Welcome to the Student AI (Artificial Intelligence) Literacy Series. This is an 8-part workshop series relevant to everyone, not just those interested in learning about AI.
Generative AI is quickly becoming an unavoidable part of every-day life. It's important that every college student graduates with the literacy skills necessary to use, encounter, and question GenAI in the real world. This series covers the most essential topics in AI literacy, from the basics of understanding machine learning to applying new critical thinking skills.
Use this companion page to identify the topics for each part of the workshop series, to follow along each week by clicking the dropdown, and to explore these subjects further by following the links provided.
You can sign up for the workshop series each week from the "workshops" tab on the Academic Support Center homepage.
Part one of the AI Literacy workshop series covers:
- A brief history of AI technology
- The basics of what AI technology is
- How GenAI and Machine Learning work
- How Large Language Models (LLMs) work
- What AI literacy is and why it's important
- The differences between AI and human "thinking" and communication
Understanding the basics provides a foundation for all other literacy and knowledge to build. To use tools effectively and ethically, we need to understand what these tools are and how they work.
The image below is taken from the workshop. It explains why AI Literacy skills are necessary.
The image below is taken from the workshop. It explains what AI Literacy includes.
For more information on the concepts from today's workshop, check out these links:
Access and read this Forbes article about the history of AI technology here.
Part two of the AI Literacy workshop series covers ethical issues such as:
- Academic Integrity
- Copyright
- Privacy and Security
- Environmental Impact
- Labor and Job Replacement
- Critical Thinking
- And Other Potential Harms
It's important for people to understand the ethical considerations of new technology and tools before using them. It's not enough to consent to use or encounter these tools, it needs to be informed consent. Informed consent means fully understanding potential risks and consequences before engaging with something.
For more information on the concepts from today's workshop, check out these links:
Access and Read the Shapiro Library's Page on Citing AI
Access and Read how to Cite Generative AI in APA at the APA Style Guide Blog
Access and Read how to Cite Generative AI in MLA at the MLA Style Guide Blog
Access and Read how to Cite Generative AI in Chicago on the Chicago Style Q&A Page
Read more about the Deloitte's unethical AI use in Fortune Magazine
Read more about Air Canada's Chatbot lawsuit in this article by The Guardian
More sources from week two's topics:
- Read about copyright law from the US congress website
- Review the Standford study on AI privacy policies
- See Duckduckgo's information page about duck AI's privacy measures
- Read this SNHU article about the environmental impact of AI
- Read this UN article about AI's impact on the environment and what can be done about it
- Review this data about AI's impact on labor and the job market
- Read this CNBC article about the MIT study on AI's ability to replace jobs
Part three of the AI Literacy workshop series covers SNHU's policies and guidelines for GenAI. In this workshop, you will learn about:
- SNHU's Student Use Guidelines for GenAI
- Your rights as a student at SNHU regarding privacy and AI use
- Expectations for disclosure and transparency
- Tools that have been vetted by SNHU's AI team
Before using AI as a student at SNHU, it's necessary to be aware of the policies and guidelines SNHU has provided regarding AI use. These guidelines will not only provide clear boundaries for academic integrity, but they also help you navigate using GenAI in ways that enhance your learning experiences instead of replacing vital learning and skill development. Following these guidelines will ensure you are prepared for your future career.
The infographic below reviews SNHU's Student Use Guidelines for Generative AI use. You can view the original resource with a screen-reader compatible text version of the infographic at our Academic Resource Center page 8 Tips For Students Using Generative AI (Infographic).
For more information on the concepts from today's workshop, check out these links:
Access and Read the Shapiro Library's Page on Citing AI
Access and Read how to Cite Generative AI in APA at the APA Style Guide Blog
Access and Read how to Cite Generative AI in MLA at the MLA Style Guide Blog
Access and Read how to Cite Generative AI in Chicago on the Chicago Style Q&A Page
View the Academic Resource Center's AI Tools section for a list of screened AI tools
Part four of the AI Literacy workshop series covers AI fluency skills including:
- The difference between AI fluency and AI literacy
- How to choose the right AI tools
- Prompt Engineering (how to write quality prompts for GenAI)
- Prompt Refining
- Ways to practice using GenAI
If you want to learn and develop the skills to use AI tools effectively, or you plan to go into a career or industry that expects employees to have these skills, this is the workshop for you.
