Assessment possibilities abound within the current generative artificial intelligence (GenAI) landscape. Here, we share resources highlighting how you might lean into GenAI and leverage it in your assignments.
Harnessing the Power of AI: Transforming Assignments and Assessments in Higher Education
Alchemy
This recording of Alchemy’s August 2023 webinar provides an overview to how instructors might leverage AI in their assignments and assessments while maximizing academic integrity.
Jess Taggart
I appreciate how this webinar provides an accessible introduction to thinking about assessment and AI. I enjoyed seeing the real-time examples of how ChatGPT handled different assessment prompts.
Georgetown University Center for New Designs in Learning and Scholarship
This website from Georgetown University’s Center for New Designs in Learning and Scholarship presents strategies for designing authentic assignments, as well as ways to integrate AI into assignment design. There are example assignments from Georgetown faculty that take into account the capabilities of AI.
Jess Taggart
To me, this website is a great place to start thinking about how you might leverage AI in your assignment design; use the examples of new and adapted assignments to spark your own ideas. If you are not yet ready to integrate AI into your assignments, you will still find useful suggestions for designing authentic, original assignments.
Designing assignments effectively relies on the same principles as before the rise of natural language processing tools, with a few added considerations explained in this section.
As you begin to (re)design assignments in your course, we encourage asking yourself the following six questions (adapted from Derek Bruff, former director of the Vanderbilt University Center for Teaching, as written in his blog):
Why does this assignment make sense for this course?
What are specific learning objectives for this assignment?
How might students use AI tools while working on this assignment?
How might AI undercut the goals of this assignment? How could you mitigate this?
How might AI enhance the assignment? Where would students need help figuring that out?
Focus on the process. How could you make the assignment more meaningful for students or support them more in the work?
Incorporating AI in Teaching: Practical Examples for Busy Instructors
Daniel Stanford
In this post, Daniel Stanford (DePaul University) shares a variety of “AI-infused learning activities” that instructors can incorporate into their classes, from text revision and critiques of AI-generated content to creative writing and role-play.
Jess Taggart
I appreciate this post because it provides concrete, manageable examples of assignments that instructors can experiment with right now. I believe the examples can inspire instructors across a variety of disciplines to try something new without too heavy an investment of time and energy.
Since January 2023, I’ve talked with hundreds of instructors at dozens of institutions about how they might incorporate AI into their teaching. Through these conversations, I’ve noticed a few common issues:
Faculty and staff are overwhelmed and burned out. Even those on the cutting edge often feel they’re behind the curve.
It’s hard to know where to begin.
It can be difficult to find practical examples of AI use that are applicable across a variety of disciplines.
To help address these challenges, I’ve been working on a list of AI-infused learning activities that encourage experimentation in (relatively) small, manageable ways.
Assigning AI: Seven Approaches for Students, with Prompts
Ethan R. Mollick and Lilach Mollick
This paper by Ethan R. Mollick and Lilach Mollick (University of Pennsylvania - Wharton School) describes seven possible approaches for using AI in the classroom: AI as mentor, tutor, coach, teammate, student, simulator, and tool. The authors describe each of these roles, along with their possible pedagogical benefits and risks.
Jess Taggart
I believe this paper does an excellent job showcasing a range of possibilities for the thoughtful, intentional use of AI in the classroom. I find the example prompts and output bring the ideas to life and make them easy to apply across contexts.
This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI’s output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop", the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.
