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International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Research Highlights

  • The Problem: The integration of artificial intelligence into educational settings introduces dependency risks and the uncritical acceptance of automated guidance, which can obscure individual metacognitive responsibility and reinforce algorithmic bias.

  • The Method: The researcher conducted a conceptual analysis integrating the Triadic Learning Alliance (TLA) model—consisting of student-AI, instructor-AI, and student-instructor dyads—with traditions from counselor education and supervision to build a two-level framework for human-AI collaboration.

  • Qualitative Finding: Reflexivity acts as a distributed metacognitive skill within technical systems to help users actively monitor cognitive biases; question algorithmic authority; realign human-AI interactions with ethical commitments; and maintain human empathy and accountability.

Abstract

As artificial intelligence (AI) is integrated into education and workforce development, the ability to engage reflexively with technology may represent an emerging metacognitive skill. This paper conceptualizes reflexivity as an intentional, cyclical process of recognizing assumptions, reflecting on human-AI interactions, and responding with ethical discernment. Drawing from counselor education and supervision models that emphasize self-awareness and metacognitive reflection, the paper proposes a conceptual framework for cultivating ethical reflexivity in AI-augmented learning and decision-making environments. Through conceptual analysis and integration of the Triadic Learning Alliance (TLA) model, the framework identifies potential strategies for professionals and students to monitor cognitive biases, critically examine algorithmic authority, and maintain human empathy and accountability when collaborating with AI systems. The discussion situates reflexivity within broader interpersonal and metacognitive competencies that may support lifelong learning and ethical reasoning in a data-driven world. This paper argues that reflexivity may function as a foundational metacognitive process for ethical human-AI collaboration in AI-augmented learning environments.

A link to a video of Jennifer Young's presentation can be found below in the Additional Files section.

Additional Files
Session 1.mp4 (181262 kB)
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