The image below is taken from the workshop. It breaks down the P.R.O.M.P.T. method of prompt engineering. For a screen-reader compatible text-only version, see the dropdown below the image.
P.R.O.M.P.T.
- P for Purpose: What is the reason for the prompt?
- R for Role: Give the AI a role to play (editor, instructor, mentor, partner, assistant)
- O for Organize: Structure your prompt logically, provide clear context
- M for Model: Specify a clear desired outcome and provide examples if relevant
- P for Parameters: Provide context, details, scope, boundaries, and limitations that are relevant. Set up necessary gaurdrails.
- T for Tweak: Read through before sending. Evaluate the output. Refine and re-prompt if necessary.
For more information on the concepts from today's workshop, check out these links:
See the list of AI tools in the Shapiro Library's AI literacy guide
View the Academic Resource Center's AI Tools section for a list of screened AI tools
Read Edutopia's article about the P.R.O.M.P.T. method and student AI use frameworks
Part five of the AI Literacy workshop series covers AI output evaluation and assessment. The topics covered will include:
- How to assess outputs for relevance
- How to assess outputs for usefulness
- How to assess outputs for accuracy
- How to assess outputs for bias
- And how to ensure your work contains a human contribution
Output evaluation and assessment is one of the most important AI literacy skills you can have if you plan to use AI tools to assist you in doing your own projects or work.
The image below is taken from the workshop. Use it as a visual reminder of how to check AI outputs for relevance.
The image below is taken from the workshop. Use it as a visual reminder of how to check AI outputs for accuracy.
The image below is taken from the workshop. Use it as a visual reminder of how to check AI outputs for bias.
For more information on the concepts from today's workshop, check out these other links:
Read more about AI hallucinations in this article from PubMed Central
Read about ChatGPT Health's February 2026 safety evaluation in this article from The Guardian
Learn more about what bias is in this Psychology Today article
Read about the racist facial recognition technology in this article from The Guardian
Learn more about confirmation bias in this Simply Psychology article
Learn more about Latimer AI in this Academic Resource Center Introduction
Part six of the AI Literacy workshop series explores how critical thinking is still necessary when using AI tools. Some of the topics discussed are:
- Why AI should enhance critical thinking, not replace it
- Using critical thinking to evaluate why AI is being used before using it
- Using AI as a learning tool, not a replacement for learning
- Using AI for creativity and skill development without offloading critical thinking
- The similarities between AI literacy and Media literacy
- Using critical thining to evaluate bias in AI
- Self-reflection and self-honesty as necessary critical thinking with AI use
Off-loading critical thinking is one of the main concerns regarding GenAI tools. But it is completely within your control to use AI in a way that enhances and provides new opportunities for critical thinking instead of offloading it.
The image below is of the GenAI Decision Tree for Students. You can find this resource and a screen-reader compatible text-only version of the Decision Tree in our Academic Resource Center Guide GenAI Student Decision Tree (with Infographic).
Reflection Questions to Ask Yourself After Using AI
- What will I do with the AI output?
- What was the outcome of my output evaluation?
- How much revision was needed? Did I need to correct hallucinations?
- Was the AI helpful? Did it save me time, resources, or provide me with more equitable access to a task?
- Would I use AI like this again?
- How would I do things differently next time to improve results or usefulness?
- Was this tool an effective choice? Would I use a different tool next time?
- Are my own ideas, work, and voice still reflected in the results?
AI Self-Audit Questions for Regular Reflection on AI Use
- How much do I rely on AI?
- Do I attempt the task myself first?
- Do I use AI ethically and cite it when I have?
- Am I developing and using my own voice consistently in my work?
