Generative AI for Teaching and Learning

Blackboard Ultra: What's New?

Generative AI offers exciting possibilities to transform teaching and learning but raises challenges around academic integrity, privacy, and bias. Here, you'll find resources on what generative AI is, how it works, and how to write effective prompts to unleash the power of AI tools. You can also find insights on how to leverage AI tools to create personalized learning experiences, enhance student engagement, and support faculty development. Explore practical applications, discover best practices, find training information, and join us in shaping the future of teaching and learning with generative AI at WPU.

For further information about the use of Generative AI in teaching and learning, please visit the Center for Teaching Excellence Artificial Intelligence: Instructional Recommendations page.

What's New?

Introducing the WPU Blackboard Support Assistant! It is a tailored version of ChatGPT with extra knowledge about WPU’s policy and practices of Blackboard usage and local help files. Leveraging generative AI technology, this custom GPT offers technical support tailored specifically for the needs of WPU faculty and student Blackboard users. Click the link below to experience instant assistance at your fingertips! Please note that some of the live links provided by this GPT is intentionally disabled by OpenAI for security reasons.

Blackboard Suppport Assistant Blackboard Support Assistant

  • Generative AI Basics

    There are many definitions available. Here is one that is generated by ChatGPT 4-o:

    Generative AI refers to artificial intelligence technologies that create new content, such as text, images, audio, and video, by learning patterns from existing data. These systems utilize advanced machine learning models, including deep learning techniques like neural networks, to generate outputs that are often indistinguishable from those created by humans. Generative AI can be applied in various fields.

    The key aspects and concepts of Generative AI include:
    • Creating new content: This differentiates it from traditional AI that primarily makes predictions or classifications based on existing data.
    • Learning from existing data: Generative AI models are trained on massive datasets to understand patterns and relationships, which they then use to create new, similar content.
    • Prompt-based generation: Many generative AI systems work by responding to prompts or queries from users to guide the content creation process.
    • Underlying technologies: Deep learning techniques like advanced neural networks are crucial for the complex tasks involved.
    • Human-like outputs: The quality of generated content has reached a point where it often seems indistinguishable from human-made content.
    • Broad applications: The potential of generative AI extends across a diverse range of fields, showcasing its versatility.

      • Introduction to AI for Teachers and Students by Ethan Mollick and Lilach Mollick of Wharton School. This video is Part 1 of a five-part Practical AI for Instructors and Students course. The course explores how the Large Language models work, how to use generative AI to make teaching easier and more effective, and how students can use GenAI to improve their learning. Part 1 provides a short overview of how Large Language models (LLMs) work.

    Large Language Models (LLMs) like OpenAI’s GPT-4 are designed to be user-friendly and accessible to people without technical expertise. While it’s not necessary to understand the technical details of using LLMs, having some basic knowledge brings several benefits:

    • Help you communicate more effectively with them, allowing you to write better prompts that yield better results.
    • Help you better leverage their abilities to assist you in various tasks, from data analysis to creative writing.
    • Enable you to evaluate the information provided critically, discerning between well-supported facts and less reliable outputs.

    The core of Generative AI is the Large Language Model, and the Transformer is the architecture that enables the efficient training and functioning of these models. Below are a few excellent introductions to LLM and the Transformer for people without technical expertise:

    • A Jargon-Free Explanation of How AI Large Language Models Work by Timothy B. Lee and Sean Trott - This article is one of the best non-technical explanations of how Large Language Models (LLMs) and AI-powered chatbots such as ChatGPT work. It makes complex concepts more accessible and sheds light on the fascinating field of machine learning and language processing.
    • What Is ChatGPT Doing … and Why Does It Work? By Stephen Wolfram - Stephen Wolfram is a renowned scientist. This article explores how ChatGPT functions and why it can generate human-like text. Wolfram’s clear and engaging explanation covers neural nets, data training, probabilistic approach, word generation process, temperature parameter, and some engineering details.
    • But what is a GPT? Visual intro to transformers by Grant Sanderson (3Blue1Brown) - This video is a little more technical than Timothy Lee’s article. However, using beautiful visual animation, it explains GPT and Transformers in a remarkably intuitive and engaging way.



  • Generative AI Tools for Faculty

    Licensed AI Tools at WPUNJ

    The following generative AI tools are accessible to all WPU employees and are licensed under WPU’s enterprise agreements with Microsoft, Zoom, Adobe, and Anthology. Ensuring privacy and data security is crucial for the responsible use of AI tools. One significant benefit of these licensed AI tools is their provision of commercial data protection, which helps safeguard privacy and maintain data security while using Generative AI. Despite this protection, it's still important to be cautious when sharing personal or organizational information.

    Microsoft uses the term "Copilot" to refer to AI-based assistance that appears in different forms throughout Microsoft's products and services. Copilot’s AI-powered features include text, voice, and image capabilities in online AI chatbot, document and web page summarization, image creation using Dall-E 3, web grounding/connection to the Internet, etc. WPU is currently only licensed for Microsoft Copilot which can be accessed at copilot.miscrosoft.com, via Edge sidebar (click the Copilot logo CoPilot logo), and through the Copilot mobile app. Currently, Copilot+PCs are not yet available on WPU-owned computers, and WPU doesn’t have a license for Copilot for Microsoft 365. Make sure you sign in using your WPU credentials. You will know you are successfully signed in with your WPU account when you see credentials tag next to your credentials in the top right corner of your screen.

