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3.1: Case Study- "Using AI for Social Justice Counter Points" with Dual Enrollment High School Students in Tennessee

  • Page ID
    • Christina Branson, Robert Ian Jones, Jon Parrish Peede, and Summer Boyd Vertrees

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    Teaching Context:
    This lesson was delivered to an English 102 Dual Enrollment (DE) class that takes place within a high school in the Nashville, TN area. Students are juniors or seniors, approximately 16-18 years of age. These dually enrolled students have either earned credit for the first portion of the English sequence through taking English 101 via a DE class previously, through securing a high enough ACT score, or through earning an approved score on the Advanced Placement (AP) exam. All students in this district are issued a Chromebook.

    What Happened:

    For this lesson, I began the day by telling students that we would be using Artificial Intelligence in our coursework. Immediately, students were a bit wide eyed and bewildered, curious to why we would suddenly be incorporating something that they had been warned about using in their paper writing. I used this opportunity to instruct on generative AI as a tool for learning and social justice, rather than as a copy/paste cheat code. After a brief overview of the difference, students really seemed to grasp how and why we would be employing AI.

    I wanted to ensure that students did not need to create an account in order to access generative AI. After having previously determined a free platform with ease of access, I planned for students to use that platform for the activity. However, some students encountered issues with individual access, with some randomly being asked to create an account. Students soon responded to the issue at hand, offering their own solutions to free generative AI access via Snapchat.

    Building off previous lessons concerning the defense of thesis statements, I asked AI to establish reasons behind why someone might not want all the water fountains in the school to be replaced with Gatorade. Together, we watched AI generate a list of reasons to avoid such an implementation. Next, we approached each component and created a pushback, or counter argument, to why AI’s stance might be wrong. After further modeling and discussion, students were released to appeal to AI for the alternate point of view on their previously selected topics.

    As AI created stances, students immediately got to work, creating product for their own counterargument. While they worked, I walked through the room, assisting students in their counter, as well as reviewing the content AI provided. The drafted content, along with a screenshot or copy of the AI results, was uploaded into the Learning Management System for full teacher review after class.

    What worked:

    Using AI to produce initial counters to social justice themed topics freed up class space to the actual creation of content to retort. Rather than spending time wondering what the other side might say, students were near immediately presented with the alternate point of view. By being able to begin drafting content quickly, the creation of a counter paragraph happened with greater ease, and students were also able to dedicate time to further research that might more fully explore the contrasting pint of view.

    What didn’t work:

    Expecting a singular free AI platform to meet the needs of the entire class was a bit short sighted. Between district web filters, internet connectivity issues, and matters of the like, I should have expected more conflict in this realm.

    For next time:

    In the future, I think providing multiple points of access initially would be best. Fortunately, students were able to pivot and suggest their own way of reaching artificial intelligence for the activity. However, the live recalibrating could have been avoided if the lesson had multiple platforms already at disposal.