Center for Innovation in Teaching and Learning

Generative AI 

Beyond generating topic-based paper assignments, generative AI can be used to improve writing skills, create royalty free art and music, and serve as an advanced form of search in all aspects of life. One example of how unique this resource is: you can copy and paste your grocery list and ChatGPT or Bing will provide you with recipe recommendations ensuring that you maximize meals throughout the week.

The list of generative AI is ever expanding. Here are a few examples of generative AI platforms:

ChatGPT: ChatGPT is a conversational agent powered by OpenAI's GPT (Generative Pre-trained Transformer) technology. It's an artificial intelligence model designed to understand and generate human-like text based on the input it receives. ChatGPT can engage in a wide range of conversations, answer questions, provide explanations, generate creative content, and assist users with various tasks. It's trained on vast amounts of text data from the internet, allowing it to generate responses that are contextually relevant and coherent (response from ChatGPT on 02/29/2024).

Learn more:  

• What is ChatGPT? Open AI’s Chat GPT Explained 
• OpenAI blog on ChatGPT 

Copilot (Bing): Microsoft Copilot is an AI assistant developed by Microsoft. It serves as your everyday companion within the Windows ecosystem. Copilot offers a conversational chat interface, allowing you to interact naturally. It’s not limited to Windows 11; it communicates across all Microsoft applications, including Edge and Bing. Beyond conversation, Copilot excels in information retrieval, text generation, and task automation. It can summarize, explain, or rewrite text found on the web, and even create new content. Privacy controls allow users to manage its access to texts. Integrated into apps like Paint and Photos, Copilot enhances tasks and adapts to your needs (response from Copilot on 02/29/2024).

Dall-E: DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs. We’ve found that it has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. DALL·E 2 and DALL·E 3 are updated versions which generate more realistic and accurate images (OpenAI).

Microsoft Designer: Launched in late 2022, Microsoft Designer is an AI-powered design app for creating social media posts, invitations, and other visuals. It uses AI to generate unique images based on your descriptions, edit photos, and offer design inspiration. With its user-friendly interface, Designer aims to make graphic design accessible to everyone, regardless of their design skills (response from Gemini on 02/29/2024). You can learn more here.  

Gemini: Google's AI model, formerly known as Bard, has undergone a name change and is now known as Gemini. This shift reflects Google's broader strategy of integrating the AI model across various services and products, making "Gemini" the unifying brand for its entire AI ecosystem. Additionally, the name change aims to streamline branding and is considered easier to remember and pronounce compared to "Bard." Despite the name change, the core functionalities and capabilities remain the same.

 As the most advanced AI model developed by Google, Gemini offers users the ability to directly interact with Google's AI technology. Trained on vast amounts of publicly accessible data, Gemini can communicate and generate human-like text, responding to a wide range of questions and prompts. Whether you seek further information about Gemini's capabilities or wish to experience them firsthand, feel free to explore further and discover what Gemini can do for you (response from Gemini on 02/29/2024).

Midjourney: Midjourney, an independent research lab, utilizes its generative AI program to empower users to create stunning images through simple text descriptions. By understanding the connections between words and visuals, similar to DALL-E 2, Midjourney allows users to describe any scene, explore artistic styles, and refine their creations through an iterative process, offering a glimpse into the future of AI-powered creative expression (response from Gemini on 02/29/2024).

Sora: Sora is an AI model created by OpenAI that can create realistic and imaginative scenes from text instructions. Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. Learn more by visiting the Sora Capabilities page.

Teaching with Generative AI 

Welcome to an era where AI assistance has become an invaluable tool in education and the workforce. Through properly educating ourselves and our students about AI and its uses, generative AI can become more than a way to cheat, but a resource for taking concepts and ideas beyond what we can imagine. 

As we navigate the AI revolution in education, we will continue to uphold the values of integrity, fostering a culture of honesty, and nurturing an environment where genuine learning thrives providing a foundation of success for our students in their future careers.

Our pedagogical approaches will need to change to mediate the temptation of students who want to overly rely on this resource in their course work.

Here are some suggestions that you can implement in your classroom. 

• Make reflection and planning part of your course work. Encourage your students to spend time reviewing their learnings and planning their next steps as part of the learning process. Currently, artificial intelligence cannot compete with human ability in these tasks. After each lesson, set aside some time for students to discuss and digest what they've learned. Include a section for self-review and future planning in their written assignments, which will also be a part of their grades. This is something that students won't be able to accomplish effectively using AI tools, so feel free to explain this to them.