    Zoom AI Companion is a generative AI digital assistant integrated into the Zoom platform to enhance productivity and collaboration. Here are the key features and aspects of Zoom AI Companion:

    • Meeting Summaries: With this feature enabled, hosts will be able to generate a summary and action items that were discussed in their meetings. The summary will be sent only to the host by way of an email after the meeting has concluded. Important Note: The AI Companion summary must be fully vetted and edited (if necessary), before being shared with others.
    • Smart Recording: Once enabled, your meeting will be recorded and saved to the cloud. You can then review your cloud recordings faster through highlights, smart chapters, summaries, next steps, and get analytics on key meeting and conversation factors.

    Firefly is accessible as a standalone web app at firefly.adobe.com and is also integrated into Adobe's flagship apps like Photoshop, Illustrator, and Premiere Pro. Adobe Firefly is a family of generative AI models that power generative AI features across Adobe’s creative apps, including text to image, generative fill, text to vector graphic, generative remove, generative recolor, and video editing.

    Blackboard AI Assistant is available within the Blackboard Learning Management System. Blackboard’s AI Assistant uses advanced AI to help faculty design the course, create learning models, and generate discussion, assignment prompts, rubrics, question banks, and tests.


    Popular Generative AI Tools


    Tool Description Web Grounded* Pricing
    Microsoft Copilot Microsoft Copilot in Edge sidebar (Click the Copilot icon to open it in the Edge browser) offers AI-powered assistance, webpage summarization, content-specific queries, image analysis, and composition tools. It integrates with browsing, providing real-time information and task support without switching tabs or windows.

    Microsoft Copilot in the Edge sidebar offers AI-powered assistance, webpage summarization, content-specific queries, image analysis, and composition tools. It integrates with browsing, providing real-time information and task support without switching tabs or windows.    		 Yes Yes Free to WPU employees and students
    Gemini (Google) A generative AI chatbot. Its main features include advanced AI-driven search capabilities, personalized content recommendations, cross-platform integration, voice assistant support, and user experience across Google services. Yes Yes Free
    Paid subscription: Gemini 1.5 Pro Gemini 1.5 Flash
    ChatGPT (OpenAI) Natural language conversations, content creation, task assistance, information retrieval, and problem-solving. It features web browsing, voice interactions, data analysis, multilingual support, and image capabilities. Yes No Free limited tier
    ChatGPT Plus $20/month
    Claude (Anthropic) A versatile chatbot with natural language understanding, content generation, and image analysis capabilities. Offers ethical AI, large context windows, and multimodal processing for tasks like summarization, coding, and creative writing. Yes No Free tier
    Claude Pro $20/month
    Perplexity (Perplexity AI) An AI chatbot-powered research and conversational search engine with real-time web sourcing, natural language queries, follow-up questions, and content generation. It also features voice search and data analysis. Provides linked citation. Yes Yes Free tier
    Perplexity Pro $20/month

    * The term “web-grounded” refers to an AI tool that can access and retrieve real-time information from the web to generate up-to-date responses based on current events and available online data.


    Tool Description Pricing
    DALL-E (OpenAI) OpenAI's text-to-image models use generative AI to generate digital images from prompts. DALL-E 3 is the latest version. It is integrated with ChatGPT, and accessing DALL-E requires a ChatGPT Plus subscription. $20/month.
    WPU employees and students can access DALL-E 3 through Microsoft Copilot free of charge.
    Firefly (Adobe) A generative machine learning model to create, ideate, and edit images through text prompts. It powers AI features in Adobe's flagship apps like Photoshop, Illustrator, and Premiere Pro. Firefly is included as part of Adobe Creative Cloud, free to WPU employees and students.
    Midjourney An AI-powered image generation service that creates artwork from textual descriptions. Basic Plan: $10
    Standard Plan: $30
    Pro Plan: $60
    Mega Plan $120
    Stable Diffusion (Stability AI) Similar to DALL-E and Midjourney, Stable Diffusion transforms text prompts into visually rich and often creative images. Basic Plan: $27/month

    Tool Description Pricing
    Synthesia Allows you to turn text into video with AI-generated speakers and voices. Known for creating AI-generated video content with virtual avatars. Free Plan: Includes 36 minutes of video/year and 6 AI avatars.
    Starter Plan: $22/month.
    InVideo Good for generating professional-quality videos from text prompts. Key features include AI-powered scriptwriting, video generation, text-to-speech, and video summarization. Free plan: Includes 10 minutes/week of AI generation, 10 GB storage, and 4 exports/week with InVideo branding.
    Plus Plan: $20/month.
    Runway Good for experimenting with generative AI in video creation. It offers advanced AI-powered editing features. Free Plan: Includes 125 one-time credits and limited features.
    Standard Plan: $12/user/month.
    Descript A script-based video editing tool that simplifies the video creation process by allowing you to edit videos as easily as editing text. Hobbyist Plan: $12/month.
    Creator Plan: $24/month.

    Avatar-based AI platforms use artificial intelligence to create digital representations of people, often for use in virtual environments, videos, or customer interactions. Avatar-based AI platforms can transform education by making learning more personalized, interactive, and accessible.


