Click a question below to be taken to the answer.
What IS Generative AI anyway??
Besides ChatGPT what are other major GenAI tools I should know about?
How exactly does Generative AI work? And how do LLMs work? (jargon-free, please!)
Is the output of these GenAI platforms reliable? Can I trust the veracity of the information?
How can GenAI be used in teaching and learning?
Does SFCC have a GenAI use policy?
What are student perceptions of GenAI?
What are some other ethical implications I should consider when engaging with various GenAI platforms?
How 'smart is GenAI really?
How can I stay updated on the firehose of latest GenAI developments?
Where can I find a more comprehensive list of GenAI tools and apps as well as reviews?
Be honest! Was generative AI used in the creation or editing of this guide?? (And if the answer is yes, please explain in your own words! 😁)
What IS Generative AI anyway??
Generative artificial intelligence (AI) is a type of AI that can create new content like text, images, audio, and videos. It uses machine learning to learn from large amounts of data and create new content based on prompts.
How it works
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Generative AI uses machine learning models to learn patterns from data.
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It predicts what word, sound, or pixel would come next in a pattern.
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It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter.
What it can do
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Create a short story based on an author's style
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Generate an image of a person who doesn't exist
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Compose a symphony in a famous composer's style
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Create a video clip from a textual description
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Rewrite text in a different form
Besides ChatGPT what are other major Generative AI tools I should know about?
The major players are:
ChatGPT: https://chatgpt.com/
Claude: https://claude.ai/
Gemini: https://gemini.google.com/
Microsoft Copilot: https://copilot.microsoft.com/
DeepSeek: https://chat.deepseek.com/
Want to learn more? Check out their Wikipedia entries:
ChatGPT Claude Gemini Microsoft Copilot DeepSeek Grok
How exactly does Generative AI work? And how do LLMs work? (jargon-free, please!)
And here are some handy glossaries of Generative AI terms:
Is the output of these GenAI platforms reliable? Can I trust the veracity of the information?
That is a huge topic of debate right now. The short answer is: If the results/answers/information matter a lot to you, be prepared to fact-check the GenAI's output.
While GenAI platforms have made significant advancements, the output is not yet consistently reliable. Consequently, they still face several challenges:
1. Accuracy issues: Generative AI can produce inaccurate, nonsensical, or biased content due to limitations in their training data and understanding.
2. Hallucinations: These models can generate plausible but incorrect information, presenting it as factual.
3. Bias: Inherent biases in training data can lead to skewed or discriminatory outputs.
4. Inconsistency: The reliability of responses can vary between queries and models.
5. Lack of real understanding: AI generates content based on patterns rather than true comprehension.
To mitigate these issues:
- Always fact-check AI-generated information, especially for important or sensitive topics.
- Use lateral reading to verify claims across multiple sources.
- Be aware of the AI's limitations and potential biases.
- Treat AI outputs as starting points or suggestions rather than definitive answers.
How can generative AI be used in teaching and learning?
Generative AI has many potential use cases for teaching and learning. It can tailor materials to individual learning styles, offer instant feedback, and support diverse needs through features like text-to-speech and language translation. AI tutors could provide 24/7 assistance, adapting to students’ backgrounds and learning preferences while generating content in various formats. In assessment, AI can help create test questions, automate grading, and provide personalized feedback. It also could also support project-based learning by aiding research and analysis. However, ethical considerations such as bias, privacy, and academic integrity should always be addressed to ensure responsible AI use in education.
CUNY just published a thorough yet concise Teaching with AI Toolkit that covers: About AI, Course Policies, Assignment Makeovers and Learning Activities that you may find helpful.
Does SFCC have a GenAI use policy?
While SFCC does not have a single, institution-wide policy, effective fall 2024, all credit-bearing course syllabi must include a Generative AI policy, where faculty may either choose one from a list of four policy statements (ranging from complete prohibition to liberal use of Generative AI) or craft their own policy with approval of their Dean/Assistant Dean. (learn more here)
What are student perceptions of GenAI?
