Mapping Students' AI Literacy Framing and Learning through Reflective Journals

2025-08-20

AI Summary

This study investigates the dynamic development of undergraduate students’ AI literacy through weekly reflective journals in an AI course. Prior research lacks longitudinal, fine-grained insights into how AI literacy evolves across cognitive, affective, and sociocultural dimensions. Method: A six-week qualitative longitudinal design was employed, with generative interpretive thematic analysis applied to reflective journals across five dimensions: AI conceptual understanding, application intentionality, disciplinary integration tensions, ethical concerns, and knowledge mediation. Contribution/Results: The study is the first to systematically trace learners’ continuous, situated construction of AI literacy, introducing the novel construct of the “AI knowledge mediator”—students who not only internalize AI concepts but also proactively disseminate and interpret them for non-expert peers and family. Findings reveal baseline AI awareness and ethical sensitivity, persistent tension between pragmatic enthusiasm and disciplinary depth, and spontaneous, context-sensitive knowledge mediation behaviors—offering a theoretically grounded framework and empirical evidence for AI general education design and literacy assessment.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Humans and AI: Learning Human Values and Preferences
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search

Abstract

This research paper presents a study of undergraduate technology students' self-reflective learning about artificial intelligence (AI). Research on AI literacy proposes that learners must develop five competencies associated with AI: awareness, knowledge, application, evaluation, and development. It is important to understand what, how, and why students learn about AI so formal instruction can better support their learning. We conducted a reflective journal study where students described their interactions with AI each week. Data was collected over six weeks and analyzed using an emergent interpretive process. We found that the participants were aware of AI, expressed opinions on their future use of AI skills, and conveyed conflicted feelings about developing deep AI expertise. They also described ethical concerns with AI use and saw themselves as intermediaries of knowledge for friends and family. We present the implications of this study and propose ideas for future work in this area.

Problem

• Understanding students' AI literacy development through reflective journals
• Exploring undergraduate technology students' self-reflective AI learning
• Investigating students' awareness and ethical concerns about AI

Innovation

• Reflective journal study for AI literacy
• Emergent interpretive process analysis
• Mapping ethical concerns and self-awareness