  • Example: Instead of a traditional paper assignment, which is even easier to cheat through now, assign a multimedia project and a self-reflection essay.

• Encourage students to utilize a variety of media. Instead of a traditional essay or brief written task, ask them to present their understanding via an audio recording, podcast, video presentation, speech, sketch, infographic, or a multifaceted multimedia project.

• Design tasks that relate to recent happenings or emerging discussions in the relevant domain; to challenges inherent to the local community, or to debates that have transpired within your own class. Alternatively, prompt your students to discern a correlation between the subject matter of the course and their individual experiences or understanding.

• Flipped Learning approach: Instruct students to study and understand the subject matter at home, and then put this knowledge into practice, show their understanding, and actively participate in class activities, either individually or in collaborative small groups.

• Integrate ChatGPT into your assignments. The more accustomed your students get with its advantages and disadvantages, the less likely they are to rely on it when trying to cut corners on assignments.

  • Example: to enhance your students' analytical skills, have them formulate a question for ChatGPT and analyze the generated response, highlighting its strengths and weaknesses.

• Educate your students on the safe usage of generative AI. Be aware that ChatGPT may share the personal information of account holders with third parties, such as vendors and service providers, as per their Privacy Policy. Ensure your students understand the importance of never divulging personal or sensitive data to AI chatbots. See Privacy Policy. 

  • Tennessee Tech Syllabus Statement Options 

• Ensure your students know how to correctly cite generative AI. 

  • ChatGPT outlines how it uses the information provided in its privacy policy.  

  • Cite generative AI correctly 

AI in the Classroom 

As generative AI continues to expand and make its way into industry, it is important we ensure our students are familiar with the tools and technology being created for their future fields. Listed below are a few examples of how generative AI is being used in various industries. 

Gaming and Entertainment:

• Procedural content generation for video games, including landscapes, characters, and levels.

• Generating realistic human-like faces for video game characters.

• Creating virtual worlds and environments.

Fashion and Design:

• Generating unique and innovative designs for clothing, accessories, and home decor.

• Virtual try-on for clothing and accessories, allowing customers to see how they would look.

• Creating personalized fashion recommendations based on individual preferences.

Art and Creativity:

• Generating artwork, including paintings, sculptures, and digital art.

• Assisting artists in creating new and unique pieces.

• Enhancing and transforming images and photographs.

Film and Animation:

• Creating realistic visual effects and computer-generated imagery (CGI) in movies.

• Generating virtual characters and creatures.

• Automating the animation process.

Healthcare and Medicine:

• Generating synthetic medical images for training and testing algorithms.

• Creating personalized treatment plans based on patient data.

• Assisting in drug discovery and molecule design.

Robotics and Automation:

• Generating robot motion and behavior.

• Simulating and optimizing robot tasks and movements.

• Creating virtual training environments for robots.

Advertising and Marketing:

• Generating personalized content and advertisements based on customer preferences.

• Creating virtual spokespersons and influencers.

• Designing customized user experiences and interfaces.

Music and Audio:

• Composing original music and melodies.

• Enhancing audio quality and removing noise.

• Creating virtual instruments and synthesizers.

Architecture and Interior Design:

• Generating building designs and floor plans.

• Simulating and visualizing interior design concepts.

• Assisting in urban planning and landscape design.

Data Augmentation:

• Generating synthetic data for training machine learning models.

• Increasing the size and diversity of datasets.

• Addressing privacy concerns by generating privacy-preserving synthetic data.

Please note that this list is not exhaustive, and generative AI is continually finding new applications in various industries as research and development progress.

AI in Assessments 

For assignments where generative AI is not allowed, here are a few tips for detecting the use of generative AI and helping ensure students don't use it. 

Clear Communication

• It is important to clearly communicate your expectations to your students. This should be done on the syllabus and specified for each assignment. 

• Remind your students of the university's academic integrity policy. 

Rethink Assessment Questions

• Run your assessment questions through ChatGPT.  It is important to run them a few times so you can see the various outputs students could turn in and learn the likely answers ChatGPT or other generative AI might generate. 

• Create questions that require reflecting on personal experiences or in class discussions. 

• Use multiple assignments and methods to create a history of student's writing styles to use as comparison for their personal writing abilities and voice. 

