Faculty Senate

Artifical Intelligence (AI)

AI in Higher Education and Career Preparation

AI technology is evolving rapidly. AI can provide some useful tools, but can also be prone to dangerous errors. Misuse of AI can prevent students from developing the knowledge and skills necessary for successful careers. At WIU, students master skills and knowledge areas while also learning about the uses and limitations of AI technologies for work in various fields.

Popular Topics

  1. Defining Artificial Intelligence

    Artificial Intelligence (AI) can be defined as computer systems designed to simulate aspects of human intelligence, including learning, reasoning, organizing information, recognizing patterns, and generating content.

    Essentially, AI refers to systems designed to analyze large datasets and execute tasks that traditionally required human thought and effort. Recently, the definition has shifted from "narrow" task-specific scripts to Multimodal Generative Models that can process and generate text, code, audio, and visual data simultaneously.

  2. Key Terms:
    • Machine Learning (ML): A subset of AI where systems "learn" from data patterns rather than following hard-coded rules.
    • Generative AI (GenAI): A specific branch that creates new content (essays, images, datasets) based on its training, rather than just classifying existing data.
    • Large Language Models (LLMs): The specific architectures and model families (like GPT or Gemini) that power most modern academic tools.
  3. Characteristics of AI:

    Modern AI systems possess four defining traits that distinguish them from traditional software:

    1. Pattern Recognition & Synthesis

      AI does not "know" facts in the human sense; it recognizes statistical patterns.

      • Academic Application: AI can synthesize literature reviews by identifying common themes across thousands of papers in seconds—a task that would take a student weeks. However, it may not reliably judge the quality or errors of context in those referenced papers.
    2. Probabilistic Reasoning

      AI is probabilistic. It predicts the most likely next word based on a vast dataset. (Example, Grammarly or Word predictive text).

      • The "Hallucination" Risk: Because it relies on probability, AI can generate plausible-sounding but entirely fabricated citations or data points.
    3. Adaptability (“Learning”)

      Modern systems can learn new tasks with very little instruction.

      • Academic Application Concern: A student can provide three examples of a specific paper writing style, and the AI can generate similar style student essays on directed topics.
    4. Multimodality

      Currently, the boundary between text, image, and data has blurred. AI can now "read" a handwritten notebook, "watch" a lecture to generate a transcript, and "render" a 3D model of a molecule from a text description.

  4. AI Course Policies

    WIU course policies on the use of AI vary from class to class, depending on the skills and knowledge that each course is designed to teach and the types of AI that might be relevant to those particular skills and knowledge areas. Beginning in Fall 2026, every WIU class syllabus will provide clear information about permitted and prohibited uses of AI for that particular course.

    The current WIU Course Syllabus Policy can always be located here: https://www.wiu.edu/policies/syllabus.php

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