Dialogic Reflection and Algorithmic Bias: Pathways Toward Inclusive AI in Education

Abstract

Artificial Intelligence (AI) systems typically inherit biases from their training data, leading to discriminatory outcomes that undermine equity and inclusion. This issue is particularly significant when popular Generative AI (GAI) applications are used in educational contexts. To respond to this challenge, the study evaluates the effectiveness of dialogic reflection-based training for educators in identifying and mitigating biases in AI. Furthermore, it considers how these sessions contribute to the advancement of algorithmic justice and inclusive practices. A key component of the proposed training methodology involved equipping educators with the skills to design inclusive prompts—specific instructions or queries aimed at minimizing bias in AI outputs. This approach not only raised awareness of algorithmic inequities but also provided practical strategies for educators to actively contribute to fairer AI systems. A qualitative analysis of the course’s Moodle forum interactions was conducted with 102 university professors and graduate students from diverse regions of the Dominican Republic. Participants engaged in interactive activities, debates, and practical exercises addressing AI bias, algorithmic justice, and ethical implications. Responses were analyzed using Atlas.ti across five categories: participation quality, bias identification strategies, ethical responsibility, social impact, and equity proposals. The training methodology emphasized collaborative learning through real case analyses and the co-construction of knowledge. The study contributes a hypothesis-driven model linking dialogic reflection, bias awareness, and inclusive teaching, offering a replicable framework for ethical AI integration in higher education.

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