An Examination of How Technology Students Develop AI Literacy

Improving artificial intelligence (AI) literacy among students across disciplines has become an increasingly important task in building an AI-literate society - individuals competent in recognizing, understanding the impact of, and evaluating their interactions with AI. As we prepare students to make informed decisions about the use, development, and implementation of AI in their fields, it is essential to build opportunities to contextualize how AI affects their lives, profession, and society. Research on AI literacy is ongoing, and it often explores what factors and methods contribute to or hinder students' development of AI literacy. However, integrating best practices from educational research into AI literacy activities is challenging. Developing context-specific instructional activities is a key component; what works for one group of learners may need to be adapted to another. This dissertation focuses on three ideas: 1) the effectiveness of collaborative learning in developing AI literacy; 2) understanding how students can negotiate and make decisions about AI use, implementation, and ethics by being part of an inquiry-based community; and 3) the impact of students' formal and informal interactions with AI on their understanding and interest in using and learning about AI. All three areas are framed within the Sociocultural Paradigm, which posits that students learn through social experiences guided by context-specific tools and can help design and implement activities and interventions for students across different disciplines. The dissertation focuses explicitly on technology students who study disciplines such as cybersecurity, information technology, and data analytics, as they are both technical and non-technical learners, offering an opportunity to create activities that reach a broader audience. With this framing, I first conducted a systematic review of existing literature to assess the impact of collaborative learning incorporated with AI literacy activities and interventions. Next, I analyzed 12-hour-long transcripts from role-playing case study discussions using Epistemic Network Analysis to explore how students' discussions of ethically ambiguous scenarios help them engage within their community of inquiry and understand the different dimensions of AI. Lastly, I collected reflective journals from 22 participants over 6 weeks and presented the findings in two studies. I reviewed the journal entries using emergent analysis, exploring the interactions students reported with AI in and outside the classroom and how these interactions map towards building AI literacy. The findings of this dissertation suggest that theories and best practices across the Sociocultural Paradigm can help frame how to design and implement AI literacy activities. Additionally, collaborative AI literacy activities can help students build a deeper understanding of AI by enabling them to critically evaluate their own assumptions and perspectives on AI's use, development, or impact and compare them with those of other stakeholders. Finally, contextualizing the learning to topics that learners are interested in and that support their career goals is vital for engagement and as a feedback mechanism.

Author

• Hingle, Ashish

• AI competency
• AI education
• AI literacy
• Inquiry-based research
• Network analysis
• Reflection

Identifier

hdl:1920/14731

OAI identifier oai:identifier

oai:MARS:1920/14731