On behalf of the LTC, thank you to all the participants of our Use Cases and Opportunities for AI! If you were unable to attend the session, a summary is below:

Key Takeaways:

1. Generative AI has numerous applications across higher education; a small excerpt of examples includes summarizing texts, creating study guides, providing personalized tutoring, and generating student feedback. 
2. Prompts are but a starting point. Be as specific as possible in your query and customize it as needed to suit your objective. Modify and iterate! 
3. Optimal utilization of generative AI involves hands-on exploration. Given the highly individualized nature of job requirements and responsibilities, you are in the best position to discern how AI can be effectively harnessed for your specific role.

Supplemental Resources:

1. Presentation Slide Deck
2. Workshop Recording
3. AI for Education – Prompt Library
4. Ethan Mollick on Encoding Human Expertise 

Save the Date! The LTC is hosting “Navigating AI: Panel Discussion on Implications for Higher Education” on November 15th at 3pm – more information available here.

Thank you to all the attendees of our workshop, “Ethics and Limitations of AI”! If you were unable to attend, we have provided a summary below:

Key Takeaways:

• Responsible generative AI literacy inside and out of the classroom includes a consideration of the ethical concerns. With a rush of generative AI platforms’ products, companies are looking to monetize, with minor regard for public privacy, safety, or access.
• “Degenerative” AI is a real problem. In a world where volumes of content can be produced in seconds, it will be difficult to discern human-creation from AI-creation, and with it, a loss of the creative process. An implicit underlying thesis of generative AI is that one can only derive enjoyment from the consumption of content, not the production.
• Federal legislative action has slowly begun; both the White House and Congress have begun work on guardrails for the responsible development of AI, with a primary focus on safety, security and trust. 
• The majority of ethical concerns surrounding AI can be categorized into a four part framework: privacy, equity, transparency, and accountability.

Supplemental Resources:

1. Presentation Slide Deck
2. Workshop Recording
3. White House AI Response 
4. Degenerative AI – Mike Trigg

Save the Date! The LTC is hosting “Navigating AI: Panel Discussion on Implications for Higher Education” on November 15th at 3pm – open to all! – more information available here.

The LTC would like to thank all the attendees of our Incorporating AI in Assessments workshops! In case you were unable to attend our sessions, we have provided a summary below:

Key Takeaways:

1. Utilizing AI in building assessments can save time and effort, allowing instructors to focus on other aspects of teaching and learning.
2. Use AI to generate a wide range of responses based on different prompts and criteria, allowing instructors to test potential responses or create more varied and engaging assessments that can be tailored to the needs of individual students.
3. AI can identify patterns, themes, and other characteristics in students’ writing that can provide insights into their writing style, language proficiency, and overall academic performance.
4. Instructors can customize assignments through AI by including learning objectives, creating a rubric, providing resources, and reviewing and revising the assignment.
5. Harness the creativity of AI – try it for discussion topics, essay topics, and quiz prompts that are personalized to students’ interests and backgrounds.

Supplemental Resources

1. Presentation Slide Deck
2. Workshop Recording
3. Prompt Examples
4. AI For Education – Prompt Library 

Additional LTC Workshops

Use Cases and Opportunities for AI

• Wednesday, October 18; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

 Ethics and Limitations of AI

• Wednesday, October 25; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

For full descriptions of each workshop, please visit our previous post.

Thank you for attending our AI Detection and Awareness workshops! We now offer a Turnitin AI detection tool for all campus instructors. Please note that while other options exist, our support will be focused on Turnitin.

Key Takeaways

Clearly Define AI Use for Your Students

• It’s essential to communicate your expectations regarding AI usage with your students.
• Recognize that the definition of “cheating” can vary among instructors and courses.
• Consider AI as a potential educational resource rather than a threat. For example, consider using AI as a possible tutor or study aid.
• Make sure to incorporate accessible AI policies within your syllabus.

Adapting Assignments for an AI-Integrated Environment

• Encourage students to reflect personally as they interact with AI tools.
• Request students to provide documentary artifacts such as outlines, rough drafts, and bibliographies as part of their assignments.

