Why are Students Using Generative AI?

Understanding why students might decide to use AI for their coursework is critical to ensuring that we genuinely support learning, and that the institution meets its obligation to graduate employable and ethical citizens. Students might elect to use genAI for their coursework for numerous reasons, and it is important to understand that not all of these reasons are mischievous or with an intent to cheat. Some students may lack confidence in producing work entirely themselves, whilst others may not feel motivated or supported to do so. Indeed, scholarship on why students participate in academic dishonesty more widely suggests that the possible reasons can extend beyond the desire to achieve certain results to include: feeling inadequately prepared for assessments; caring more about results than learning; confusion around what constitutes academically dishonest behaviour; feeling like the behaviour is commonplace amongst their peers; or feeling a lack of connection to their studies or institution more generally (Bryzgornia, 2022). Some scholars have even raised the notion of ‘ethical cheating’ in reference to students collaborating, sharing knowledge/information/ideas and using open-source platforms precisely to develop 21^st century skills, yet in ways that might traditionally have been considered cheating (see Brimble, 2016).

In relation to AI specifically, a survey of US higher education students conducted by Best Colleges in March 2023 showed that students hold diverse views towards the use of AI in university coursework, ranging from those who actively use it to those who believe it should be prohibited in educational settings (Richards, 2023). In the same survey, 40 percent of respondents said the use of AI defeats the purpose of education, and 63 percent said AI cannot replace human intelligence or creativity. As discussed in the Assessment section of this guide, students deserve clarity around what is considered proper versus improper use of AI in their studies and for each assessment task. This includes when and how students are meant to disclose the use of AI tools, and any distinctions around expectations when it comes to AI use in text-based versus graphic-based formats.

As Siva Vaidhyanathan writes, “We have been dealing with cheating methods and technologies as long as we have been asking students to prove their knowledge to us.” Apart from clarifying university policies and expectations, it may be beneficial to discuss with students the use of AI by professionals and academics in the field, and the current set of ethical questions surrounding these practices. The key is to maintain an open dialogue with students about the use of AI in terms of what is permitted or restricted, versus what is encouraged or even required. Not only are tools and technologies certain to develop over time, institutional and personal stances towards AI are context-dependent. Generally, if students feel uncomfortable or discouraged to discuss their views or habits with staff, this can contribute to a problematic gap between teacher assumptions/expectations and learner practices. ­­The more educators and students can feel like they are working together to promote learning and professional development the better. As Ouyang and Jiao (2021) argue, the advancement of AI technologies does not ensure good education outcomes; rather, the long-term goal of AI use in educational contexts is to contribute to a paradigm where learners are supported and empowered to take agency for their own learning.