“AI allows all students, despite the way they learn, to understand your course materials.”

Cheating is, of course, a major problem in colleges. Professors report a dramatic rise in AI-generated writing and other forms of misuse. Yet another, equally profound, change is taking place under the radar: Increasingly, students are turning to artificial intelligence as an all-purpose study tool, recasting how they think about learning and reshaping their relationships with classmates and professors.

For many students, AI has been a godsend, helping them overcome learning deficits or poorly taught courses. Others appreciate the tools’ efficiency. In a fast-paced world, where students might be juggling a full course load and working 20 hours a week or more, speed is everything. And while they are bothered by the idea that professors assume students just use AI to cheat, they are often unsure whether they are cheating by using AI. Even students don’t talk much about these gray areas, students say, other than through jokes about “ChatGPTing” an assignment.

Learning Outcomes

Students will…

• Increase knowledge of how AI tools work
• Better understand ethical considerations and biases in AI tools
• Consider potential risks and values of using AI tools
• Increase digital adaptability

Learning Outcomes

1. Students will be introduced to the machine bias inherent in algorithmic decision making, with a focus on information systems.
2. Students will discuss the effects of algorithm bias in order to articulate how some individuals or groups of individuals may be misrepresented or systematically marginalized in search engine results.
3. Students will develop an attitude of informed skepticism in order to critically evaluate search results.

Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.