Teaching and Learning with Generative AI

Exploring the integration of Generative AI (GenAI) in education at NDSU, focusing on its transformative potential and practical applications for educators in teaching and learning.

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How I use GenAI in My Course?

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 email your professor or instructor.

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: FERPA issues exist when inputting 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.

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, incorporating student collaboration and group work, and fostering a culture of academic integrity. Additionally, the focus of assessment should move away from the final product to the process (Hodges & Kirscher, 2023). It is also 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 assignment 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 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 with GenAI?

Research shows that faculty are concerned about cheating and academic integrity. These concerns are legitimate. Ironically, most students do not consider the use of GenAI tools as cheating (Bowen & Watson, 2024). Hence, instructors must articulate clearly in the syllabus and in their conversations with students about what is “acceptable use of GenAI” in their courses.

Some proactive measures and strategies 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, we must 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, instructors might need to consider the adoption of alternative evaluations in place of high-stakes examinations. There is a pressing need to transition away 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 authentic assessment.

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 Instructional Design Center (IDC):

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

References 

  • 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


Keywords:
Artificial intelligence ndsu, ndsu artificial intelligence, ai, ai in ndsu, ChatGPT, Copilot, chatgpt, genai 
Doc ID:
138283
Owned by:
Sharley K. in NDSU IT Knowledge Base
Created:
2024-07-08
Updated:
2024-08-21
Sites:
NDSU IT Knowledge Base