    Tool Description Pricing
    ElevenLabs Excels at text-to-speech synthesis and in creating high-quality, natural-sounding, realistic, and expressive voices. It also offers voice cloning and multi-lingual support. It emphasizes quality, customization, and ease of use. Free Plan: Includes 10,000 characters and three custom voices every month.
    Basic Plan: $19/month.
    Descript While primarily known as a script-based video editing tool, Descript also offers efficient AI-powered audio editing capabilities, text-to-audio (T2A), audio-to-text (A2T), screen recording, and clip creation. Free plan: Basic transcription, collaboration features, and a limited number of credits for editing.
    Creator Plan: $12/month.
    Murf.ai Consistently ranked as a top choice for its flexibility and wide range of use cases, Murf.ai is popular among content creators for its user-friendly interface and diverse voice options. Free Plan: 2 projects, 10 mins of Voice Generation, 3 Editors, 5 Viewers.
    Creator Plan: $23/user/month.
    LOVO (Genny) This platform stands out for its multichannel content creation capabilities. It offers over 500 voices in 142+ languages and accents and features like emotion control, speed adjustment, and an integrated video editor. Free Plan: First 14 Days Free Trial of Pro, unlimited Sharing.
    BAsic Plan: $24/month.
  • Prompt Engeering

    Prompt engineering involves crafting specific instructions or questions to guide a computer program in generating desired content effectively. Within the world of generative artificial intelligence (AI), prompt engineering allows users to leverage AI models – often referred to as large language models (LLMs) and the natural language processing (NLP, or the instructions that allow computers to interact with humans) capabilities of those models to generate customized content that is designed according to the specifics of the prompt.

    Key AI Prompt Elements

    The quality of the AI's response and output is largely determined by the prompt. Creating effective AI prompts involves some key elements. An effective AI prompt can contain any or all of the following elements (examples are taken from the full prompt attached below):

    1. Role/Persona: Define the role or persona the AI should assume, such as an expert in a specific field or a particular character. This increases the likelihood of more accurate information.
      Example: “Act as/You are a Spanish language instructor for an intermediate-level adult learner preparing for a trip to Spain.”
    2. Goal/Objective: Clearly state the purpose or desired outcome of the prompt.
      Example: “Create a lesson plan focusing on improving students’ practical conversation skills for travel situations.”
    3. Instructions/Task: Provide specific, clear directions on what you want the AI to do, using action verbs like "Write," "Summarize," or "Create".
      Example: “Create a lesson plan focusing on improving students’ practical conversation skills for travel situations.”
    4. Context: Offer relevant background information, situation details, or assumptions to frame the task.
      Example: “You are a Spanish language instructor for an intermediate-level adult learner preparing for a trip to Spain.”
    5. Audience: Specify the target audience's characteristics, such as age, education level, or interests.
      Example: “Act as/You are a Spanish language instructor for an intermediate-level adult learner preparing for a trip to Spain.”
    6. Output Format: Define the desired structure, length, or style of the response (e.g., bullet points, paragraph form, specific word count).
      Example: “Present this information in a clear, organized manner using headings, bullet points, and numbered lists where appropriate.”
    7. Step-by-step instructions: For complex tasks, break them down into smaller steps.
      Example: In the sample prompt below, the lesson plan is broken down into clear sections.
    8. When helpful, provide sample inputs and outputs to guide the AI's response. Examples can lead to more accurate, relevant, and creative AI responses.
      Example: You can attach a file demonstrating how “headings, bullet points, and numbered lists” should be organized in the lesson plan.
    9. Constraints: Set any limitations or specific requirements, such as word count, time period, or topics to avoid.
      Example: “Limit your total response to approximately 700 words.”
    10. Style and Tone: Specify the desired writing style, language level, and tone (e.g., formal, conversational, technical).
      Example: “Use a friendly and encouraging tone throughout the lesson.”
    11. Refinement Instructions: Include guidance on how to improve or iterate on the initial response if needed.
      Example: “If you need any clarification about the learner's specific goals or travel plans before creating this lesson, please ask." Follow-up questions: The instructor is prompted to ask for clarification if needed and to suggest follow-up activities.

    Here is a full Spanish language instructor prompt that incorporates the elements listed above. And I asked Claude to help me generate this prompt:

    Act as a Spanish language instructor for an intermediate-level adult learner preparing for a trip to Spain. Create a lesson plan focusing on practical conversation skills for travel situations. Include the following elements:

    1. Vocabulary: Provide a list of 15-20 essential Spanish words and phrases related to transportation, accommodation, and ordering food.
    2. Grammar focus: Explain the use of the near future tense ("ir a + infinitive") for making plans, with 3 example sentences.
    3. Dialogue practice: Write a short dialogue (6-8 exchanges) between a traveler and a hotel receptionist, incorporating the vocabulary and grammar point above.
    4. Cultural note: Briefly describe one important cultural custom or etiquette rule for travelers in Spain (100 words max).
    5. Interactive exercise: Design a fill-in-the-blank exercise using the dialogue, leaving out key vocabulary or grammar elements for the learner to complete.

    Present this information in a clear, organized manner using headings, bullet points, and numbered lists where appropriate. Assume the learner has a solid grasp of basic Spanish grammar and vocabulary but needs practice with real-world applications.

    • Use a friendly and encouraging tone throughout the lesson. Include pronunciation tips where relevant, using phonetic spelling in brackets for potentially challenging words.
    • Limit your total response to approximately 700 words.
    • After presenting the lesson plan, suggest 2-3 follow-up activities the learner could do to reinforce these skills.
    • If you need any clarification about the learner's specific goals or travel plans before creating this lesson, please ask.

