Classroom Applications, Case Studies, and Additional Resources
Introduction
Incorporating AI literacy into higher education can feel like a daunting and time-intensive endeavor. This chapter highlights real-world examples of how educators are effectively teaching the competencies covered in this chapter in their classrooms. By showcasing diverse approaches, we aim to inspire and empower faculty to integrate AI literacy into their teaching practices, regardless of discipline or expertise.
Aligned with the philosophy of open educational resources (OER), we encourage the adaptation of existing approaches and resources to save time and effort while addressing the unique needs of individual educators and their students. By building on proven strategies, faculty can seamlessly incorporate AI literacy into their teaching without reinventing the wheel.
In addition to these examples, we provide effective pedagogical strategies that can be adapted to various educational contexts, along with curated resources for further exploration.
We also encourage you to contribute to this growing body of knowledge. If you have a classroom example to share, please submit it via the form below or contact us at teachingwithaioer@virginia.edu. Your insights and experiences can help shape the future of AI education in higher ed.
AI Evaluation: AI-Enhanced Instructional Design
Example Overview: This interactive open textbook, created collaboratively by University of Saskatchewan graduate students, showcases how educators can implement authentic learning experiences that develop students’ abilities to critically evaluate AI technologies while explicitly connecting to media literacy frameworks. The project demonstrates how structured evaluation activities can bridge technical skills with deeper critical analysis.
Recommended Use: Educators can adapt this project-based approach to develop both students’ technical proficiency with AI tools and their critical evaluation abilities. By requiring students to explicitly connect their analyses to specific literacies (information, media, data, or digital), instructors help students engage in a deeper level of critical analysis rather than superficial observation. Encouraging students to create openly licensed resources further enhances authenticity and impact, as students produce work for real audiences beyond the classroom, motivating deeper engagement with evaluation frameworks.
Explore the Example: AI-Enhanced Instructional Design Pressbook, licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
AI Evaluation: Helping Students Understand the Biases in Generative AI
Resource Overview: The University of Kansas has developed a resource that equips both instructors and students with tools to analyze and discuss biases and ethical considerations in generative AI. This resource provides foundational knowledge about different types of bias that may appear in AI outputs, complemented by practical classroom activities and a curated reading list for deeper exploration.
Recommended Use: Educators can readily incorporate the provided classroom activities to help students develop critical analysis skills for identifying and evaluating biases in both text-based and visual AI outputs. The included reading list allows instructors to assign targeted readings that align with specific course objectives or student needs, helping learners develop a more nuanced understanding of how biases manifest in AI systems and their implications for various contexts.
Explore the Resource:Helping students understand the biases in generative AI
AI Evaluation: Assess Content: Assessing AI-Based Tools for Accuracy
Example Overview: The module, Assessing AI-Based Tools for Accuracy, provides step-by-step instructions and video examples designed to teach students lateral reading techniques for critically evaluating AI-generated content. Through clear explanations and practical demonstrations, students learn how to verify the accuracy and reliability of AI outputs effectively.
Recommended Use: Educators can directly integrate this module or adapt specific activities and video examples into their teaching to support students in mastering lateral reading techniques. Incorporating these resources helps students develop essential critical evaluation skills, enabling them to better navigate and assess AI-produced information.
Explore the Example: Assessing AI-Based Tools for Accuracy, licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
AI Evaluation and Assessment of Impact: Incorporation of ChatGPT and Other Large Language Models into a Graduate Level Computational Bioengineering Course
Example Overview: The paper “Incorporation of ChatGPT and Other Large Language Models into a Graduate Level Computational Bioengineering Course” presents a structured approach to integrating AI tools into STEM education. Throughout the Fall 2023 semester, the authors explored the use of ChatGPT and similar large language models (LLMs) in a graduate-level numerical and statistical methods course for PhD-level bioengineering students. The paper shares examples of ChatGPT-generated content, observations on effective course integration, and speculates on how bioengineering students may benefit from this technology in the future.
Recommended Use: Educators can draw inspiration from this paper to design structured activities that encourage students to critically evaluate AI-generated outputs in STEM. By incorporating AI tools like ChatGPT into coursework, students can gain hands-on experience, allowing them to assess the benefits and limitations of these technologies in real-world applications. This approach not only enhances technical proficiency but also fosters critical thinking regarding the ethical and practical implications of AI in their respective fields.
Explore the Example:
AI Evaluation: ChatGPT Assignments to Use in Your Classroom Today
Example Overview: The bulk of the book, developed by educators at the University of Central Florida, consists of over 60 practical assignment prompts and ideas across disciplines to assist with teaching skills for using ChatGPT (and other AI tools), including prompt engineering, evaluating output, analyzing texts, writing, generating content, studying, and career planning.
Recommended Use: This open resource, licensed under the Creative Commons Attribution-Noncommercial-Share Alike 4.0 license, allows educators to remix and adapt it as needed. You can draw inspiration from the assignment ideas on evaluation and analysis from pages 25-52, designed to help students critically analyze and evaluate AI outputs while exploring diverse applications of gen AI tools.
Explore the Example: ChatGPT Assignments to Use in Your Classroom Today
AI Evaluation: AI Assignment Library
Example Overview: Faculty at the University of North Dakota developed the resources in this AI Assignment Library in response to the immediate impact of ChatGPT and other generative AI tools on higher education. Assignments included in this collection emerged from an intensive faculty workshop focused on generative AI and the evolving information landscape. Faculty authors participated in peer-reviewed assignment charrettes and revised their assignments based on constructive feedback.
