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Research Highlights

  • The Problem: Academics face a specific subset of biases toward Artificial Intelligence (AI) rooted in a lifetime of conditioning that equates legitimacy with "brute-force" effort and rigorous, independent human labor. 

  • The Method: The researcher employed a vulnerable self-interrogation and critical narrative methodology, drawing on Pierre Bourdieu’s frameworks of habitus and capital to analyze personal classroom experiences and assessment failures during 2024 and 2025. 

  • Quantitative Finding: In a class of 26 students, only four unique quotes were initially selected from an assigned novel, with 24 students sharing just two quotes; following an intervention, the number of unique responses increased slightly from 4 to 6, though 11 students still selected the exact same quote. 

  • Qualitative Finding: Academic resistance to AI is identified as a form of "résistance" linked to academic "wounds" from past struggles; emerging themes for adapting assessment include the shift toward audio-recorded explanations, paper-and-pencil testing, and metacognitive creative assignments such as image or collage generation. 

  • Finding: Successful integration of AI in post-secondary education requires a philosophical shift from prevention to partnership, focusing on differentiating assessment formats to accommodate diverse student needs and survivability.

Abstract

Being black, brown, caramel, immigrant, French speaking, divorced, mother of six, economically disadvantaged, neurodivergent, foster care graduate, survivor of abuse, child of divorce, and health-impacted person, I’ve often examined the wheel of power and privilege with sadness and dismay. In contrast, being white, catholic, intellectually advantaged, athletic, American, Canadian, middle-class, mathematics mastermind, and Doctor of Education, I’ve needed to examine this wheel fiercely as I come face-to-face with my privilege. Through reflection and self-interrogation, I carefully and imperfectly consider how my bias informs me of my disposition towards others, my educational privileges, and my experience in the world of academia. In this paper, I offer reflections on a specific form of bias from a specific set of people: AI and academics. These reflections serve to vulnerably disclose how I continue to interrogate my bias towards AI and to offer opportunity for other academics to do the same.

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Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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