Utilizing Turnitin in Canvas for AI Writing Detection

• Turnitin, integrated into Canvas, offers a comprehensive AI Writing Detection feature.
• Emphasize that all submissions through Turnitin undergo thorough AI content scanning.
• To access the AI indicator, refer to the Similarity Report (Instructions on how to find the Similarity Report can be found here):
  • The AI indicator is not visible to students.
  • AI detection only works well with lengthy prose.
  • It may not provide reliable results for bullet points, brief responses, short essays, or poetry.
  • Currently, AI detection exclusively supports English text.
• Remember that AI detection scores are not absolute; any concerning findings should be discussed with students.

AI Detection Technology Progress

• Be aware that AI detection technology continually advances to catch up with generative models.
• Follow the LTC Blog for updates!

Supplemental Resources: 

1. Presentation Slide Deck 
2. Recording of the Second Session
3. UWW AI Syllabus Language Resource
4. UWS Chapter 14 – Student Academic Disciplinary Procedures

11/6/23 Update

 UW-System OPID Webinar Series: Safeguarding Our Students, Instructors, and Universities: Privacy, Security, Copyright, and Generative AI

As generative AI expands in capabilities, questions surrounding personal privacy, copyright, and intellectual property have risen in the academic community. The University of Wisconsin System recently hosted a panel discussion bringing together three system experts to discuss these issues. Timestamps from the webinar that discuss the highlighted points are included in parentheses. 

Key Takeaways:

• The best approach to generative AI use in the classroom is communication of expectations. A syllabus policy that clearly describes appropriate use, if permitted, will increase responsible integration and decrease confusion. (17:12, 37:52)

• Homework or other assignments, once completed and submitted to an instructor for credit or grading, is protected under FERPA. In the context of using open source generative AI detectors, instructors should be cautious of submitting student work where privacy violations may occur. Students should be given an opportunity to consent to their work being submitted to public detection software. (14:10, 17:55) 

• UW System is currently working to understand how generative AI intersects with our current practices and tools. They are working on reviewing policies, creating a guidance document, and researching potential enterprise solutions. (39:05)

• Instructors can require students to sign up for accounts with ChatGPT. (7:51)

Additional LTC Workshops

Incorporating AI into your Assessments 

• Wednesday, September 27; 3:15 pm – 4:30 pm McGraw 19A / Hybrid 
• Friday, October 13; 10:15 am – 11:30 am McGraw 19A / Hybrid 

Use Cases and Opportunities for AI

• Wednesday, October 18; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

 Ethics and Limitations of AI

• Wednesday, October 25; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

For full descriptions of each workshop, please visit our previous post. 

Thank you to all who attended our Introduction to Artificial Generative Intelligence workshops! In case you missed our introductory sessions, below is a review of the critical information and resources: 

Key Takeaways

1. AGI is a handy tool ​with the potential for both constructive and destructive applications ​
   • “Pre-trained Generative Transformer” – a predictive language model that is modeled after how we speak that can create media at scale
   • The tool is morally neutral; how we choose to employ it is the crux of the issue
     • AGI platform companies are rushing to monetize their products; expect AGI to continue to spread throughout daily life as the tools continue to be refined and improved (and eventually moved behind paywalls) 

2. AGI has a place in the learning process through thoughtful integration ​
   • Education and regulation​ will help combat the misuse of AGI
     • Communicate course expectations and provide context to students
   • Detection option on campus: Turnitin (Canvas Integration) 
   • Privacy and security risks – any information that should not be publicly shared on the internet should not entered into any AGI platform
   • AGI offers opportunities to improve the student (and instructor!) experience
     • Equity, accessibility, and creativity

Supplemental Resources: 

1. Presentation Slide Deck 
2. Recording of the First Session
3. UWW Syllabus Language Resource
4. AI Citation Guide

Additional LTC Workshops

Awareness and Detection of AI

• Wednesday, September 13; 3:15 pm – 4:30 pm McGraw 19A / Hybrid 
• Friday, September 15; 10:15 – 11:30 am McGraw 19A / Hybrid 
• Tuesday, September 19, 8:15 am – 9:30 am McGraw 19A / Hybrid 

Incorporating AI into your Assessments 

• Wednesday, September 27; 3:15 pm – 4:30 pm McGraw 19A / Hybrid 
• Friday, October 13; 10:15 am – 11:30 am McGraw 19A / Hybrid 

Use Cases and Opportunities for AI

• Wednesday, October 18; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

 Ethics and Limitations of AI

• Wednesday, October 25; 3:15 pm – 4:30 pm McGraw 19A / Hybrid

For full descriptions of each workshop, please visit our previous post.