For more information on the concepts from today's workshop, check out these other links:
Read about the MIT Brain Activity study
Read about the Anthropic student AI use study
Learn more about using AI for learning from this Stanford guide
Check out SNHU's Career Services
Read more about the Apple Card bias incident in this Harvard article
Read more about the Amazon resume screen bias incident in this article from the Guardian
Read more about the health care risk algorithm bias in this Scientific American article
Learn more about Latimer AI in this Academic Resource Center Introduction
Part seven of the AI Literacy workshop series covers the ways AI is changing workplaces, including:
- Which industries are impacted the most
- The AI literacy skills that employers feel are most important for job candidates to have
- Ways AI can be used for good in certain industries and workplaces
- Predictions and projections for the future professional world
Some workplaces and industries are adapting AI technology more rapidly than others. Some industries now even expect job candidates to already have the skills and experience to use these AI tools. This workshop will explore the evolving professional landscape and what you can expect moving into or forward in your career.
Top AI Literacy Skills for the Workplace
- Prompt Engineering (learn more in week 4)
- Critical Thinking (learn more in week 6)
- Problem-Solving (learn more in week 6)
- Creativity (learn more in week 6)
- AI ethics and bias (learn more in weeks 2, 5, and 6)
- Collaboration (learn more in week 5)
- Communication (learn more in week 1)
- Continuous Learning (learn more in week 6)
For more information on the concepts from today's workshop, check out these other links:
See how AI can help your job search at SNHU's Career 360 AI Page
See the Pew Research Center's survey results about AI use in workplaces
Read this Forbes article about surveys done on employer expectations for AI skills
Read about the top 10 AI skills in the workplace on OpenSesame
Part eight of the AI Literacy workshop series teaches you how to apply AI literacy skills to the real world. This workshop covers:
- How to detect AI generated content online (including deepfakes and AI generated text)
- How to self-monitor your own learning when using GenAI tools
- How to self-monitor your own decision making regarding GenAI use
This week of the workshop is relevant to everyone, including those who don't plan to use AI themselves. Encountering AI in the real world is already unavoidable, and it will only become more prevalent. Knowing how to navigate an AI world equips you to think more critically about the content you encounter on a daily basis and protects you from misinformation and disinformation.
Identifying AI Content
1. Identifying Deepfake Images
- Does the image seem surprising or shocking?
- Is the anatomy accurate? Do body parts run together? Are body parts missing? Are there extra body parts?
- Does the style seem hyper-realistic or cartoonish?
- Do items in the image function properly?
- Does the image follow the rules of physics with shadows, light, reflections, etc?
- Does the image represent accurate sociocultural plausibilities?
2. Identifying Deepfake Videos
- Does the video seem surprising or shocking?
- Do age features in skin, eyes, and hair match?
- Look carefully at eyes and eyebrows--do they move in a natural way? Does the person blink too little or too much?
- Look at lip movements. Do the lip movements seem natural?
- Are shadows appearing in places you expect them to? Are the natural physics of a scene being followed?
- If the person is wearing glasses, is there a glare when we expect there to be? Does the glare shift as they move? Is there too much glare?
3. Identifying AI Writing
- Over-polished with perfect, standard grammar
- Uniform sentence structures (low "burstiness")
- Generic tone
- High predictability
- Repetition of words and phrases
Monitoring Learning with AI
The image below is a screenshot from the workshop. It shows Bloom's Taxonomy and questions to reflect and self-monitor learning when using AI tools.
Self-monitoring questions:
- Could I do this on my own?
- Am I becoming an expert in my field?
- Have I learned the material to the level that I could create something new? (this means not skipping any steps in Bloom's pyramid)
For more information on the concepts from today's workshop, check out these other links:
Detecting Deepfakes article from MIT Media Lab
The Student AI Decision Tree Guide from the Academic Resource Center
AI Reflection and Self-Monitoring Guide from the Academic Resource Center
Additional Tips:
Check out the Aritifical Intelligence section of our Academic Resource Center for more resources about Generative AI.
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