    Prompt Engineering Methods

    Listed below are a few notable prompt engineering methods for effective prompts:

    1. Dave Birss' C.R.E.A.T.E. Framework: This method breaks down prompt creation into six components:
      • Character: Define the role the AI should assume. E.g. “You are an upbeat and practical AI tutor with high expectations …”
      • Request: Clearly state what we need AI to do (the task or information needed). E.g. “I want you to …”
      • Examples: Provide samples to guide the AI's output.
      • Additions: Refine the task with additional details or perspectives
      • Type of output: Specify the desired format or structure. E.g. “summarize the text in bullet points.”
      • Extras: Include any additional relevant information, including reference text.
    2. The Structured Approach by Lance Cummings:
      This method follows a specific formula:
      • Start with a role and goal
      • Provide context and background information
      • Specify the task or question
      • Define the output format
      • Include any additional constraints or requirements
    3. The Rhetorical Approach by Sébastien Bauer
      This method focuses on describing the main claim and rhetorical situation, including:
      • Audience: Who will read or use the output?
      • Context: Where and how will the information be consumed?
      • Purpose: What is the goal of the communication?
      • The Rhetorical Approach by Sébastien Bauer:

    • The Perfect Prompt: A Prompt Engineering Cheat Sheet This comprehensive cheat sheet, created by Maximilian Vogel and published in The Generator, serves as a quick-reference guide for various aspects of prompt engineering. It is designed to be a detailed resource for both beginners and seasoned users.
    • OpenAI’s Prompt Engineering Guide This guide provides comprehensive documentation on prompt engineering, covering various techniques and best practices for crafting effective prompts. It is an excellent starting point for anyone looking to understand the fundamentals of prompt engineering.
    • GitHub’s General Guidelines for AI Prompts The repository "awesome-gpt-prompt-engineering" on GitHub is a curated list of resources and examples for prompt engineering, providing a community-driven approach to learning.
    • Top Tools for Prompt Engineering 2024: Unlock Creativity! Introduces top prompt engineering tools represent various applications, from marketplaces and models to development frameworks and optimization platforms.
    • Prompt Library from Anthropic Anthropic's Prompt Library is a valuable resource for obtaining task-specific, optimized prompts. It is particularly useful for those looking to apply prompt engineering techniques to specific use cases.

  • Generative AI in Teaching

    This section aims to equip faculty with the knowledge and tools necessary to navigate the evolving landscape of AI in higher education and leverage its potential to enhance teaching and learning outcomes. It focuses on the practical and pedagogical aspects of using AI tools in teaching.

    These key points emphasize responsible and ethical use of AI tools, aiming to maximize the benefits of AI technology while minimizing potential risks and negative impacts on teaching and learning practices.

    • Pedagogical Value When deciding whether, when, and how to incorporate AI into teaching practices, we must ensure the AI tools we want to use have pedagogical value. The purpose of using AI should be to enhance the learning experience and learning outcomes. It is important to align AI use with course objectives and curriculum goals.
    • AI Policies and Guidelines Follow the applicable federal, state, and university policies, regulations, and procedures.
    • Data Privacy and Security When AI is used, we need to implement robust measures to protect student data and comply with relevant data protection regulations (e.g., FERPA). We are legally required to protect sensitive student information from breaches or misuse. And compliance with regulations also helps build trust with students.
    • Transparency Clearly communicate why and how AI is being used in courses and disclose any AI involvement in grading or evaluation processes. Consider including a syllabus statement defining the acceptable use of generative AI in the course. This serves to build trust with students about AI use in the learning process, helps manage students’ expectations, and addresses their concerns proactively.
    • Equity and Accessibility Ensure the required AI tools in a course are accessible to all students, including those with disabilities. This is critical to prevent AI from exacerbating existing educational inequalities and helps ensure all students can benefit from AI integration and promote fairness and inclusive education.
    • Academic Integrity Establish clear policies on acceptable AI use in student work and clearly communicate the course AI policy with students. Implement strategies to reduce, prevent, and detect AI-assisted cheating.
    • AI Bias and Hallucinations Understand how AI system works and why it can generate biases and inaccurate or fabricated content. Critically evaluate the outputs of AI tools, and verify and refine AI-generated content to mitigate biases and hallucinations.
    • Human Agency and Accountability Maintain human oversight and human control in AI use. Use AI to assist rather than replace human effort. Users are responsible for their use of generative AI tools and are accountable for the AI-generated content they use (e.g. in students’ assignments of faculty’s feedback and grading).
    • Realignment of Learning Objectives, Assessments, and Activities When AI is used, consider realigning and restructuring learning objectives, assessments, and activities to advance meaningful learning in the age of AI. With AI easily handling lower-level tasks in the hierarchical structure of cognitive processes like information recall, greater focus should be on higher-order thinking skills such as critical analysis, evaluation, and creation.
    • Student Preparation Equips students with skills necessary for an AI-integrated world. Educate students on AI literacy and teach them how to effectively prompt, interpret, and critically evaluate AI-generated content. This approach helps students become informed users and creators of AI technology.
    • Teaching Students How to Use AI in Their Discipline It has become an urgent responsibility to teach students in higher education how to effectively use and critique AI technologies in their discipline. By doing so, we can equip them with essential skills for their future careers.

    National, international, and state policies, regulations, guidance, and recommendations provide regulatory frameworks for proper use of AI tolls in education.

    Generative AI holds significant potential to transform education, but it also necessitates a proactive and thoughtful approach. By embracing a policy framework that prioritizes ethics, transparency, and equitable access, we can harness the power of AI while mitigating potential risks and ensuring a responsible and inclusive future for all learners. WPU is in the process of developing our own AI teaching guidance. Here are a few external examples for your reference.

