Generative AI in business education: Reimagining pedagogy or reinforcing inequity?
Original framing: “Generative AI in business schools: Friend or foe?” — Phys.org
The original framing omits the voices of educators and students from marginalized backgrounds who experience AI differently. It also neglects historical parallels with past educational technologies that failed to address systemic inequities. Indigenous knowledge systems and alternative pedagogical models are not considered in evaluating AI's role in education.
Low structural omission detected in mainstream coverage.
This narrative is largely produced by technologists, media outlets, and educational institutions with vested interests in AI adoption. It serves the agendas of tech corporations and neoliberal education models that prioritize efficiency and scalability over equity and critical pedagogy. The framing obscures the role of corporate influence in shaping educational content and pedagogical norms.
The debate over AI in education echoes past controversies over calculators, online learning, and MOOCs, where similar binary narratives emerged. Historically, these technologies often exacerbated existing inequalities rather than solving them, suggesting a pattern of technological determinism in educational reform.
The integration of generative AI in business schools is not a simple matter of adoption or rejection but a complex interplay of power, pedagogy, and equity.
By examining the historical patterns of technological integration in education, we see a recurring tendency to prioritize efficiency over justice. Cross-culturally, AI is being used in ways that support linguistic and cultural diversity, offering a contrast to the homogenizing tendencies in Western institutions. Indigenous and marginalized voices reveal the limitations of AI when it is not designed with inclusivity in mind. Scientific research shows that AI's impact is highly context-dependent, and future modeling suggests that the trajectory of AI in education will be shaped by the choices we make today. To move forward, we must embed ethical governance, equitable access, and diverse pedagogical models into the AI integration process, ensuring that it serves the broader goal of educational justice rather than reinforcing existing hierarchies.