ENTERPRISE AI ANALYSIS
HBCU Student Perspectives on Identity, Persistence, and Code-Generating AI in CS Education: A Case Study
This study explores how Black students at Historically Black Colleges and Universities (HBCUs) perceive code-generating AI in relation to their identity and persistence in CS. Findings emphasize the importance of personhood and solidarity in CS persistence for these students. AI tools can augment pedagogy and address deficits but cannot replace human community. Conservative deployment of AI tools is recommended to avoid harms to learning, identity, and social life, and to mitigate risks of widening technological privilege gaps due to undervaluing premium AI.
Key Metrics from Our Analysis
Our detailed study yielded critical insights into the real-world implications of AI in education.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
HBCU CS students define their identity through their humanity (curiosity, creativity, diligence), their desire for social impact, and their navigation of social categories (introversion, nationality, intersectionality).
They prioritize communal goals and humanism, viewing computing work beyond mere productivity.
Gratification (short-term and long-term), social support (role models, family expectations, religion), self-teaching, and financial aid are key helpful factors for persistence.
Harmful factors include the perceived difficulty of CS, deficits (access, confidence, knowledge), and social barriers (introversion, pride, collaboration, nationality, underestimation).
Students expect AI tools to overcome deficits and offer moral support, potentially circumventing social barriers by providing a private, judgment-free learning environment.
However, drawbacks include exposing deficits, promoting antisocial tendencies, preventing learning, disrupting identity, and creating a financial burden (due to undervaluing premium AI).
Enterprise Process Flow
| Category | Benefits | Drawbacks |
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| Learning & Support |
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| Identity & Social |
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| Accessibility & Cost |
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Impact of AI on Student Confidence (Participant J13)
Participant J13 reported that Copilot helped build confidence in private, making them feel more willing to participate in class. They stated, 'It really shows [...] it's not that difficult. You just have to really work through the problem [...] [Copilot] makes me [want to] participate more in class, because I'm like, 'Okay I do know what I am talking about.” This highlights AI's potential to address knowledge and confidence gaps discreetly.
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Your AI Integration Roadmap
A phased approach to integrate AI effectively, preserving human connection and ensuring equitable access.
Phase 1: Pilot & Assess
Identify specific low-risk programming tasks suitable for AI assistance. Conduct pilot programs with a diverse group of students to assess AI's impact on learning and confidence without disrupting core pedagogy.
Phase 2: Curricular Integration
Strategically integrate AI tools into assignments where they augment learning and address deficits. Develop guidelines for AI use, emphasizing when and how AI can be a learning aid versus a shortcut.
Phase 3: Foster Human Connection
Implement social interventions like peer programming and faculty mentorship. Encourage students to create their own progress indicators to foster intrinsic motivation and celebrate human craftsmanship.
Phase 4: Address Equity & Access
Advocate for financial aid and corporate partnerships to ensure equitable access to premium AI tools. Monitor for potential widening of technological privilege gaps and intervene as necessary.
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