2025/02/07 by Thomas Corbin, Joanna Tai, Gene Flenady · 3 citations
Computer Science · Decision Sciences · Social Sciences · #Evaluation and Performance Assessment #Online Learning and Analytics #Student Assessment and Feedback
paper · pdf · doi:10.1080/02602938.2025.2459641
openalex publication_date 2025/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Generative Artificial Intelligence (GenAI) systems demonstrate impressive capabilities in providing various forms of feedback. However, claims to its potential overlook a fundamental aspect of effective feedback between humans: recognition between teacher and student. This paper critically examines the role of GenAI in providing feedback within higher education contexts, drawing on both the established feedback literature and philosophical work on recognition. Effective feedback is predicated on trusting and respectful relationships, which are in turn grounded in mutual recognition of shared vulnerability and agency. GenAI systems, lacking the capacity for genuine recognition, operate outside of this relational framework. Therefore, while valuable, GenAI feedback cannot fully replicate the pedagogical efficacy of human-provided feedback. However, the limitations of GenAI feedback may at the same time offer unique pedagogical opportunities. We propose that GenAI systems can provide a unique environment for students to receive and engage with feedback. This environment may help students build confidence and prepare for more meaningful engagement in recognitive feedback practices with peers and teachers. We therefore propose a novel framework distinguishing between “recognitive” and “extra-recognitive” feedback. This distinction allows for a more nuanced analysis of GenAI’s potential in feedback, offering a means of appropriately integrating GenAI into pedagogical practice.