Generative AI: Course Design, Teaching, and Learning

When designing a course, instructors and instructional designers have always tried to figure out questions like: “What do we want the student(s) to be able to do?”, “How do we know that a student has learned?” (the process question), “Did the student get the right answer?” (the product question), and “What instructional strategies will support learning?” (Hodges & Kirscher, 2023). The advent of generative AI (GenAI) tools, like ChatGPT, CoPilot, and Bard, have now made these questions more difficult to address especially in online courses.

Hodges and Kirscher (2023) suggested different instructional strategies including moving the focus of assessment away from the final product to the process, incorporating oral assessments, redesigning assignments to be more personalized or context-dependent, encouraging critical thinking and creativity, and incorporating reflective writing.


What is Generative Artificial Intelligence (GenAI) like ChatGPT?

Generative Artificial Intelligence, or GenAI, is a natural language model that can generate human-like responses. These responses could be text, image, graphic, video, or music.

Introduction to AI for Teachers and Students by Ethan Mollick and Lilach Mollick Video (10:16 mins)

How does GenAI works?

When you ask GenAI a question, you’re essentially giving it an instruction. This instruction could be something like: “Here’s a fragment of text (I like my bagel with…). Tell me how this fragment might go on. What words are likely to come next?” The GenAI then uses its understanding of language, which it has learned from the vast amount of data it has been trained on, to generate a continuation of the text.

It’s important to note that every output from GenAI is generated from the texts in its database. It doesn’t “know” anything in the way humans do, but rather, it uses statistical patterns to generate its responses. This means that while GenAI can produce remarkably human-like text, it doesn’t actually understand the content in the same way a human would. It’s a tool for generating content.

What is Prompt Engineering for GenAI?

Prompt engineering in GenAI involves finding effective ways to ask a language model (GenAI tools) to give you the information or response you want. A prompt is an input or instruction given to a language model to elicit a specific output. An effective prompt must include the appropriate phrases, words, and sentence structure to generate the best response. OpenAI developed six strategies for getting better results. The strategies can be found here: Prompt engineering 
The Art of Prompting Engineering Simplified Video (10:08 mins)

Can I use GenAI?

While GenAI offers numerous possibilities, its application also raises concerns. NDSU community members should be cautious when using AI tools. With any AI tool, ask questions such as the following:

  • What is the AI tool/application keeping track of?
  • Where is it storing the data?
  • Who is it sharing the data with?
  • What does it do with the data, and who owns the data?

It is imperative to use AI responsibly. The basic rule is to never attribute AI’s work (output) as your own. It is recommended for students to consult with their course instructors on whether or not GenAI tools are allowed in their courses. Please adhere to the Standards for Academic Honesty & Integrity at NDSU. If you are uncertain or have any queries or concerns, don’t hesitate to reach out to your professor or instructor via email.

Instructors are encouraged to have conversations with students and state clearly their expectations regarding the use of GenAI in their course syllabus. These declarations should be unambiguous and concentrate on defining what constitutes permissible and impermissible use within the context of the course. Examples of syllabus statements on GenAI tools usage expectations to your students from the University of Vermont

Note: There are FERPA issues when inputting any students' submitted work into GenAI tools. For any questions on IT Security, check with the NDSU Chief Information Security Officer, and for any questions on FERPA, check with the University Registrar.

What are the challenges and opportunities of using GenAI in course design?

GenAI tools have shown promise in course design including brainstorming learning objectives, suggesting assessment activities, generating study materials, creating discussion prompts, and developing rubrics. However, renowned educators like Dr. Dora Demszky, assistant professor at Stanford University, cautioned against relying solely on GenAI for course design. Dora stated that “human teachers are [still] essential in the process of learning… AI should be in the loop and teachers should be in charge of them.” Therefore, instructors need to fact-check GenAI outputs as they can sometimes be inaccurate, biased, and lack emotional intelligence.

Practical Example: Students can brainstorm a particular concept/subject using GenAI and then develop the writing on their own without AI. Other ideas could be allowing students to generate responses from AI and then let them critique the style, accuracy, and bias of the AI output. Learn more about course design with GenAI.

Anna Haney-Withrow and Heather Olson provided some more ideas in the videos below:

What are the pedagogical considerations before using GenAI in student activities?

There are several considerations when considering the use of any technology including GenAI. It is important to consider “pedagogy first, technology second” (Kolb, 2017). The learners and the learning process should be at the center of decision-making when incorporating GenAI into student activities. This ensures that there is a clear purpose for its use. Some questions to consider include:

  • How can the GenAI tool support your learners’ goals?
  • What are your learners' GenAI literacy levels and what are the opportunities for them to improve?

  • Is the GenAI tool easy to use and will it be useful for your learners?
  • How can the GenAI tool be used to promote higher-order thinking skills, critical thinking, and problem-solving

  • Does the GenAI tool promote transparency, protect student privacy and data, and ensure equitable access?

How can I integrate GenAI into my assessment strategies?

