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 AI Literacies for Faculty

What are literacies faculty can consider for their teaching and learning styles? Faculty interested in integrating AI into their curriculum and teaching pedagogy should consider literacies below.

 Pedagogical Literacy is defined as understanding how AI tools can effectively support teaching practices and enhance student learning experiences. If you are just starting out in this space, this may seem daunting. However, think about your personal learning AI space as "what would you do if you knew you couldn't fail?" Part of the learning process with AI is trial and error, and most AI systems, like Microsoft CoPilot or ChatGpt for example, will offer feedback, tell you when something isn't working, or even provide references if asked. 

In practice, faculty can have many creative approaches to increasing their own AI pedagogical literacies by way of including assistive and and engaging learning technologies for students. Examples of this are AI avatars for role play simulation, an AI student study buddy to practice language skills, improvement of writing skills, debate practice with an AI, coding coaching by an AI assistant for students, there are so many types of AI-driven personalized feedback!

Related to these concepts, is the understanding of other pedagogical literacies like the ones listed below with their definitions and practices.

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  • Digital Literacy
    • Definition: Understanding and effectively using digital technologies, tools, and platforms.
    • In Practice: Curate and evaluate AI resources and effectively select, critique, and integrate quality AI-based educational tools and content that align with learning outcomes and manage digital content effectively.
  • AI Literacy
    • Definition: Understanding basic principles of artificial intelligence, including its capabilities, limitations, biases, and ethical implications. 
    • Understand AI Fundamentals: Grasp basic AI concepts, capabilities, and limitations to guide meaningful integration into teaching practices.
    • In Practice: Teaching students how AI models work, assessing AI-generated content critically, or creating AI-informed assignments.
  • Data Literacy
    • Definition: Ability to read, interpret, and critically assess data produced or processed by AI systems.
    • Model and Teach Responsible AI Use: Guide students in the appropriate and responsible use of AI tools, clearly communicating expectations, citation practices, and academic integrity policies.
    • In Practice: Helping students interpret analytics, discern biases in datasets, or understand data-driven decision-making processes.
  • Algorithmic Literacy
    • Definition: Awareness and understanding of how algorithms operate, their potential biases, and their influence on information and decision-making.
    • In Practice: Teaching students to critically analyze recommendations made by AI, recognize how algorithmic biases emerge, or explore algorithm transparency and explainability.
  • Ethical Literacy
    • Definition: Understanding the ethical implications and responsibilities associated with AI deployment.
    • Promote Ethical Awareness: Address issues such as algorithmic bias, data privacy, fairness, transparency, and ethical responsibilities tied to AI technologies.
    • In Practice: Engaging students in discussions about privacy, surveillance, fairness, transparency, and accountability of AI systems.
  • Media and Information Literacy
    • Definition: The ability to access, evaluate, critically analyze, and create content using various media, including AI-generated outputs.
    • In Practice: Teaching students to critically evaluate AI-generated texts, images, videos, or detecting misinformation or disinformation created by AI.
  • Computational Literacy
    • Definition: Familiarity with computational thinking principles—such as problem-solving, decomposition, pattern recognition, and abstraction.
    • In Practice: Helping students approach problems methodically, create prompts for AI effectively, or understand basic AI model logic.
  • Critical Literacy
    • Definition: Ability to question underlying assumptions, ideologies, and power structures within AI and its outputs.
    • In Practice: Facilitate critical thinking with designing activities and discussions that encourage students to critically evaluate AI-generated content and consider implications of AI-driven decisions.  Additionally, encourage critical discussions about who benefits from AI use, who might be marginalized, and examining the societal implications of widespread AI integration.
  • Collaborative and Social Literacy
    • Definition: Capability to effectively collaborate and communicate using AI-supported platforms or workflows.
    • Support Inclusive Teaching Practices
      Ensure AI tools and resources are accessible and inclusive, considering diverse student backgrounds, abilities, and learning styles.
    • In Practice: Encourage collaborative AI inquiry by fostering collaborative learning experiences where students explore AI technologies, work together on problem-solving, and share insights into AI's role in their fields. For example, students could use AI tools for collaborative research projects, communication platforms that enhance group work, or foster online communities. 

