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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 Gareth Hoskins (tgh@aber.ac.uk) from DGES in this blogpost.
This was a classroom evaluation of an AI-generated summary of the scientific concept ‘flashbulb memory’ as part of a lecture on ‘individual memory’ in the 3rd year human geography/sociology module GS37920 Memory Cultures: heritage, identity and power.
I prompted ChatGPT with the instruction: “Create a 200-word summary of the concept of flashbulb memory”, created a screengrab of the resulting text and embedded this within my lecture slides giving the class 3 minutes to read it and discuss it on their tables asking specifically for responses to the questions:
Discussion didn’t touch too much on the questions I posed but focused more on the ChatGPT content where students were much more critical of the content than I had anticipated. They noted the dull tone, the repetition, uncertainty surrounding facts presented the vague approach and general lack of specificity. Those students showed a surprising degree of GenAI literacy which was conveyed to the class as a whole. During the discussion, the students became more aware of the utility of GenAI tools, more comfortable speaking about how they use it and might go on to use it, and how its limitations and weaknesses might affect the content it generates.
I developed the exercise using UCL guidance webpage ‘Designing Assessments for an AI-enabled world’ https://www.ucl.ac.uk/teaching-learning/generative-ai-hub/designing-assessments-ai-enabled-world and re-designed my exam questions on the module to remove generic appraisals of famous academics’ contributions to various disciplinary debates and substitute with hypothetical scenario-based questions that were much more applied.
My intension was to acknowledge that we exist in an AI-enabled world which creates opportunities but also problems for learning. I also used the exercise to introduce the risks relating to assessment and outline my own strategy for assessing on this module using real-life problem-based seen-exam questions requiring use of higher-level skills of evaluation and critical thinking applied to “module-only” content and recent academic publications which GenAI essay-writing tools struggle to access.
The activity helped students become more familiar with the use GenAI as a “research assistant” (for creating outlines and locating sources) and created an environment for open discussion about the limitations of AI-generated content in terms of vagueness, hallucination, lack of understanding, and lack of access to in-house module content on Blackboard or up-to-date research (articles published in the last two years).
I would flag other systems including DeepSeek, Gemini, Microsoft Co-Pilot and Claude AI as well as discuss their origins, pros and cons, and crucially caution about environmental and intellectual property consequences.
Keep a lookout for our next blogpost on Generative AI in Learning and Teaching case studies.
Between the 8-10 July, the Learning and Teaching Enhancement Unit hosted the 13th Annual Learning and Teaching Conference.
The materials from the conference are now available on our webpages.
We would like to thank all of our contributors and attendees. The sessions were of such high quality.
We’re already heading into planning our 14th Annual Learning and Teaching Conference which is scheduled to take place in September 2026 (date TBC).
We hope to see you at a forthcoming event.
We are delighted to announce our training series for the forthcoming semester.
All training can be booked online using your Aberystwyth username and password. Our training booking system is now automated, so you will receive your calendar invitation within an hour into your calendar. Please join these sessions from your Outlook calendar.
If you have any questions, please contact us: elearning@aber.ac.uk.
As usual, our training sessions are grouped into 3 series:
In addition to the usual offerings, we also wanted to highlight the new sessions that we have introduced for 2025-26:
This session will introduce colleagues to Generative AI and offer the opportunity to think about ways in which you can incorporate Generative AI into your learning and teaching practice.
You can book your place on all the other sessions in this E-learning Essentials series via this link.
A reminder that all Essentials sessions are strongly recommended for any new members of staff in your department.
Blackboard Documents have had a complete overhaul in Ultra. This 30-minute pro session gives an overview of the new features and allows you to give it a go in your course.
We’ve combined our Discussions and Journals session into one. We’ll go through activity design for our interactive tools to help maximise student engagement.
We will look at the analytical tools available in your Blackboard course to help monitor student engagement. We’ll use this to tailor messaging as well as creating other activities such as knowledge checks and learning module progression to help keep your students engaged with their learning.
