Using AI in the Classroom

Brief Overview

When considering the many new and ever-changing AI tools, their ubiquitous and exponential nature can make it difficult to know where to start and how they might be best used in the classroom. One way to approach Large Language Model (LLM) AIs like Chat GPT and Bing is to think of them as assistants or thought partners. The AIs can assist with instructional development and preparation tasks so that instructors can focus more on the pedagogy and practices that lead to the best outcomes for learners. When the capabilities of AI are paired with what instructors and students envision, the potential for AI to enhance the teaching and learning experience becomes more apparent.

Expand

AI can generate varied examples, scenarios, case studies, questions, activities

Students need and prefer varied examples, scenarios and case studies when learning new and complex information. Generating these things in order to expand on topics covered in class is one of the best use cases for LLM AIs, particularly because it is possible to seek endless variations to accomplish class objectives.

Example prompt: “Write three detailed scenarios that help demonstrate the accommodating style of conflict management. Each scenario should be 1-2 paragraphs long, written at a university level. Then, write 5 critical thinking questions related to the scenarios that focus on contrasting the accommodating style of conflict management with the other styles.”

Explain

AI can generate targeted explanations, descriptions, comparisons, summaries, instructions

Students generally learn best in contexts they already know, which makes providing targeted, varied explanations and comparisons especially effective. Generating focused summaries and explanations to help process long and/or difficult information is something students probably already use AI to do and can be leveraged to include a larger variety of input.

Example prompt: “Explain the kinetic theory of gases in a way that a non-scientist could understand. Frame it as a comparison to something most university students are familiar with in their daily lives. Also add a series of instructions for how to demonstrate the kinetic theory of gases in a simple science experiment.”

Iterate

AI can modify context, style, voice, format, structure

Diversity of information and examples is helpful for students, especially in discovering nuance, sparking new ideas and increasing engagement. On the more creative end of the AI spectrum, LLMs can take one set of text and transform it contextually, stylistically or structurally. This might look like explaining something in the voice of a certain person, turning a book into a song or vice versa, or visualizing data sets in multiple ways.

Example prompt: “Write a summary of Plato’s Republic in the form of a Stephen Colbert monologue and also in the style of an Ezra Klein essay. Then write a hip hop song about Plato’s philosopher kings.”

Assess

AI can provide feedback, error correction, assessments and answers

Beyond its ability to support instructors and students in content generation, LLMs can also be used to review content. This application of AI allows for feedback, error identification and assessment. On the more technical end, AIs can help identify errors in code (as well as generate new code) to help build new programs. At the same time, AIs can offer feedback on writing samples, make and grade assessment questions or offer suggestions and improvements on ways to teach and learn specific concepts for specific learner levels. Like any tool, it isn’t perfect, but it can be used to help students and instructors consider potential issues.

Example prompt: “Proofread the following text for spelling and grammatical errors. Provide a corrected version and list the corrections that were made. Offer suggestions to make it more engaging.”

Build AI Literacy

Students need to develop skills to use generative AI ethically and productively

AI literacy is "the ability to understand, use, monitor, and critically reflect on AI applications" (Laupichler, et. al, 2022). This does not include developing AI models; instead, AI literacy can be conceptualized as a broad view of the skills needed to apply generative AI in all disciplines. One way to approach this is through a framework for AI literacy, such as this one developed by Barnard College (Hibbert, et. al, 2024).

  1. Understand AI
    1. Be able to defin the terms "artificial intelligence," "machine learning," "large language model," and "neural network."
    2. Recognize the benefits and limitations of AI tools.
    3. Identify and explain the differences between various types of AI, as defindby their capabilities and computational mechanisms.
  2. Use and Apply AI
    1. Successfully utilize generative AI tools for desired responses.
    2. Experiment with prompting techniques and iterate on prompt language to improve AI-generated output.
    3. Review AI-generated content with an eye toward potential "hallucinations," incrrect reasoning, and bias.
  3. Analyze and Evaluate AI
    1. Examine AI in a broader context, bringing in knowledge from one's disciplin or interests.
    2. Critique AI tools and offer arguments in support of or against their creation, use, and application.
    3. Analyze ethical considerations in the development and deployment of AI.
  4. Create AI
    1. Synthesize learning to conceptualize or create new ideas, technologies, or structures that relate to AI. Reaching this level of literacy could include conceiving of novel uses for AI, building software that leverages AI technology, or proposing theories about AI.

References


Portions of this toolkit were developed by Western Michigan University's WMUx Teaching and Learning Team and adapted with permission for NIU by the Center for Innovative Teaching and Learning

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