    Data Privacy and Security

    When utilizing AI tools, it's crucial to safeguard privacy by not sharing non-public data. This includes sensitive information like social security numbers, credit card information, or confidential hiring documents. Additionally, personal identification must be protected in accordance with state regulations, institutional policies at WPU, and the Family Educational Rights and Privacy Act (FERPA) of 1974. Adhering to these guidelines ensures compliance and maintains the integrity of private information.

    1. State Policy and Guidelines for Responsible Use of AI
      • Policy to guide State employees to responsibly use generative AI
      • Creating Content with Generative AI (video)
      • New Jersey Data Privacy Act (NJDPA). This act provides definitions of personal data and sensitive personal information:
        Personal Data: (name, Name, Address, Email address, Social Security number, Financial information, Geolocation data, Online identifiers, Biometric data)
        Sensitive Personal Information: (e.g., religious beliefs, political opinions, sexual orientation or sex life, citizenship or immigration status, mental or physical health, treatment or diagnosis, racial or ethnic origin, genetic or biometric data, status as transgender or non-binary)
    2. WPU Policies and Guidelines
      The WPU AI Task Force is working on a responsible and ethical AI use policy, which should cover data privacy and security.
      Following federal and WPU policies cover data privacy issues:

    Bias in AI

    Bias in GenAI is a critical issue and a significant concern in using AI in teaching as its unfair outcomes can perpetuate existing societal inequalities. The biases often arise from the data used to train AI systems, reflecting historical and current social biases. Following resources cover the key aspects of AI bias - what it is, where it originates, different types, examples, and potential mitigation strategies.

    To address AI bias, it’s essential to consider various types of biases such as:

    • Implicit bias: Unconscious discrimination against certain groups.
    • Sampling bias: When the training data does not represent the population accurately.
    • Temporal bias: When the data reflects past conditions that are no longer relevant.
    • Over-fitting: When the AI model is too closely tailored to the training data, failing to generalize well.
    • Edge cases and outliers: When the model does not account for rare or extreme cases.

    How to Mitigate AI Bias in Teaching?

    To mitigate AI bias when using AI tools in teaching, consider the following strategies:

    • Understand bias in GenAI: Understand the origin and different types of biases in GenAI. Learn and understand the AI tools and select or prioritize those created by diverse teams to help minimize built-in biases.
    • Educate students: Train students on the potential biases in AI systems and how to critically evaluate AI-generated content.
    • Human oversight: Critically review and validate AI-generated content before they are used in teaching materials and assessments.
    • Combine multiple AI tools: Use a variety of AI tools from different providers to cross-reference results and reduce the impact of biases from any single system.
    • Use AI in appropriate contexts/Contextual use: Ensure AI tools are used in appropriate contexts and not relied upon for tasks where human judgment and cultural sensitivity are crucial.

    Intellectual Property Rights

    The intersection of intellectual property rights and the use of generative AI is a complex and evolving area of law. The legal implications of using generative AI are still unclear, especially about copyright infringement, ownership of AI-generated works, and unlicensed content in training data. The following resources might shed some light on these issues.

    • Artificial Intelligence and Intellectual Property - WIPO (World Intellectual Property Organization): This resource covers a broad range of topics related to AI and IP, including the impact of AI on innovation, creation, and the various questions it raises for IP protection. It also discusses the WIPO Conversation on GenAI and IP, which explores the protection of creative works in the digital age and the challenges brought to the copyright system by GenAI.
    • USPTO releases report on artificial intelligence and intellectual property policy USPTO Report: The United States Patent and Trademark Office has released a report titled “Public Views on Artificial Intelligence and Intellectual Property Policy.” It provides a comprehensive look at stakeholder views on the impact of AI across the IP landscape, touching on patent, trademark, copyright, and trade secret policy, as well as database protection2.
    • AI and IP Springer Article: An article titled “AI and IP: Theory to Policy and Back Again” discusses various topics such as AI inventorship in patent law, AI authorship in copyright law, and the need for sui generis rights to protect innovative AI output. It also addresses the allocation of AI-related IPRs and the use of AI tools by IP offices.
    • Generative AI Has an Intellectual Property Problem - Gil Appel, Juliana Neelbauer, and David A. Schweidel. Harvard Business Review.
    • How Do Intellectual Property Rights Apply to Generative AI Outputs? – Martin Gomez and Joel E. Lehrer.

    AI Tools for Teaching

    Tool Description Pricing
    Blackboard AI Design Assistant Available within the Blackboard. Help design the course and create learning models. Based on given topics and materials, it can generate discussion and assignment prompts, rubrics, different types of tests, and question banks. Free for WPU Blackboard users
    Gradescope AI-enhanced platform to streamline the grading process for instructors and provide valuable feedback for students. Free for Basic.
    AI Bookstore Utilize AI to help users find books that align with their preferences through a chatbot. Free
    Casper.ai A Chrome extension that helps you summarize, understand, and share the learnings of any written content on the web. Free
    ClassPoint Integrate interactive features directly into Microsoft PowerPoint, generate interactive quizzes, gamification, questions with PowerPoint slides. Free for Basic; Pro $8/month.
    Magic School Use AI to generate educational materials, such as lesson plans, syllabi, vocabulary lists, and rubrics. Free for Basic; 8.33/month for Plus version.
    QuestionWell Generate essential learning questions for students based on input content. Free version; Paid Plan $7/month
    Twee AI-powered tools to create questions, generate dialogues, find quotes, brainstorm vocabulary for English teachers. Free version; Twee Pro $19/month
    Microsoft Reading Progress Reading Progress is an add-on in Microsoft Class team that supports and tracks the reading fluency of the students. Students record their readings on camera and submit the recording to Teams. The tool then offers automated feedback on pronunciation errors, missing words, added words, and other aspects of reading fluency. Free for WPU Microsoft Teams users.

