptual question might address how someone in their profession would benefit from AI to help them perform better in their future careers. If these cases are created to be used throughout a course term, students can gain a clearer picture of how AI might be applied to their classroom experience.

In conclusion, faculty can use AI to create tools, such as case studies which are focused on specific topics, to expose students to real-world scenarios and reinforce student learning. It is very important, as faculty and students use AI, to acknowledge that current AI outputs may not always be accurate. Faculty and students should evaluate the accuracy of AI-generated output and adjust as necessary. Experimenting with various AI inputs will allow faculty to become more comfortable with using AI. Recognizing AI’s role in the classroom does not replace faculty, but it can help provide excellent learning opportunities for students and demonstrate how AI can be used effectively.

Rhonda Gilreath is an associate professor of accounting at Tiffin University in Northwest Ohio. She enjoys exploring new opportunities to implement in the classroom to improve pedagogical approaches to prepare her students for career readiness.

References

Afzaal, M., Shanshan, X., Yan, D., and Younas, M. 2024. “Mapping Artificial Intelligence Integration in Education: A Decade of Innovation and Impact (2013–2023)—A Bibliometric Analysis.” IEEE Access 12: 113275–113299. https://doi.org/10.1109/ACCESS.2024.3443313.

Amado-Salvatierra, H. R., Morales-Chan, M., Hernandez-Rizzardini, R., and Rosales, M. 2024. “Exploring Educators’ Perceptions: Artificial Intelligence Integration in Higher Education.” In 2024 IEEE World Engineering Education Conference (EDUNINE), 1–5. https://doi.org/10.1109/EDUNINE60625.2024.10500578.

Holmes, W., Bialik, M., and Fadel, C. 2019. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign.

Hsiao, D., and Han, L. 2023. “The Impact of Data Analytics and Artificial Intelligence on the Future Accounting Profession: Perspectives from Accounting Students.” Journal of Theoretical Accounting Research 19 (1): 70–100.

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May 7, 2025

Category: prompting AI

Beyond the First Response: Prompting with Purpose in University Classrooms – Faculty Focus

What I Learned From Using AI in My Teaching

When AI Becomes the Assignment

Why Prompting Is Worth Teaching

A Simple Framework for Better Prompts

What Changed for Students

Final Thoughts

The Use of Artificial Intelligence (AI) to Generate Case Studies for the Classroom – Faculty Focus