These literacies equip faculty to thoughtfully, effectively, and ethically integrate AI into educational environments. The significance in listing and describing them here is for faculty to reflect on the various aspects of AI as it relates to pedagogy, students' perspectives on knowledge acquisition and application on various subject areas, and our ability to transform pedagogy to successfully leverage AI for student learning. 

Training & Development to Support Faculty Literacy

There are professional development opportunities and resources for faculty to increase their AI literacy.

CAT: Teaching with AI (Part 1) & AI in Action (Part 2)

In this series of three half-day workshops, participants explore practical methods for integrating AI into their courses. The workshops cover redesigning lessons with AI, creating AI-enhanced assignments and assessments, guiding faculty on ethical AI use, and addressing academic integrity concerns. This hands-on approach equips educators with the tools to elevate teaching and learning practices through AI. Register here.

 

Writing Across the Curriculum (WAC) Microcredential/Badge

Teaching with Writing in the Disciplines: The six-week course is open to all full-time FIU faculty from any discipline. Faculty are invited to explore the ways in which they might use AI tools in their teaching with writing so that AI strengthens rather than replaces student learning and craft course policies that are specific to their course learning goals.

Writing in the Disciplines:  This follow-on course helps faculty develop AI literacy so they can critically assess the accuracy, bias, and context of AI-generated text as well as question, evaluate, and interpret AI-generated content. Together, the faculty and student micro-credentials support WAC efforts to cultivate and sustain a robust culture of writing on FIU’s campus. More information about WAC's programming is available here.

AI Skill Pathways

 AI Skill Pathways from LinkedIn Learning and Microsoft offer learning paths and credentials to empower learners and organizations to develop, practice, and validate AI skills across roles to excel in the AI economy. When using FIU's Single-Sign-On (SSO) you have access to these tools from anywhere at any time).

 

LinkedIn Learning

LinkedIn Learning is an online platform that offers FIU faculty, staff, and students access to a vast library of expert-led video courses, including a range of AI topics from foundational concepts to more advanced techniques that help users build and expand their AI skills at their own pace. 

Getting Started:

  • To access the LinkedIn Learning platform, login with your FIU credentials.
  • Check out our LinkedIn Learning Collection that showcases a comprehensive range of AI-related topics or search for other AI videos by typing your topic of interest in the search bar. 
  • The experience level ranges from beginner, intermediate, to advanced; and the time to complete each learning experience ranges from under 10 minutes to over 3 hours, so choose whichever pathway suites your schedule. You can always save your progress and come back later. 

Microsoft Learn (CoPilot + Other Applications)

Microsoft Learn courses offer practical insights and foundational knowledge to help users apply AI principles and tools effectively in various settings. 

Microsoft Learn Educator Center offers resources and learning opportunities specifically tailored for higher education in collaboration with Microsoft tools, such as CoPilot. 

Getting Started:

  • Sign in with your FIU credentials to earn badges and certifications on Microsoft Learn as you progress through courses and learning paths, showcasing your AI skills and expertise.
  • You may search for an AI topic you are interested in learning or browse through our curated Microsoft Learn collection focused on AI, covering topics such as responsible AI, generative AI, and machine learning, and more. 

 

FIU Develop (ETS)

The FIU Develop platform serves as FIU's hub for online, self-paced professional development, offering a range of self-paced training modules designed for flexible learning. Below is an example of an AI-focused professional development opportunity. 

Getting Started:

A learning opportunity reviewing Artificial Intelligence (AI), Tech Integrity, and its impact on our teaching and learning environments at FIU.

FIU List of approved, pending and prohibited technologies

The list of approved technologies has been carefully reviewed by FIU’s Technology Evaluation Group (TEG) to ensure they meet the university’s high standards for data privacy, security, and academic value. As FIU continues to explore and expand its AI-powered offerings, this list will be regularly updated to keep you informed on the latest, approved technologies.