Making metacognition visible: AI-generated learning clues as signals of self-regulation and competence
Abstract
Generative artificial intelligence makes knowledge instantly accessible, shifting the core educational value from what one knows to the ability to learn. This paper argues that metacognitive self-regulation, meaning the capacity to monitor, evaluate, and adjust one's own cognitive processes, is the foundational competence that AI cannot automate. Yet higher education lacks tools to simultaneously develop and document this capacity. We present Aegis-Strategos, a generative AI-powered strategic simulation device that activates metacognitive regulation through a structured comparison mechanism (the Oracle) across six sequential turns. In this exploratory mixed-method study with 15 master’s students (MAE Entrepreneurship & Innovation, Université Marie & Louis Pasteur), the device generated 90 scored interaction traces. One student's trajectory from 40/100 at turn 3 to 95/100 at turn 4, illustrates the complete self-regulated learning cycle in real time: erroneous forethought, AI-detected conceptual confusion, feedback-driven correction, and observable progression. We propose a three-layer theoretical model: cognitive (Anderson & Krathwohl, 2001; Flavell, 1979; Zimmerman, 2002), mechanistic (Nicol, 2021; Sadler, 1989; Carless, 2015), and valorization (Spence, 1973; Hillage & Pollard, 1998; Yorke & Knight, 2006), to show that AI interaction traces can function as credible competence signals in the labor market. The paper concludes that, within the limits of this exploratory study, these devices partially answer both missions of higher education: developing capacities and helping professional integration. If receptor credibility among employers is actively constructed, longitudinal evidence is produced.
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