• Keep in mind, AI generated responses typically have a tone of voice that seems overly professional, grammatically exact, and detached. 

Strategies to Deter Academic Integrity Violations

Submitting assignments created or enhanced by Generative AI when not approved by the course instructor is an academic integrity violation. It is similar to paying an individual to complete your assignments, write your papers, or take your tests. Here are some tips to mitigate cheating and ensure that your students are getting the most out of their educational experience.

Or as a hallucinating AI would say, “Using AI to whip up or boost your work when it's not really asked for or okayed is pretty much cheating. It's like paying someone else to pen your essay, sit your exam, or get your work done. Here are a few pointers on keeping cheating in check in this age of AI magic.”

• Implement Consistent and Clear Communication in your course. The best way to deter an academic integrity violation is to address the elephant in the room. 

• Discuss the policy on academic integrity, discuss when and when not to use AI for each of your assignments throughout the semester (policy 217). 

• Give specific examples of how generative AI can help and hinder their academic progress.

• Be specific and real with the students about your concern for their academic progress if they use AI to cheat.

• Build relationships with your students by ensuring them that you want what they want, to be successful in their future careers. 

• Ask pointed questions through out the semester: Why is integrity important? Are you getting the most out of this class? Are you ensuring that you are prepared for your future career?

• Run your assignments through generative AI and discuss the results with your students. Generative AI does not produce the same results each time, but the results will be similar. 

• Learn to recognize some key components to AI produced text.

  • Often very formal (unless asked to use a different tone) and systematic utilizing perfect grammar and sentence compensation.

  • Consider students previous writing and compare for a dramatic difference.

  • Ask students to handwrite every other assignment. Research shows some improvement in mixing both methods of writing.

  • Often very verbose using multiple paragraphs to illustrate a point. Additionally, AI produced text will repeat, though phrased differently throughout the text.

  • Cite made up sources, though this is quickly changing with access to tools like Orchid, Research Gate, and Google Scholar. (Paid AI services are nearly perfect in citing sources)

  • Does not follow specific assignment guidelines by using sources not discussed in class.

  • Perfect use of grammar, editing, and the tone of the work is voiceless and impersonal.

  • General and predictable responses. For example, Some people believe that X is better than Y. However, there are well established foundations for X and Y with strong supporters of Y over X. 

• AI hallucination is when factual errors are written to look plausible but are factually incorrect. These happens often when referencing data from a specific “Lens”, but does not take into account the entire picture of events. 

Resources

The CITL Team provides just-in-time training and presentations to faculty and departments across campus. View presentation slides from a recent presentation session below. 

Presentation 

Online Courses and Tutorials:

• Fast.ai's "Practical Deep Learning for Coders": A practical course that covers various deep learning techniques, including GANs. Website: https://course.fast.ai/
• Stanford University's CS231n: "Deep Learning for Computer Vision": A renowned course that covers deep learning topics, including GANs. Website: http://cs231n.stanford.edu/

Books:

• Generative Deep Learning by David Foster: This book provides a comprehensive introduction to generative models, including GANs, and their applications. Website: https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/
• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Although not focused solely on generative AI, this book provides a detailed understanding of deep learning, including GANs. Website: http://www.deeplearningbook.org/

Research Papers and Publications:

• "Generative Adversarial Networks" by Ian Goodfellow et al.: The original research paper that introduced GANs. Paper: https://arxiv.org/abs/1406.2661
• "Progressive Growing of GANs for Improved Quality, Stability, and Variation" by Tero Karras et al.: A significant advancement in GANs that enables the generation of high-quality images. Paper: https://arxiv.org/abs/1710.10196
• "CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks" by Jun-Yan Zhu et al.: Introduces the CycleGAN model for image-to-image translation without paired training data. Paper: https://arxiv.org/abs/1703.10593

Online Platforms and Communities:

• GitHub: Explore GitHub repositories for open-source implementations of generative AI models and projects. Website: https://github.com/
  Papers with Code: Provides a collection of research papers, code implementations, and evaluation results for various topics, including generative models. Website: https://paperswithcode.com/
• Reddit: Join the subreddit r/MachineLearning or r/generativeai to engage in discussions, ask questions, and learn from the community. Website:
  • • https://www.reddit.com/r/MachineLearning/
    • https://www.reddit.com/r/generativeai/