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Resources for use in teaching

 

Gen AI has the potential to address some of the biggest challenges in education today. Nonetheless, as educators and students, we face a new frontier as we navigate a world in which the distinction between content generated by AI and humans is rapidly blurring. 

To support conversations about how to include generative AI in your teaching, you are invited to participate in a new "Teaching and Learning with Generative AI" self-paced course designed by university experts to support Generative AI usage by faculty and staff. Click here to Self-enroll in the Canvas course now. This course includes foundational information about generative AI as well as practical ideas for implementation into courses. It is not necessary to complete the entire course. Each module can be completed individually to give you immediate access to the information you need. 

Whether you plan to incorporate Gen AI into your course or avoid it, explain your policies in the course syllabus. Sample language is here. 

The ASU Library Guide on Citing Generative AI Models shows examples of how to cite usage of Generative AI in the most common academic styles.

As these conversations take place, this website will be continually updated with resources for the ASU community. Our goal is to approach generative AI in a way that embraces its benefits while providing recommendations and guidelines on how to prevent its misuse. 

If you have feedback about these resources or others you would like to see, provide it here. 

What is Generative AI?

Generative AI is a broad term that refers to a type of artificial intelligence (AI) application that is designed to use a variety of machine learning algorithms to create new content (text, images, video, music, artwork, synthetic data, etc.) based on user input that was not explicitly programmed into the AI application. Generative AI systems are “trained” by using complex algorithms to learn from an existing large corpus of datasets (often consisting of millions of examples) and to analyze patterns, rules and statistical structures from the sample data to be used in generating new content that is similar in style and characteristics to the original training datasets.

There are many different types of generative AI applications and models, and while they might share a similar high-level definition of creating new content based on large sets of training data, they often represent completely different machine learning models, underlying neural network technologies and approaches to generating new content. You may have heard of recent popular examples of generative AI such as ChatGPT, DALL-E or StableDiffusion. While these are all examples of generative AI applications, DALL-E and StableDiffusion are text-to-image generators that use a type of neural network called generative adversarial network (GAN) to create a new image based on two systems (a generator and discriminator) surfacing an image of best fit based on the input parameters. By contrast, ChatGPT builds a transformer-based language model from a generative pre-trained transformer (GPT) to process sequential data, such as language, and then calculate the most probable words that will follow in a sequenced response.

Generative AI and the broader classification of artificial intelligence have been identified by UNESCO as having the potential to "address some of the biggest challenges in education today, innovate teaching and learning practices, and accelerate progress" as well as calling for a human-centered approach to respond to inequalities

 

Additional FAQs

Please review these additional Generative AI FAQs designed to guide faculty, staff and students.

 

Join the Generative AI Teaching Conversations

The Office of the University Provost invites you to watch the Generative AI Teaching Conversation recordings linked below. In each 30-minute, podcast-style webinar, ASU expert panelists discussed an aspect of the use of generative AI in higher education with an audience Q&A

What are generative AI tools and how are they being used at ASU?

Learn what generative AI tools are and how they are being used. How can the ASU community stay informed and up to date on the latest developments in this field? How can we ensure that we are using these tools to their fullest potential while also minimizing the risks associated with their use?

 

View recording

What generative AI tools are available?

What generative AI tools are available for various types of content creation, including text, visual and audio? Learn the capabilities of what's out there and which tools may require specific technical expertise to use effectively.

View recording

How can ASU foster a positive culture around using generative AI?

How can ASU foster a positive culture around incorporating generative AI into teaching, learning, research and creative activity? This will require an ongoing commitment to learning, collaboration and responsible use. How can we approach this technology with an open mind and a commitment to ethical use? How can we unlock its full potential and make new discoveries in a wide range of fields?

View recording

In what ways can generative AI impact academic integrity?

How do we cultivate a positive outlook on the potential learning advancements that generative AI can provide? By reframing the conversation from cheating to focusing on how AI is already used in education and how it can effectively improve learning, we can promote a more positive and productive dialogue about this technology in the education community.

View recording

 

 

 

In what ways can generative AI impact critical thinking, research and writing?

What are the effects of generative AI tools on critical thinking, research and writing? How can we approach these tools with caution and responsibility, and carefully consider their potential impact on research and writing outcomes?

View recording

 

What are the aspects of social justice in the generative AI conversation?

How can ASU incorporate social justice considerations into the development and use of AI (training, data, discrimination, different types of understanding that are not included in AI tools)? The social justice aspect is crucial for ensuring that this technology is used in ways that are fair, equitable and inclusive for all. How can we recognize and address the potential for bias and discrimination? How can we incorporate diverse perspectives, and promote transparency and accountability? Join the conversation that will discuss how we can create AI systems that are more just and reflective of our values as a society.

View recording

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