Recommended Use: This collection offers various classroom activities designed to support students as they critically evaluate AI-generated outputs. We recommend educators browse the examples provided below and within the full database to gather inspiration and adapt activities suitable for your specific teaching contexts.
- AI and Concerns on Bias and Correctness in Engineering
- Is ChatGPT good at establishing validity? – For research
- Annotated Bibliography Assignment for an Art History Class
- Using Generative AI in the Classroom: Fact-Checking ChatGPT as Part of a Capstone Project
Explore the Example: AI Assignment Library
Reflect and Apply: Educator’s Toolkit
Pedagogical Strategies and Considerations
Cultivating an Integrated AI Literacy Framework
Recognize that effective AI evaluation is built upon a foundational literacy ecosystem encompassing information, media, data, and digital literacies. Strengthen these interconnected literacies, demonstrating how skills in one area enhance others when evaluating AI. You don’t need to be expert on all the literacies but think about which literacies might be most relevant to your field and make sure your AI evaluation provides opportunities for students to reflect on the literacy framework. The AI-Enhanced Instructional Design Pressbookis a good example of this approach of having students evaluate AI outputs against the media literacy framework that is essential to the course.
At the same time, address existing literacy gaps proactively, understanding that students may struggle with evaluating AI outputs if they lack foundational skills in effective writing or analytical thinking. Anticipate and guide students beyond superficial critiques by explicitly teaching them what constitutes strong analytical work and what to look for beyond surface-level observations in AI outputs. Model critical evaluation by transparently discussing your own experiences with AI in teaching preparation, including benefits and limitations. Address the challenge of missed relevancy assessment by explicitly connecting AI-generated content to course readings, personal experiences, and assignment purposes.
You could also consider collaborating with other campus units, such as the writing center and the library, to provide additional resources to fill these gaps. Additionally, utilize structured frameworks, such as the TIMED model (Technology, Information, Media, Ethics, Data), to provide clarity and guide students in a systematic critical assessment of AI outputs for accuracy, relevance, bias, ethical implications, and logical coherence within disciplinary contexts.
Designing a Scaffolded Learning Journey for AI Assessment
Implement comparative experiences, deliberately alternating between AI-permitted and AI-restricted activities. This helps students develop a nuanced understanding of AI’s distinct contributions or limitations in various learning contexts. Incorporation of ChatGPT and Other Large Language Models into a Graduate Level Computational Bioengineering Courseis a good example of helping students compare AI-permitted and AI-restricted activities and self-assess the benefits and concerns. In addition, embed ongoing reflection by providing structured opportunities after AI-permitted assignments for students to examine how the technology influenced their thinking processes and the quality of their work.
Additionally, you can conclude the activities with a culminating synthesis, where students analyze their learning trajectory and identify patterns in how AI has shaped their knowledge acquisition, skill development, and academic identity. Consider providing scaffolded assessment frameworks that evolve with student experience, offering more structured guidance for beginners and more open-ended prompts for advanced users.
Fostering a Psychologically Safe and Open Learning Environment
Establish psychological safety in your classrooms where students feel comfortable sharing both successes and challenges with AI tools without fear of judgment or academic penalty. This is crucial for honest self-assessment. It’s important to validate the full spectrum of student experiences with AI, recognizing that different learning preferences, backgrounds, and contexts can lead to varied impacts from the same tool.
Reflection Questions
- How might you encourage students to explicitly connect their AI analyses to specific literacy frameworks such as information, media, data, or digital literacies, similar to the example shown in AI-Enhanced Instructional Design Pressbook?
- How might you leverage open resources, such as Assessing AI-Based Tools for Accuracy, for teaching students practical techniques, such as lateral reading, to critically evaluate AI-generated content and verify its accuracy and reliability? Which staff/units at your university can you collaborate with to teach these techniques if you don’t feel confident teaching yourself?
- Which of the practical assignment prompts from “ChatGPT Assignments to Use in Your Classroom Today,” particularly those focusing on evaluating output and analyzing texts, can you adapt for your course objectives?
- The AI Pedagogy Project features a collection of AI assignments. After browsing the assignments related to Level 3 competencies, which assignments are you interested in adapting to your classes? How might you adapt the assignments to better align with your course learning objectives while still maintaining their core purpose?
Use the Padlet Discussion Board to share your thoughts with peer educators.
Reflection Questions
- How might you encourage students to explicitly connect their AI analyses to specific literacy frameworks such as information, media, data, or digital literacies, similar to the example shown in AI-Enhanced Instructional Design Pressbook?
- How might you leverage open resources, such as Assessing AI-Based Tools for Accuracy, for teaching students practical techniques, such as lateral reading, to critically evaluate AI-generated content and verify its accuracy and reliability? Which staff/units at your university can you collaborate with to teach these techniques if you don’t feel confident teaching yourself?
- Which of the practical assignment prompts from “ChatGPT Assignments to Use in Your Classroom Today,” particularly those focusing on evaluating output and analyzing texts, can you adapt for your course objectives?
- The AI Pedagogy Project features a collection of AI assignments. After browsing the assignments related to Level 3 competencies, which assignments are you interested in adapting to your classes? How might you adapt the assignments to better align with your course learning objectives while still maintaining their core purpose?
Contribute: Add a Resource