Generative AI is a hot topic these days, with constant chatter regarding the potential and pitfalls of using it happening both online and offline. It remains to be determined how it may be used for learning and teaching, with ongoing exploration and discussion. However, generative AI is only one type of AI and there’s plenty of applications outside of it that could be used for teaching and learning as well. With the growing potential of AI, instructors should endeavor to establish and develop their understanding of it so they’re better prepared for future applications of artificial intelligence in the classroom and training, especially if they’re struggling with misconceptions around artificial intelligence and their perceived capabilities. Whatever comes in the next ten years, twenty years, beyond: it remains to be seen how disruptive generative AI could be, how transformative AI could be in the teaching and learning process.
Faculty currently contend with the potential for academic dishonesty when students use generative AI, bypassing the vital development of knowledge and comprehension. Generative AI has a number of issues that could lead to flawed submissions, including potential copyright issues, erroneous information presented as factually correct, and perpetuation of potential biases that exist in the dataset. Furthermore, if students fail to adequately understand the given material, they risk complications in their path of study or chosen careers. This also threatens the quality of education that schools profess to offer: if students routinely cheat using AI or other related tools, the entire integrity of the program is put at risk.
Librarians in higher education often have instruction as part of their core responsibilities, teaching students how to research using library resources. One aspect is the evaluation of information sources they find during the research process, with a discussion of several factors they should consider before utilizing the information in their assignment. Generative AI could potentially help supplement the research process or help students build their critical thinking skills, rather than bypassing the whole process altogether to generate a completed albeit flawed product.
Teaching students how to appropriately use generative AI could prove important or perhaps vital, not only in order to avoid potential academic dishonesty, but to help establish the need to use AI or similar tools ethically in the first place. This will help demonstrate the capabilities of artificial intelligence in supplementing or aiding the work they do, without entirely rendering the learning process null and void. This could include potential teaching activities such as asking the generative AI to explore the background of a given topic and help devise searching strategies to elucidate further information from credible sources through the generation of search terms. Another potential activity could have the instructor input a writing sample into the generative AI, ask it to evaluate the writing based on the criteria given, and have it provide constructive feedback to help improve the final product. Librarians and instruction designers could also collaborate with faculty to create new assignments that would prove difficult to complete with generative AI, to better gauge students’ learning and understanding.