Last week in a Zoom call, a great question was asked…what is the best way to organize resources on AI by Centers for Teaching in higher education?
It is an important question in that GenAI is being adopted at rates not seen since 20 years ago when one looked at the rate of adoption of online learning management systems, as this graphic by Phil Hill illustrated:
Almost overnight, LMS went from small homebuilt solutions to mega-corporate solutions like Blackboard or Canvas. Now we seem to be seeing similar levels of both adoption and corporate investment in GenAI. Like was done two decades ago with learning management systems, as generative AI continues to reshape higher education, it’s crucial for academic institutions to develop comprehensive strategies for resource organization and implementation.
One current suggestion on curating resources from Educause is from Barnard College. It breaks AI literacy into the following four levels:
- Understand AI
- Use and Apply AI
- Analyze and Evaluate AI
- Create AI
Each level includes core competencies, key concepts, and reflection questions, providing a holistic view of what AI literacy entails at different stages. The framework acknowledges that AI is a broad field and allows for non-linear progression through the levels. This framework maintains a neutral stance on AI use, emphasizing that literacy can lead to informed decisions about whether or not to use AI, and it suggests a shift towards focusing on higher levels of the pyramid (levels 2 and 3) as basic literacy grows, while also addressing the need to engage those who have never used AI.
Nice, and worth checking out!
However, in this Zoom call, Jeff suggested a different take that I like better – one developed by Martha Burtis for her institution, Plymouth State University. I first met Martha fifteen years ago when she worked at the University of Mary Washington. With her Open CoLab and AI Challenge, she is now providing faculty with self-guided modules to understand AI and then explore it along three dimensions: Teaching With AI, Teaching Against AI, and Teaching About AI.
- Fundamentals of Generative AI: This section provides an overview of how generative AI tools work, their capabilities, and potential future developments.
- Teaching With Generative AI: This topic explores practical applications of generative AI in the classroom, including using AI for assignment design, student projects, and rethinking assessment methods.
- Teaching Against Generative AI: This section takes a critical perspective on the use of AI in education, addressing ethical concerns, potential biases, environmental impact, and issues related to cheating and plagiarism.
- Teaching About Generative AI: The final topic emphasizes the importance of educating students about AI tools, their cultural impact, and how to approach an AI-influenced future.
Her module is designed to be flexible, allowing faculty to focus on the components most relevant to their pedagogical needs and values. Martha encourages a balanced approach, suggesting that when using AI in the classroom, educators should not only teach how to use the tools but also engage students in discussions about the technology’s broader implications and meaning in society.
Check out her site…a ton of resources nicely curated into specific themes.
In fact, it raised the idea for me that there is a matrix out there to push AI curation into all the facets of academic life:
And perhaps I should add an interest area for students as well, because as we all know, we can learn from our students!
As generative AI continues to reshape higher education, it’s crucial for academic institutions to develop comprehensive strategies for resource organization and implementation. The proposed matrix offers a structured approach to curating AI resources across various facets of academic life. By addressing AI’s impact on teaching, research, service, and administration, institutions can foster a well-rounded understanding and application of this transformative technology. Moving forward, it will be essential for centers of teaching and learning to collaborate, share best practices, and continuously adapt their approaches to ensure that AI enhances rather than disrupts the educational experience. As we navigate this rapidly evolving landscape, maintaining a balance between embracing AI’s potential and preserving the core values of education will be key to success in the AI-augmented academic world.
{Graphics: Phil Hill, Educause, Martha Burtis, Watwood, DALL-E}