High school students create machine-learning AI models for analyzing data.
As part of a North Carolina State University study, the 28 students explored the challenges, limitations and promise of AI. Likewise, the goal was to ensure that a future workforce is prepared to make use of AI tools.
The study was conducted in conjunction with a high school journalism class in the Northeast. Since then, researchers have expanded the program to high school classrooms in multiple states, including North Carolina. NC State researchers are looking to partner with additional schools to collaborate in bringing the curriculum into classrooms.
“We want students, from a very young age, to open up that black box so they aren’t afraid of AI,” said Shiyan Jiang, the study’s lead author and an assistant professor of learning design and technology at NC State.
“We want students to know the potential and challenges of AI, so they think about how they… can respond to the evolving role of AI and society. We want to prepare students for the future workforce.”
Researchers Develop StoryQ for Student Machine-Learning
First, researchers developed a computer program for the study called StoryQ that allows students to build their own machine-learning models. Then, researchers hosted a teacher workshop about the machine-learning curriculum and technology in one-and-a-half hour sessions each week for a month. For teachers who signed up to participate further, researchers did another recap of the curriculum for participating teachers, and worked out logistics.
“We created the StoryQ technology to allow students in high school or undergraduate classrooms to build what we call ‘text classification’ models,” Jiang said.
“We wanted to lower the barriers so students can really know what’s going on in machine learning, instead of struggling with the coding. So we created StoryQ, a tool that allows students to understand the nuances in building machine-learning and text classification models.”
A teacher participant led a journalism class through a 15-day lesson where they used StoryQ to evaluate a series of Yelp reviews about ice cream stores. Students developed models to predict if reviews were “positive” or “negative” based on the language.
“The teacher saw the relevance of the program to journalism,” Jiang said.
“This was a very diverse class with many students who are under-represented in STEM and in computing. Overall, we found students enjoyed the lessons a lot, and had great discussions about the use and mechanism of machine learning.”
Students Analyze Their Machine-Learning AI Models
Researchers observed that students formulated hypotheses about specific words in Yelp reviews. The students believed these words would indicate whether or not the review would be classified as positive or negative. For example, they expected reviews containing the word “like” to be positive. Then, the teacher guided the students to analyze whether their models correctly classified reviews. For example, a student who used the word “like” to predict reviews found that more than half of reviews containing the word were actually negative. Then, researchers said students used trial and error to try to improve the accuracy of their models.
“Students learned how these models make decisions, and the role that humans can play in creating these technologies, and the kind of perspectives that can be brought in when they create AI technology,” Jiang said.
From their discussions, researchers found that students had mixed reactions to AI technologies. Students were deeply concerned about the potential to use AI to automate selective processes for opportunities like scholarships or programs.
Researchers Launch AI-focused Program
For future classes, researchers created a shorter, five-hour program. They launched the program in two high schools in North Carolina, as well as schools in Georgia, Maryland and Massachusetts. In the next phase, they will study how teachers collaborate to launch AI-focused programs and a community of AI learning.
The study, ‘High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions’, published online on 13 March in the journal Learning, Media and Technology.
Image credit: iStock.com/Shutthiphong Chandaeng Students Create Machine-Learning AI
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