One of the features of Turnitin is PeerMark which allows you to create peer assessment opportunities for your students. This is great to allow students to provide formative feedback on each other’s work.
Want to spruce up your recordings? This session will showcase different ways in which you can use Panopto: from inserting quizzes mid recording, to give students the opportunity to get creative and use Panopto themselves. This session is great for those adopting a flipped classroom approach or who want to make use of Panopto beyond Lecture Capture.
Other sessions include the Blackboard AI Design Assistant and Advanced Vevox polling software design.
You can book your place on all the other sessions in this E-learning Enhanced series via this link.
We’ve designed 4 new workshops for colleagues based on the 4 areas of the Exemplary Course Award. Looking at each aspect, colleagues will reflect on how their own courses can be developed.
The 4 sessions are:
You can book your place on all the other sessions in this E-learning Excellence series via this link. Other sessions include Submitting an Exemplary Course Award.
If there are any other training topics that you’d like us to consider for Semester 2, please contact us.
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 Megan Talbot (met32@aber.ac.uk) from the Department of Law and Criminology in this blogpost.
We designed an assessment to improve AI literacy skills in our family law module.
The students were given a normal essay question: “To what extent should British law recognize prenuptial agreements?”.
They were also presented with the response of ChatGPTo1 to the same question.
The students were advised that their objective was to write an essay in response to the question. They were free to use the AI response in any way they wanted, they could build off it, use it as a starting point for research or totally ignore it, whatever they prefer. They were told that we would not tell them how the AI essay would score if they submitted it with no modification, but they were free to do that if they wished (none did).
We explained that with the increased use of AI tools they will not only need to be able to use AI outputs competently and responsibly, but also will need to demonstrate that they can add value that an AI cannot. Therefore they should view the task as trying to show that they can perform better than the AI.
The students generally did very well. We recorded fewer failing marks (below 40%) than previous years, as well as fewer marks below 50%. Very high performing assignments tended to use the text provided by the AI far less than those scoring lower.
They were provided with the normal assignment briefing sheet, as well as a lecture session on how to approach the assessment. The briefing document included more guidance than normal to help overcome any uncertainty as to how to approach the assessment. This included spesific guidance on things they may be able to do to improve on the AI answer, such as more use of case law, evidence of understanding the caselaw, examining more critical arguments advanced by academics and looking at the peer reviewed literature and writings by legal professionals. Students were also specifically warned about hallucinations (the tendency of AI to provide false information in a way that appears “confident”) and the need to fact check the AI if they were going to rely on it.
We received a number of questions from higher performing students asking “do I have to use the AI response”, to which we responded “no”. Students generally seemed uncertain as to what they were allowed to do despite a great deal of guidance given in the initial briefing document and accompanying lecture.
Unfortunately, a significant number of students were tripped up by failing to factcheck one of the case descriptions that ChatGPT used, which was inaccurate. Feedback was left on those essays to remind them of the need to factcheck AI resources.
We did not survey the students on this assignment specifically, but in the SES several of them reported that they found it very useful in understanding the limitations of AI. In conversation, a number of students said it helped them overcome initial procrastination, as they were given a starting point to build from. Higher scoring students reported reading the AI output, but doing their own research and writing as normal, only referring to the AI to make sure that they did not ignore any core points by mistake.
We are considering reducing the length of the essay and incorporating a small reflection on their use of AI as a part of the assignment. Additionally, we will be elaborating on the warning to factcheck AI outputs to specifically mention that real cases may be cited but be given misleading or false descriptions or may be cited to support points not addressed by the case.
Keep a lookout for our next blogpost on Generative AI in Learning and Teaching case studies.
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.
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.
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.
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.
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.
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.
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.
Library and Learning Services, Academic Registry, and UndebAber are collaborating on Generative AI guidance and advice.
Following approval at the recent Education and Student Experience Committee, we are pleased to share these resources with you ready for next academic year.