    AI Tools for Research

    Tool Description Pricing
    Consensus Find relevant papers based on your research inquiries. Extracts "findings" from research. Synthesizes findings with a consensus percentage breakdown. Free (20 searches/month); Paid version allows unlimited searching.
    Elicit.com Reviews literature, analyzes research papers, automates time-consuming research tasks, provides a synthesis of themes and concepts, summarizes papers, and extracts data. Extracts details into table form. Free trial available. Pay for credits after the trial expires.
    scite site has a suite of products that help researchers develop their topics, find papers, and search citations in context. Free trial available. $20/month after the trial expires.
    Semantic Scholar Offers powerful search features, comprehensive coverage, quick paper assessment, discovering related research, up-to-date information. Free access and open resources
    Research Rabbit Understands what you’re researching and gives you recommendations, personalized digests, and visualizations of networks of papers and co-authorships. Free access
    Scholarcy AI-powered article summarizer. Free (short articles only); Paid version allows articles of any length.
    ScholarAI ChatGPT plug-in that helps clinicians connect with peer-reviewed research articles. Free trial available. Basic plan: $9.99/month
    Typset.io AI-powered tool to help you understand research papers better. Basic plan: free. Premium plan: $12/month (billed annually)

    While Generative AI can be an excellent tool to help students learn and improve their work, its core capabilities and wide availability make it easier than ever before for students to use it to submit work that is not their own. In the AI era, it is critical for faculty to balance the importance of mitigating inappropriate AI usage and AI’s potential benefits in teaching and learning.

    Our goal is to promote learning and academic integrity, not just to catch cheating and punish students. For this purpose, we suggest a holistic approach that emphasizes education, prevention, and fair assessment, while introducing methods and tools to help faculty mitigate possible inappropriate AI usage.

    • Academic Integrity Policy for Students

    • Copyright Policy
      WPU is actively developing policies around the use of AI-generated content to address the rapidly evolving landscape of generative AI tools. The university has an AI task force to develop guidelines for AI use, which are expected to cover academic integrity and unauthorized use of generative AI.
      WPU allows instructors the discretion to define how AI may be used in their courses. The university has offered a syllabus language menu to help draft AI policies and encourage faculty to consider their stance on AI use in their classrooms.

    WPU Suggested Syllabi Language
    All assignments submitted in this course must be your own and the ideas and contributions of others must be appropriately acknowledged (cited). The use of Artificial Intelligence (AI) programs and tools (e.g., ChatGPT) in this course are at the discretion of the instructor to ensure that they are being used to support your learning. Any use of AI programs or tools outside of what is permitted by the instructor and without proper attribution (citation) is a form of academic dishonesty which may result in grade penalties and/or subject to disciplinary action per the Academic Integrity Policy.

    Given differences in how AI might be considered or integrated into a course, here is some additional language that might be included/adapted:

    • This course does not permit the use of AI for assignments. Any use of AI will be considered in violation of the Academic Integrity Policy. All ideas must be your own.
    • This course permits AI to edit original ideas. Students may use AI to improve sentence fluency, spelling, and grammar; however, use of AI for content development will be considered in violation of the Academic Integrity Policy.
    • This course permits AI to develop ideas beyond editing. Students must cite AI each time it is used in an assignment and vet the accuracy of the content generated by AI. Content produced by AI may not exceed X% of the assignment.
    • Integrated into this course are varied uses of AI. When AI is used, the student must cite its use and vet the accuracy of the content generated by AI. Content produced by AI may not exceed X% of the assignment.

    The best way to protect academic integrity is to implement proactive measures to address the root causes and opportunities for misuse of generative AI.

    The root causes of student cheating include:

    1. Pressure to Perform: Students often face immense pressure to achieve high grades, which can lead them to seek shortcuts, such as using AI tools to complete assignments or exams. This pressure may come from personal ambitions, parental expectations, or the competitive nature of academia.
    2. Lack of Engagement: Engagement is key to motivating genuine effort and learning. When students are not fully engaged with the material or see assignments as irrelevant to their personal or professional goals, they may be more likely to use AI to bypass the work.
    3. Time Constraints: At WPU, many students juggle multiple responsibilities, such as work, family, and school. The time pressure can make the quick solutions offered by AI tools tempting, especially when deadlines are looming.
    4. Perceived Low Risk of Detection: If students believe that the use of AI-generated content will go undetected or unpunished, they are more likely to take the risk. This perception can be influenced by the lack of robust detection tools or clear academic integrity policies.
    5. Insufficient Understanding of AI Ethics: Some students may not fully grasp the ethical implications of using AI tools inappropriately. They may see AI as just another tool, not realizing that its misuse can constitute academic dishonesty.

