We are working on a series of case studies to share practices of using Generative AI in Learning and Teaching Activities.
In this series of blogposts, colleagues who are using Generative AI in their teaching, will share how they went about designing these activities.
We’re delighted to welcome Dr Panna Karlinger (pzk@aber.ac.uk) from the School of Education in this blogpost.
Case Study # 1 – ResearchRabbit
What is the activity
This activity is focused around finding reliable academic sources for students to use in their coursework. The students are invited to use a ‘seed paper’ for an upcoming assignment to feed into ResearchRabbit, that uses machine learning to map related literature based on authors, citations, related topics or concepts. The students are then prompted to choose sources for their assignments, and critically evaluate these using the CRAAP test – checking the currency, relevance, accuracy, authors and purpose of the sources to pass a judgment on overall reliability before use.
What were the outcomes of the activity?
Students reported an increased confidence and ability to find academic sources and to demonstrate criticality within their work. Despite the vast resources and detailed guidance provided by both the teaching and library staff, students often struggle to find relevant sources to support their work, which was successfully addressed where students engaged with the activity.
How was the activity introduced to the students?
This activity was part of a key skills module, where students had prior knowledge of the CRAAP test, finding sources and had a discussion around and introduction to generative AI, the opportunities and risks involved as well as efficient and ethical use. Synthesising their prior knowledge, the tool was introduced as a demonstration, and then students used their own devices to find sources for a chosen, upcoming assignment for a different module.
What challenges were overcome?
Some students are still wary or skeptical about using AI, or fear being accused of unfair practice, so it was important to demonstrate use cases where they can use AI in a confident manner to help develop these skills. Some students did not have large screen devices on them and the activity was challenging to carry out on a phone, this has to be considered in the future, and some students require more hands-on guidance and support with the activity, this is largely down to digital skills and competence.
How did it help with their learning?
It reinforced some messages about critical AI literacy, evaluating output and sources in general, reminding them of the importance if criticality in their work, and finding further and often more up-to-date information and resources helped inform the coverage and evaluations in their assignments where students engaged as expected.
How will you develop this activity in the future?
As we no longer teach the key skills module, there is an opportunity to embed this into other modules, for instance in assignment support sessions or optional drop-ins. This allows for smaller groups of students and more one-to-one time as necessary, which could make this activity more successful; given that the students received the necessary guidance from the department on the use of AI. This could also be part of research methods modules or guidance we give to PGRs, as this resource is not only free, but also has more advanced capabilities compared to similar literature mapping tools, which was be valuable to anyone working on a dissertation or thesis.
Keep a lookout for our next blogpost on Generative AI in Learning and Teaching case studies. If you are using Generative AI in your teaching practice and would like to submit a blogpost, please contact elearning@aber.ac.uk.