AI Literacy – Learning Resources

AI literacy at WP means a great deal more than learning to “use a chatbot.” It includes understanding how AI systems work at a high level, what they are good at, where they fail, and how to make responsible choices about privacy, bias, attribution, and academic integrity.

Students: Student Guide to Artificial Intelligence

William Paterson students have access to a comprehensive AI resource tailored for college students: the Student Guide to Artificial Intelligence. Organized around seven core principles, it supports students in using AI responsibly in coursework and career preparation:

  1. Know and follow your school’s rules.
  2. Learn about AI (how it works and where it fails).
  3. Do the right thing (integrity and accountability).
  4. Think beyond your major (AI in every field).
  5. Commit to lifelong learning.
  6. Prioritize privacy and security.
  7. Cultivate your human abilities (judgment, creativity, empathy, communication).

Faculty and staff: a growth-mindset approach

Faculty and staff are invited to grow their understanding of AI, particularly tools that connect directly to their work on campus. More than most technologies, AI requires lifelong learning. Building on the shared growth mindset highlighted in WP’s HR Summer Development Programs, employees can use self‑directed learning options—often with AI itself acting as a guide to identify role‑specific resources—alongside WP training opportunities.

With new tools emerging regularly and departmental needs varying across campus, colleagues play an important role in learning together and sharing ideas. Many AI features already exist within commonly used systems such as Workday and Microsoft, offering practical ways to streamline everyday tasks. Through exploration, experimentation, open sharing, and ongoing curiosity, WP employees can strengthen confidence and adaptability while navigating an evolving digital environment.

Recommended learning pathways

  • AI basics: What generative AI is, how it’s trained, and why it hallucinates.
  • Prompting as communication: How to write—and use AI to write—instructions, provide context, and set constraints (tone, audience, length, format) and save them into memory for re-use.
  • Verification and fact-checking: Methods for validating AI outputs and documenting sources.
  • Bias and fairness: Recognizing bias, testing for differential outcomes, and designing equitable workflows.
  • Privacy and security: What not to share; how data can be stored or used by vendors; safe alternatives.
  • Discipline-specific practice: Field‑relevant use cases and limitations (e.g., education, business, health, arts, sciences).

Campus learning formats (examples)

  • Workshops and webinars (introductory to advanced).
  • Short “how‑to” guides and templates (syllabus language, disclosure statements, prompting checklists).
  • Faculty learning communities and show‑and‑tell sessions for teaching experiments.
  • Micro‑credentials or certificates focused on applied AI in specific disciplines.