    The opportunities for misuse of generative AI:

    1. Wide availability and ease of use of AI Tools: Generative AI tools like ChatGPT are readily accessible and often free or low-cost, making them easy for students to use without much effort or oversight.
    2. Assignments can be easily completed by AI: Traditional assignments, especially those focused on rote memorization or simple information retrieval, can often be completed by AI with little modification. And AI tools can do all kinds of writing assignments. This provides an opportunity for students to use AI-generated content without engaging deeply with the material.
    3. Lack of clear and consistent AI guides and Academic Integrity Policies: If the rules around AI usage are not clear or consistently enforced, students may misuse AI without fully understanding the boundaries of acceptable use.
    4. Lack of Instructor Familiarity with generative AI and AI tools: When instructors are not familiar with how generative AI works, they may not design assessments that effectively mitigate the risk of AI misuse or recognize AI-generated content when it occurs.
    5. Online Learning Environments: In online courses, where face-to-face interaction is limited, it can be easier for students to use AI tools without detection. The lack of direct supervision in online exams or assignments can create opportunities for misuse.

    To address the above root causes, we may consider the following measures:

    1. Lower the pressure to perform
      • Promote a Growth Mindset: Encourage a learning environment focusing on mastering concepts rather than just achieving high grades.
      • Offer regular low-stakes formative assessments: This lowers stakes and allows students to learn from mistakes without severe penalties.
      • Reasonable workloads: Students often cite high workloads and performance pressure as reasons for cheating. When students perceive their workload as too high, they may resort to cheating as a coping mechanism to manage stress and meet academic expectations.
      • Provide support services: Offer resources like tutoring, counseling, and time management workshops to help students cope with academic pressures. This support can reduce the temptation to rely on AI as a quick fix.
      • Use AI to provide personalized support: Leverage AI to offer intelligent tutoring, individualized guidance and support, and provide targeted explanations, feedback, and practice exercises. This kind of support can better serve students and save faculty a significant amount of time.
    2. Promote student engagement
      • Design relevant assignments: Create assignments that connect course material to students’ personal interests, future careers, or real-world problems. When students see value in their work, they are more likely to engage genuinely.
      • Incorporate active and authentic learning: Use active learning strategies, such as discussions, problem-solving sessions, or case studies, that require students to actively participate and think critically, making it harder to outsource the work to AI. When students are engaged in meaningful activities and actively think about what they are learning, it fosters their intrinsic motivation to learn and makes them less likely to cheat.
      • Intrinsic motivation: Build students’ intrinsic motivation by creating meaningful assignments, encouraging collaboration, giving students feedback, allowing students choice, and developing student’s self-efficacy.
      • In-class active learning: Use class discussions, peer instruction, problems solving, etc.
    3. Flexible deadlines and scaffold assignments
      • Flexible deadlines: Consider offering flexible deadlines or a window of time in which assignments can be submitted. This approach can help students manage their workload without feeling pressured to cheat.
      • Scaffold assignments: Break larger assignments into smaller, manageable tasks with staggered deadlines. This helps students stay on track and reduces the likelihood of last-minute cheating.
    4. Make student aware of the risk of detection and consequences of AI misuse
      • Demonstrate AI detection tools: Implement and demonstrate AI content detection tools to inform students about their use. This transparency can act as a deterrent, as students know their work will be checked for authenticity.
      • Enforce academic integrity policies: Clearly communicate the consequences of AI misuse and consistently enforce academic integrity policies. Ensure students understand the importance of these policies and the potential academic and ethical consequences of cheating.
    5. Promote understanding of AI ethics
      • Educate on AI ethics: Incorporate lessons on the ethical use of AI into your curriculum. Teach students about the importance of academic honesty and the long-term implications of misusing AI tools.
      • Promote ethical decision-making: Encourage discussions on the ethical dilemmas that AI presents in academia and beyond, helping students develop a strong ethical foundation.
      • Give an integrity quiz: Give the quiz before the first assignment and require a 100% score to continue in the course.

    Other measures to limit students' chances to misuse generative AI and make it harder for them to engage in dishonest practices:

    1. Create connections to current events, local community, and specific cases: Design assignments that require students to apply course concepts to recent news, local issues, or specific real-world cases, which may not be within the AI's training data.
    2. Reference materials that will not be in the AI’s data: Use niche or unpublished sources, such as proprietary research, lecture notes, or obscure articles that AI models are less likely to have encountered.
    3. Provide more oral exams, group work, handwritten assessments, and in-class assignments: Incorporate assessment methods that require real-time, spontaneous responses or collaboration, reducing reliance on AI tools.
    4. Engage diverse media: Ask students to create or analyze content across various formats like videos, podcasts, or artwork, which require skills beyond text generation.
    5. Assign social annotation: Use platforms like Hypothesis or Perusall to have students collaboratively annotate readings, fostering engagement and critical thinking that are hard to automate.
    6. Require attributions and citations: Ask students to provide references and citations. Some AI tools do not produce references and citations or provide fake or unverifiable ones.
    7. Shift the emphasis from the final product to the process: Students use to arrive at their conclusions. Require students to submit drafts, outlines, or reflections on their thought processes alongside the final work.
    8. Create AI Assignments
      • Integrating AI tools and technologies into assignments to account for AI capabilities.
      • Critical evaluation of AI outputs: Have students critique the accuracy, bias, or relevance of AI-generated content, fostering analytical skills.
      • Applying concepts to analyze data: Assign tasks where students must use AI tools to process data and apply course concepts, ensuring they understand the underlying principles.
    9. Run your assignments in AI chatbots: Test your assignment prompts in AI chatbots to identify and adjust questions that might be easily solvable by AI, ensuring the tasks demand deeper understanding.
    10. Use proctored exam tools such as Respondus LockDown Browser and Monitor: Use technology to monitor and restrict student activity during exams, reducing opportunities for unauthorized use of AI tools.

