The AI Syllabus

Critical AI Literacies: 

• Identify, engage with, and evaluate AI output for meaning, accuracy, and bias.
• Analyze the impact of AI on teaching and learning. 

On this page you will find multimedia sources that demonstrate how individual teachers are  implementing AI tools into their classrooms and how education, as an institution, regards AI. Additionally, there is discussion of how AI can reinforce biases in school settings and offer increased learning opportunities for neurodiverse students. This page also includes key concepts, activities, and assignments to build understanding of and critically engage with the AI through the lens of education, teaching, and learning. 

Teaching and Learning + Education

Sources: 

 

“Advancing Meaningful Learning in the Age of AI”. Artificial Intelligence Tools from Oregon State University Online. (2023)

• A reconsideration and revision of Bloom’s Taxonomy for rethinking course outcomes. #Practical

 

Bruin Learn. (2023, March 6). What is ChatGPT and how does it relate to UCLA’s academic mission? [Video]. Youtube. 

• A 60 min moderated town hall discussion among UCLA faculty, Safiya Noble (Professor of Gender Studies and African American Studies), Ramesh Srinivasan (Professor of Information Studies), and John Villasenor (Professor of Electrical Engineering, Public Policy, and Law and Management) in which they discuss the impact of AI (and specifically, ChatGPT) on student learning. #Practical #Philosophical

 

FitzPatrick, D., Fox, A., & Weinstein, B. (2023). The AI classroom. TeacherGoals Publishing. 

• This guide for AI in education begins with a little bit of historical background on AI including acknowledgement that we are still in the very early days of our life with AI (likened to mid-nineties Internet) before shifting into a practical handbook that includes real world assignment and educational resources for teaching and learning with AI e.g. graphic organizers, lesson plans, essay prompts, etc. #Practical

 

Maynard, A. (2023, July 16.) How I used ChatGPT to develop a college class about itself. Slate. 

• This article discusses how a professor used ChatGPT to design and run a course about AI. #Practical #Philosophical

 

McNutt, C. and Covington, N. (Hosts). (2023, June 17). The implication and biases of AI in classrooms with Meredith Broussard (No. 34) [Audio podcast episode]. In Human Restoration Project. 

• A 30 minute conversation that discusses how AI can be brought into the classroom in ways that enhance and empower student learning and how students can learn to think critically about (and push back on) these technology tools. #Practical

 

Mogensen, J.F. (2023, February). “Vanderbilt Staff used AI to Email Students About the Michigan State Shooting”. Motherjones.com. 

• This short article describes an AI email incident at Vanderbilt including student reaction to AI generated message centered on the tragedy. #Practical

 

Office of Educational Technology. (2023, May). Artificial intelligence and the future of teaching and learning. [government report]

• This 71 page report features insights and recommendations by the U.S. Department of Education from May 2023. It is a useful resource in terms of learning about the impact of AI on education as well as documenting how the government was thinking about AI and teaching and learning at the time of its publication. [With thanks to student researcher Maggie for this contribution.] #Practical

 

Song, Y., Weisberg, L.R., Zhang, S., Tian, X., Boyer, K.E., & Israel, M. (2024, June). A framework for inclusive AI learning design for diverse learners. Computers and Education: Artificial Intelligence, 6, 10012. Doi: 

• In this open access academic journal article, the authors acknowledge that many of the AI Literacy frameworks they’ve seen for K-12 students don’t include inclusive learning principles or Universal Design for Learning elements. They propose their own novel framework which centers UDL in developing AI curriculum and test the framework at an AI summer camp for middle schoolers. #Practical #Philosophical

 

Stachowiak, B. (Host). (2023, February 9). ChatGPT and good intentions in higher ed (No. 452) [Audio podcast episode]. In Teaching in Higher Ed. 

• A 40 minute conversation between the host and instructional designer, Autumn Caines, in which they consider how OpenAI, the chatbot’s parent company, navigates technology ethics.  #Philosophical #Practical

 

Swauger, S. (2020, August 7). Software that monitors students during tests perpetuates inequality and violates their privacy. MIT Technology Review. 

• This op-ed written by an academic librarian explores how algorithmic test proctoring software is “a modern surveillance technology that reinforces white supremacy, sexism, ableism, and transphobia. The use of these tools is an invasion of students’ privacy, and often, a civil rights violation”. #Practical #Philosophical

 

THE podcast: How to use generative AI in your teaching and research [Audio podcast episode]. (2023, July 6). In Times Higher Education.

• A one hour conversation between three scholars who explore how they use AI in their own praxis including how these technologies can serve neurodivergent users. #Practical

 

UNSW eLearning. (2024, November 7). Supporting Universal Design for Learning using AI tools [Video]. Youtube. 

• This 3 minute video features a Special Education professor and UDL expert explaining how AI can support educators in brainstorming lessons that align with UDL principles, personalize rubrics for assessment which is one of the common challenges in implementing UDL, and assist neurodiverse learners via tutoring chatbots, learning materials summary, etc. #Practical

 

Wood, P. and Kelly, L. (2023, January 26). ‘Everybody is cheating: Why this teacher has adopted an open ChatGPT policy. NPR. 

• This short web article or 5 min audio file shares one professor’s reasoning for allowing AI into the classroom rather than trying to regulate AI generated work. #Practical

 

KNOWING: 

AI has the potential to transform education by personalizing learning and supporting equitable teaching practices, but it also carries risks of reinforcing biases and inequities. Understanding these dynamics is crucial to critically navigate and engage with AI tools, inputs, and outputs in teaching and learning.

To critically engage with AI in education, it’s important to: 

• Evaluate how AI can both support equitable learning and reinforce existing and new forms of inequity in the classroom. 
• Identify and analyze biases in AI tools and consider how they impact your teaching and/or learning experiences. 
• Approach AI in education with a critical mindset, making informed choices about how to engage with and evaluate AI-generated content. 

DOING: 

• What critical skills will students need to enter (and feel empowered to enter) into dialogue and usage of AI? Define and name those skills. Build a project that engages students in your discipline to learn these core skills.
• Hackathon - select a hot topic in your discipline that is actively being discussed right now. Use AI in groups to outline projects that help address that topic through AI. How can you engage or prompt AI to create the best project possible? What AI skills did you use? What does AI create that is useful in terms of the topic? What are the limitations? 
• What do faculty think about AI in teaching and learning? Use different data sets to research and review teaching practices around Generative AI. Data sets could include: interviews, social media, peer-reviewed articles, online blogs, etc. Try to use at least 3 data sets to inform your analysis.
• Similarly, what do students think about AI in the learning process? What are the opportunities and what are the limitations in education? Use different data sets to research and review teaching practices around Generative AI. Data sets could include: interviews, social media, peer-reviewed articles, online blogs, etc. Try to use at least 3 data sets to inform your analysis.
• Use AI to create several teaching and learning models for integrating AI into research projects. Apply these models to specific discipline discussions or topics. Interview workers, thinkers, researchers, etc. in those fields to get feedback on which projects seem most relevant, useful, or significant.