In this section, we examine the complex challenges of generative AI in higher education, including accessibility and the digital divide, and equity and bias. Environmental impacts add further complexity as institutions wrestle with the balance between innovation with sustainability. Some educators may thoughtfully resist AI integration, contributing valuable perspective to debates about technology’s role in academia. Understanding these interconnected challenges is essential for making informed decisions about AI in higher education.

This section is divided into four chapters:

Access and Accessibility

As generative AI tools become integrated into higher education, they create both new possibilities and barriers for accessibility. While these technologies can assist students with disabilities through features like text-to-speech and simplified explanations, they also raise concerns about the digital divide. Access often depends on reliable internet, financial resources, and technical literacy, while many AI interfaces themselves present challenges for users with various disabilities. [COMING SOON]

Equity and Inclusion

Generative AI’s relationship with educational equity presents a double-edged sword. These tools can provide personalized learning experiences and support for some students, yet they often reflect and amplify existing societal biases through their training data. The cost of premium AI services and required technology can further widen educational gaps, particularly affecting equity-denied students. [COMING SOON]

Environmental Impacts

The environmental cost of generative AI extends beyond its visible benefits in education. Training these models requires massive computational resources, contributing significantly to energy consumption and carbon emissions. As universities adopt these technologies, they must balance educational advantages against sustainability goals, considering both the direct energy usage and the environmental impact of required devices.

Resisting GenA.I.

Some educators and institutions may thoughtfully choose to limit or reject AI tools based on pedagogical principles, ethical concerns, or commitment to traditional teaching methods. This resistance represents a legitimate perspective in educational discourse, rooted in considerations of academic integrity and the fundamental purpose of higher education. [COMING SOON]