    Detecting AI-generated text can be challenging and will become even more difficult with generative AI evolving rapidly. However there are methods and tools to help faculty identify inappropriate AI use.

    1. Knowing and understanding your students: The most effective strategy for identifying potential breaches of academic integrity involves instructors developing a thorough knowledge and understanding of their students. It is not hard for a faculty with such knowledge and understanding to notice something is not right in her students’ works. Such human connection and understanding are a faculty’s best tool for recognizing inappropriate AI use.
    2. Comparing AI-generated text with benchmark writing samples: Obtaining benchmark writing samples by asking students to write something that AI cannot generate (such as deeply personal story and personal reflection) at the beginning of the semester to be used to compare against suspected AI generated texts in students’ submissions for inconsistency and stylistic differences.
    3. Using faculty expertise: As subject experts, you should use your expertise to identify inaccuracies or lack of depth in AI-generated content.
    4. Conduct oral checks and quiz students on their own work: if necessary, meet with the student in-person or online to conduct
    5. Looking for the possible indicators of AI-generated text
      • Repetitive Patterns: AI-generated text may exhibit repetitive text structure (e.g. 3 to 5 paragraphs, always summing up), frequent use of certain words and phrases (in conclusion, finally, etc.), or ideas. This is often due to the model's programming which often favors familiar combinations of words or topics.
      • Inconsistencies in Context: AI text might occasionally include contextually irrelevant or nonsensical information, showing a lack of deeper understanding of the topic.
      • Lack of personal experience, reflection, opinions, or voice: AI-generated text lacks genuine personal anecdotes, opinions, and emotions, often resulting in a more detached tone. It also tries to avoid making definitive statements by overusing phrases like "it seems," "possibly," and "could be."
      • Specificity and Detail: AI-generated text might lack specific details that a knowledgeable human would include, often providing more general information instead.
      • High Probability Phrases: AI models tend to use common phrases and clichés frequently because they are statistically more likely to appear in training data.
      • Excessive Hedging: Phrases like "it seems," "possibly," and "could be" might be overused as the model tries to avoid making definitive statements.
      • Outdated and incorrect information: AI tools may output outdated or incorrect information because lack the ability to verify facts in real-time. Popular AI tools such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude all have their knowledge cutoff dates and cannot access and process live information from the internet. These AI tools also tend to provide wrong citations.
      • Uncommon vocabulary, phrases and unnatural transitions: AI may use vocabulary or phrases you never expect your students to use. The flow of AI-generated text might be less smooth, with abrupt transitions between sentences or paragraphs that don't logically follow one another.

    When considering using AI detectors, we always need to be mindful of the fact that currently available AI detectors are problematic and not entirely reliable. They can often flag human-written content as AI-generated, may fail to detect some AI-generated content, and their results can be inconsistent. Giving these issues, it is not recommended to use AI detectors as the sole or even primary method for identifying cheating in teaching.

    However, carefully selected AI detectors might still have some limited utility if used cautiously and in conjunction with other methods. For example, we can use AI detectors:

    • As a preliminary screening tool to flag submissions for further human review (instead of as definitive evidence of cheating).
    • For educational purposes to demonstrate to students how AI-generated text might be detected and to discuss the ethics of using AI in academic work.
    • As part of a broader academic integrity strategy to mitigate inappropriate use of AI tools.

    Leading AI Detectors

    This list is not an endorsement of any tool below. AI detectors are problematic and not recommended as a sole indicator of academic misconduct.

    Tool Description Pricing
    GPTZero Optimizes its detection capabilities for teachers and educators, trains AI models tailored to evaluating student writing, easy to use, offers a more generous free tier Basic free with limited functions; Essential $7.5/month; Premium $12/month
    Originality AI Claims high accuracy in detecting AI-generated text, combines AI detection and plagiarism checking, generates detailed reports, and direct website scanning via Chrome extension Limited free option; Pay as you go: $30; Pro $12.45/month
    CopyLeaks Sentence-level assessment, flags even content that’s been rephrased or “spun” after initially being created by AI, more than 30 language coverage Limited free scan; AI Detector $7.99/month
    Winston AI Claims high accuracy, capable of processing handwritten documents, but the free tier only allows up to 2,000 words per month Free trial; Essential $12/month; Advanced $19/month

    Our objective should be to educate students about the ethical considerations surrounding AI utilization, not to reprimand them. By cultivating an environment of scholarly honesty and maintaining transparent dialogue and defined standards, we can significantly advance the understanding and management of AI applications in teaching and learning.

    • Follow the WPU Academic Integrity Policy: This policy details WPU informal and formal disciplinary processes and provides guidance when considering the enforcement process and the application of specific penalties.
    • Have a direct private conversation with the student: Talk to the student privately about your concerns, discuss the university’s and course’s AI policies, and assignment expectations, and explain why certain AI uses are considered academic dishonesty.
    • Act promptly: If students see cheating happening and do not see faculty responding, this can greatly increase their own likelihood of violating academic integrity.
    • Focus on learning: Frame the conversation as an opportunity for growth and learning, emphasizing the importance of developing critical thinking skills and original ideas.
    • Offer alternatives: Provide resources and guidance on how to utilize AI tools responsibly and ethically, such as for research or creative brainstorming.
    • Document the conversation: Record the date, time, and key points discussed to create a clear record.

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