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This chapter offers a critical examination of the increasing integration of Generative AI (GenAI) into educational feedback mechanisms, challenging dominant portrayals of these technologies as neutral and objective tools that enhance assessment efficiency and standardisation. Anchored in feminist epistemology, Foucauldian analyses of power/knowledge, critical algorithm studies, and the sociology of education, the chapter repositions GenAI-generated feedback as a socio-technical construct deeply embedded in cultural, ideological, and epistemological assumptions. Synthesising insights from post-2020 empirical studies, audit documentation, and recorded GenAI-generated outputs, this chapter investigates how AI-based feedback systems prioritise linear argumentation, standardised academic English, and dominant Western epistemological norms. Such privileging often marginalises diverse rhetorical traditions and linguistic registers. Students from multilingual, rural, or non-urban contexts frequently encounter epistemic harm, as feedback mechanisms correct or devalue expressions rooted in their cultural and cognitive frameworks. Simultaneously, educators are recast as intermediaries of opaque technological systems they are unable to fully scrutinise, thereby limiting their pedagogical agency and critical discretion. Although GenAI feedback systems can deliver pragmatic support in under-resourced educational environments, their unexamined implementation often sustains symbolic violence and entrenches normative hierarchies of academic legitimacy. This chapter advances a framework for inclusive AI design, grounded in participatory methodologies, multilingual training datasets, and robust transparency protocols, as foundational steps toward feedback systems that foreground epistemic justice. By reframing feedback as a socially embedded and ethically consequential act, the chapter contributes to a sociological critique of algorithmic objectivity and advocates for reflexive, culturally aware principles in the design of educational AI.

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The Cultural Politics of ‘Objectivity’: A Sociological Critique of GenAI-Based Feedback Mechanisms in Educational Assessment

  • 章节

  • 发表日期：

    2026年

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  • Pratham Parekh

摘要

This chapter offers a critical examination of the increasing integration of Generative AI (GenAI) into educational feedback mechanisms, challenging dominant portrayals of these technologies as neutral and objective tools that enhance assessment efficiency and standardisation. Anchored in feminist epistemology, Foucauldian analyses of power/knowledge, critical algorithm studies, and the sociology of education, the chapter repositions GenAI-generated feedback as a socio-technical construct deeply embedded in cultural, ideological, and epistemological assumptions. Synthesising insights from post-2020 empirical studies, audit documentation, and recorded GenAI-generated outputs, this chapter investigates how AI-based feedback systems prioritise linear argumentation, standardised academic English, and dominant Western epistemological norms. Such privileging often marginalises diverse rhetorical traditions and linguistic registers. Students from multilingual, rural, or non-urban contexts frequently encounter epistemic harm, as feedback mechanisms correct or devalue expressions rooted in their cultural and cognitive frameworks. Simultaneously, educators are recast as intermediaries of opaque technological systems they are unable to fully scrutinise, thereby limiting their pedagogical agency and critical discretion. Although GenAI feedback systems can deliver pragmatic support in under-resourced educational environments, their unexamined implementation often sustains symbolic violence and entrenches normative hierarchies of academic legitimacy. This chapter advances a framework for inclusive AI design, grounded in participatory methodologies, multilingual training datasets, and robust transparency protocols, as foundational steps toward feedback systems that foreground epistemic justice. By reframing feedback as a socially embedded and ethically consequential act, the chapter contributes to a sociological critique of algorithmic objectivity and advocates for reflexive, culturally aware principles in the design of educational AI.