AI LITERACY ACCESS
Problematizing AI Literacy Access- Understanding Student AI Literacy from Student Voices
Domestic undergraduate computer science students formally learn about machine learning and artificial intelligence in upper level undergraduate computing programs, yet they must navigate the lure of ChatGPT and other generative Artificial Intelligence tools that have been found to be somewhat accurate at completing early coding assignments. As AI tools proliferate, messaging about their use in academic settings are varied, and access to AI literacy is unknown. Through an investigation of interviews with Pell grant eligible college students at open access colleges, we address the following research questions: How do low-income undergraduate interview participants describe their uses of and attitudes regarding generative AI tool use for academic purposes? and What elements of AI digital literacy appear to be accessible to interview participants, based on their descriptive statements?
Student AI Literacy Insights
Analysis of interview data reveals key areas of student engagement and understanding concerning generative AI tools for academic purposes.
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Ethical AI Discussions
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Generative AI Skills
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AI Capacity & Limits
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Output Assessment
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Ethical Use & Independent Learning
AI Tool Capabilities & Assessment
Faculty Guidance & Contexts
Student Perspectives on Ethical AI Use
Many students expressed a strong moral stance against cheating with AI, preferring to learn independently. The study found 28 instances where interviewees discussed the ethical implications of AI tool use, highlighting a clear awareness of potential misuse. This indicates that while AI tools offer temptations, a significant portion of students actively navigate these ethical dilemmas.
One student articulated, "I'm not a fond of cheating... I don't want to get my degree that way." This sentiment underscores a desire for genuine learning despite the availability of powerful generative AI tools.
Leveraging AI for Understanding and Output Assessment
Students reported using generative AI tools not just for task completion, but for better understanding assignments. There were 23 instances of students describing their "Skill to use generative AI tools" and 20 instances of discussing the "capacity and limitations of AI". A crucial element of AI literacy identified was the ability to "assess the output of AI", which occurred in 12 interview passages.
As one interviewee noted, AI can be "really helpful... when I'm just trying to understand a problem or something." However, they also cautioned against blindly trusting AI output, stating that without content knowledge, one might mistakenly believe "oh, this is right. This is perfect."
Navigating Mixed Faculty Messages and Use Contexts
The academic environment presents varied messages regarding AI tool use, forcing students to navigate complex expectations. Interviewees mentioned "9 knowledge of AI tool use contexts" in 11 instances, often related to differences between departments or individual professors.
Some faculty "highly discourage using AI in their assignments," while others, especially those teaching AI, "support using AI only if you for help, not just passing code or something in as your homework." This mixed messaging requires students to develop nuanced understanding and judgment in their AI usage.
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Student Discussions on Ethical AI Implications in Academic Work
Enterprise Process Flow: Student AI Interaction Cycle
Understand Assignment
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Explore AI for Support
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Critically Assess AI Output
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Refine & Integrate
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Independent Learning
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Roadmap for Enhancing AI Literacy
A structured approach is essential for successfully integrating AI literacy development within educational institutions, ensuring equitable access and ethical use for all students.
Phase 1: Define AI Strategy
Establish clear guidelines for generative AI use in academic settings, addressing ethics and fairness, and forming an AI literacy task force.
Phase 2: Pilot AI Integration
Introduce AI tools in select courses with structured support for student learning and feedback mechanisms. Collect baseline data on student usage and perceptions.
Phase 3: Faculty & Student Training
Develop comprehensive training programs on AI literacy, tool capabilities, critical evaluation of AI output, and ethical considerations for both educators and students.
Phase 4: Continuous Evaluation & Adaptation
Regularly assess the impact of AI tools on learning outcomes, academic integrity, and equity. Update policies and resources based on ongoing feedback and technological advancements.
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