Beyond Basic Literacy: Wiring Science Students for Rapid Technological Shifts via Self-Regulation and Collaborative AI Metacognition
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Abstract
To thrive in today's rapidly shifting technological era, future professionals must possess strong adaptive capabilities. Consequently, this research models the mechanisms driving student adaptability, specifically analyzing the interplay of Digital Fluency (DF), Self-Regulated Learning (SRL), and Collaborative AI Metacognition (CAM). Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), we evaluated survey responses from science undergraduates at North Eastern Mindanao State University. While baseline digital fluency directly enhances adaptability (β = 0.198, p < 0.001), this technological proficiency achieves its greatest impact when coupled with internal learning strategies. Notably, self-regulation acts as a critical bridge, significantly mediating the DF-adaptability link (β = 0.131, p < 0.001). Furthermore, the capacity to critically reflect alongside artificial intelligence (CAM) emerged as a powerful direct catalyst for adaptive performance (β = 0.406, p < 0.001). Ultimately, preparing a resilient workforce requires moving beyond basic computer literacy; educational institutions must actively foster self-directed learning behaviors and sophisticated AI evaluation tactics to ensure graduates can seamlessly navigate future technological disruptions
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