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AI Information Hub for Faculty

Making Sense of AI in Education

Before integrating artificial intelligence (AI) into teaching and course design, it’s important to understand the different types of AI and their applications. AI isn't a single tool—it’s an umbrella term that includes several technologies that power today’s innovations in education, including:

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Machine Learning (ML): ML allows systems to learn from data and improve over time without being explicitly programmed. It powers applications like predictive analytics, personalized learning paths, and content recommendations.
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Deep Learning: A more advanced form of ML that uses layered neural networks to analyze complex, unstructured data like images, speech, and text. It's used in systems for speech-to-text, image recognition, and adaptive learning technologies.
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Generative AI: This subset of AI creates new content (text, images, audio, and code) based on patterns it has learned from large datasets. Tools like ChatGPT and Copilot can support faculty with idea generation, feedback, translation, and even coding.
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Large Language Models (LLMs): These are the engines behind most generative AI tools. LLMs analyze language patterns to produce coherent, contextually appropriate outputs. Their capabilities range from summarizing readings to drafting syllabi or responding to student questions.

Understanding how these technologies work, and their benefits and limitations is essential to using them responsibly and effectively in higher education. 

To explore these types of AI in more detail, visit UNF’s Artificial Intelligence site, which provides a comprehensive overview of key terms, concepts, and examples. The site also highlights UNF’s AI Council, strategic initiatives, innovation grants, and training opportunities—underscoring the university’s commitment to responsible, informed AI integration across campus.

To support the UNF community in understanding and engaging with artificial intelligence, the university has adapted a Framework for AI Literacy grounded in established models from Kennedy et al. (2023) and EDUCAUSE (2024). This framework outlines key competencies in AI understanding, responsible use, and critical evaluation. It serves as a foundational guide for building digital fluency and supporting informed decision-making around AI in higher education. 

The framework includes:

  • Definitions and examples of AI technologies
  • Core literacies for faculty and students
  • Guidance on responsible use and ethical considerations
  • Suggestions for integrating AI literacy into the curriculum
2024-11-28 UNF Framework for AI Literacy.docx