Assessments help us answer critical questions about the learning process such as “What have our students learned and how well have they learned it?” and “How successful have we been at what we are trying to accomplish?” (Huba & Freed, 2000). Effective assessment should transition from a focus on ‘knowledge checking’ to demonstration of skills and abilities. This underscores a shift in educational assessment paradigms where the emphasis moves away from memorizing and recalling (rote memorization) to focus on students demonstrating their skills and abilities through authentic tasks and real-world problem-solving.

Redesigning your assessments to be more authentic is one way to “ChatGPT-proof” your assessments. This means designing assessments that encourage critical thinking, creativity, and originality, incorporate student collaboration and group work, and foster a culture of academic integrity. It is important to question what is being evaluated, the methods used, and the purpose. Learn more about AI in Assignment Design and Assessment Design Considerations with AI in Mind. See examples of assignments prompts incorporating GenAI tools adapted from Rob Rose (2023).

  • Example 1: Designing assessments that promote critical thinking

GenAI Prompt: Generate several open-ended discussion questions that require students to construct their own viewpoints, scrutinize various perspectives, and substantiate their opinions with evidence. I prefer these questions to be challenging for students to answer using ChatGPT. These are intended for a postgraduate course named “Curriculum Design and Delivery.” The objective these prompts should cater to is: “Evaluate different pedagogical approaches for their effectiveness in diverse settings.”

  • Example 2: Designing assessments to apply concepts in real-world scenarios

GenAI Prompt: “Two local governments in North Dakota, facing different demographics and economic realities, propose contrasting approaches to early childhood education funding. One advocates for expanding Head Start programs, while the other leans towards tax breaks for childcare provider businesses. As an early childhood advocate, analyze the legal merits of both proposals within the framework of North Dakota's Early Childhood Education and Development Act (ECEDA) and federal funding initiatives like IDEA. Explore potential benefits and drawbacks, considering equity, access, and long-term sustainability.”

How should I address cheating and academic integrity in relation to GenAI?

Research shows that faculty are concerned about cheating and academic integrity. These concerns are legitimate. There are proactive measures and strategies that could help in enhancing academic integrity including:

  • Firstly, a paradigm shift is required. Bertram Gallant stated that the emphasis should transition from “How do we stop students from cheating?” to “How do we ensure students are learning?” Research on student dishonesty and academic integrity indicates that when students recognize the importance of education and can establish a significant connection with the learning material, they are less likely to cheat.

  • Secondly, it is imperative for us to engage in comprehensive discussions with our students about academic integrity and its potential risks to their careers. Our students are at the center and they need to understand the importance of accountability. We need to establish connections with our students, build relationships, and humanize our learning process.

  • “Thirdly, our educators might consider the adoption of alternative evaluations in place of high-stakes examinations. There is a pressing requirement for us to transition from the “Conventional modes of assessments” towards more genuine, relatable, and real-world problem-oriented evaluations. This could encompass methods such as role-playing, scenario-based learning, problem-based learning, system-based learning, and so on.”

Learn more about academic integrity. Video on What are the tools to catch students cheating with GenAI? By Dr. Heather Olson and Anna Haney-Withrow.

What are some of the GenAI suggestions discussed during the Academic Writing series?

The Writing Program in the English Department (Lisa Arnold, Benjamin Melby, & Alexandra Rowe) along with Daniel Kenzie, School of Pharmacy, presented some general principles of good writing assignments. These include considering the purpose of the writing assignment, stating the audience and the writing context, encouraging deeper engagement with specific parts of the course content, scaffolding writing processes throughout the course, incorporating feedback and revision of drafts, and localizing or regionalizing assignments. The panelist also recommended that instructors could ask students to reflect on their writing processes while submitting final products and students could be asked to submit their assignments in multiple modes (e.g. written text video presentation).

Learn more about academic writing and ChatGPT from the discussion panel organized by the Writing program in the English Department NDSU and the Learning and Applied Innovation Center (LAIC):

*Portions of this document were generated using an AI-powered assistant, Microsoft Copilot, ChatGPT. Content has been reviewed, verified, and updated by humans.


  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  • Hodges, C. B., & Kirschner, P. A. (2023). Innovation of Instructional Design and Assessment in the Age of Generative Artificial Intelligence. TechTrends, 1-5.
  • Huba, M. E., & Freed, J. E. (2000). Learner-centered assessment on college campuses: Shifting the focus from teaching to learning. Allyn & Bacon, 160 Gould St., Needham Heights, MA 02494.
  • Kolb, L. (2017). Learning first, technology second: The educator’s guide to designing authentic lessons. International Society for Technology in Education.
  • Rose, R. (2023, April 10). CHATGPT-proof your course. ChatGPT in Higher Education.

Additional Resources

Recorded Sessions - Academic Writing and ChatGPT Series

KeywordsGenAI, course design, teaching, ChatGPT GENAi, Generative Artificial Intelligence, chatGPT, chatbot, Bard, copilot, Bing, genai, AI , artificial intelligence, GAI, DALL-E, mid-journey, ai, chat gpt, chatgpt   Doc ID133755
OwnerSharley K.GroupIT Knowledge Base
Created2023-12-29 12:15:27Updated2024-04-16 12:56:09
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