That’s a constantly evolving question. Right now, based on a handful of recent reports, here’s a snapshot of students’ GenAI perceptions:
Students view GenAI as a collaborative tool supporting critical thinking and learning rather than simply providing answers. They recognize GenAI's benefits, including accessibility, efficiency, quick feedback, and various academic uses such as explaining difficult concepts, language translation, brainstorming, summarizing text, and coding assistance. However, students are concerned about ethical issues, biases, equity, and accessibility, along with the risks of over-dependence and loss of cognitive skills and creative originality. They emphasize the importance of robust information literacy skills to critically assess GenAI outputs and there is a strong call for clear policies and updated assessment strategies that reflect contemporary teaching. Additionally, students anticipate GenAI's significant impact on the workforce, acknowledging both potential job losses and emerging opportunities, and seek institutional support in skill development. Students view equitable access to reliable GenAI tools as crucial to avoid exacerbating digital inequities, with differences in usage and perception noted across disciplines and gender.
Examining Faculty and Student Perceptions of Generative AI in University Courses
Student perceptions of generative AI
What ethical concerns should I consider when using generative AI ?
There are indeed some significant ethical concerns regarding the use of generative AI tools.
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Bias and Fairness: Generative AI can reinforce societal biases in its training data, leading to unfair or discriminatory outputs, such as underrepresenting certain groups in professional roles.
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Misinformation and Deepfakes: The realistic nature of AI-generated text, images, and videos raises concerns about misinformation and deepfakes, which could be used for deception and manipulation.
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Privacy and Data Rights: Training large AI models involves vast data collection, raising privacy concerns about the use, misuse, and potential reconstruction of sensitive personal information.
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Intellectual Property and Copyright: AI-generated content presents unresolved legal questions about ownership and copyright, including the risk of reproducing elements from copyrighted material.
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Job Displacement: The increasing capabilities of generative AI may disrupt creative and knowledge-based jobs, while its opaque decision-making raises accountability concerns in high-stakes applications.
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Accountability and Transparency: The opaque nature of many AI systems makes it difficult to understand their decision-making process, raising accountability concerns, especially in high-stakes applications.
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Authenticity and Human Creativity: Some fear that AI-generated content could undermine human creativity, reducing the value of authentic human expression.
How 'smart' is GenAI really?
That question is difficult to definitively answer because there are different kinds of 'smartness.' One of the most apt descriptions I've heard of Generative AI's so-called intelligence is "...a brain without a mind" (Cao & Dede, 2023). So I'll hedge answering that question by providing an image and a few good, food-for-thought sources on the topic.
Image below: Many models don't perform particularly well on Humanity's Last Exam, as compared to expert-level academic capabilities. For more context on this image, go to https://agi.safe.ai/.
Food-for-Thought Sources
Cao, L., & Dede, C. (2023). Navigating a world of Generative AI: Suggestions for educators. The Next Level Lab at Harvard Graduate School of Education, 5(2).
Dastin, J. & Paul, K. (Sept. 16, 2024). I experts ready 'Humanity's Last Exam' to stump powerful tech, Reuters, https://www.reuters.com/technology/artificial-intelligence/ai-experts-ready-humanitys-last-exam-stump-powerful-tech-2024-09-16/
Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., ... & Verbeken, B. (2025). Humanity's Last Exam. arXiv preprint arXiv:2501.14249.
Roose, K. (Jan. 23, 2025). When A.I. passes this test, look out. New York Times. https://www.nytimes.com/2025/01/23/technology/ai-test-humanitys-last-exam.html
How can I stay updated on the firehose of latest GenAI developments?
Here are a few things you may want to try:
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Be OK with not being on top of everything. No really - it's OK! 🤗
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Check out the "Discussions in the Higher Ed Community" tab in this guide
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Create a Google search alert (Click here to learn how to set that up. It's easy!)
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Into podcasts? Check out Emily Laird's "Generative AI 101"! Episodes are short (~5-10) and informative!
Where can I find a more comprehensive list of GenAI tools and apps as well as reviews?
There are thousands of 'listicles' and reviews out there. For right now here's what we suggest.
Was generative AI used in the creation or editing of this guide?
In Sarah's own words... 😊
That's a great question! And the honest answer is Yes. (But not all that much really.) Basically I used GenAI to help with things like brainstorming ideas for good FAQs to include and the wording of some of the FAQ writing, as well as visual organization (bullet points, etc.) of that writing. Rest assured, the actual information/content was NOT generated by GenAI, but rather came from a great deal of research I've been doing on GenAI in higher ed. As for the writing in the other tabs of this guide, it's pretty much all mine, except I did use GenAI to help with a little wordsmithing for the "What are AI-powered research platforms?" section of the AI-Powered Research Platforms tab, mainly for a better writing flow.