This statement outlines the principles and approach that AU intends to use for Generative AI across all its operations.
This document provides guidance and advice to students on how they might want to use Generative AI as a study tool. This document uses a traffic light system approach to alert students to the amount of caution they might want to apply in its use.
A statement has been added to the Blackboard course template for 2025-26 Courses providing advice and guidance for students on acceptable use of Generative AI and where to get support and help.
You can copy Generative AI assessment statements into your Blackboard course to communicate to students the acceptable use of Generative AI on the assessment. See our blogpost for further information on how to do this.
Designed by the Department of Law and Criminology, and already being used by some departments, this amended Tool Use statement allows students to outline how they have used Generative AI in their assessments. Students fill out the form and insert the tool use statement into their word document before submission.
The tool use statement can be downloaded from our webpage and uploaded to Blackboard.
There is a dedicated webpage for Generative AI advice and guidance where we place our support materials and advice.
We have consulted widely with colleagues and students on this matter, and we’d like to thank those who have helped shape this guidance.
Please direct staff enquiries to elearning@aber.ac.uk or contact your subject librarian.
We’re pleased to announce that Generative AI Assessment Statements are now available in the Blackboard Learning Object Repository.
This is part of the work that Library and Learning Services are undertaking in collaboration with UndebAber and Academic Registry.
The aim of this work is to make it clear to students the expectations regarding their engagement and use of Generative AI in learning and teaching.
There are three statements available in the Learning Object Repository:
Each of the statements gives advice and signposts students to additional support.
Colleagues can copy these statements into the relevant area of the course. As acceptable levels of use of Generative AI vary between individual assessments, it’s recommended that the statements are copied into the relevant assessment folder.
For further information see: How do I add an item from Blackboard’s Learning Object Repository to my course?
In addition to the Generative AI Assessment Statements, a Generative AI Tool Use Statement is also available. This statement has been developed by colleagues in the Department of Law and Criminology and gives students the opportunity to outline how they have used Generative AI in their assignments.
If you have any questions about using the Learning Object Repository, please contact elearning@aber.ac.uk.
We are delighted to announce the programme for the University’s 13th Annual Learning and Teaching Conference.
The Conference is taking place between Tuesday 8 and Thursday 10 July.
Tuesday 8 July will be online, with sessions in person on Wednesday 9 and Thursday 10 July.
You can see the full programme on our webpages and you can book your place online.
This year’s conference boasts a number of external speakers.
Keynote speaker:
Dr Neil Currant will be offering a keynote presentation on Compassionate Assessment. There will also be a masterclass workshop with colleagues being able to apply these principles to their own scenarios. See our blog update for further information.
External speakers:
We’ve got three other external speakers lined up. For further information, click the links below:
In addition to that, we’ve got some amazing sessions from colleagues showcasing the cutting-edge teaching practices taking place at the University.
Topics include:
The programme has been designed to support learning and teaching priorities and initiatives for the forthcoming academic year.
If you have any questions, please contact us on elearning@aber.ac.uk.
We’re looking forward to seeing you in July.
We’re delighted to confirm our next external speaker for this year’s Annual Learning and Teaching Conference.
We’ve previously announced our keynote, Neil Currant, and Professor Lee Elliot Major and Beth Brooks from the University of Exeter, and Higher Education Partners.
Now, we’ve also got Professor John Traxler joining us for a special panel discussion on Generative AI.
This session will be taking place on the afternoon of Tuesday 8 July via Teams.
John Traxler, FRSA, MBCS, AFIMA, MIET, is Professor of Digital Learning, UNESCO Chair in Innovative Informal Digital Learning in Disadvantaged and Development Contexts and Commonwealth of Learning Chair for innovations in higher education. He is Academic Director of the Avallain Lab, leading research on ethical and pedagogic aspects of educational AI. His papers are cited around 12,000 times and Stanford continues to list him in the top 2% in his discipline. He has written over 40 papers and seven books, and has consulted for international agencies including UNESCO, ITU, ILO, USAID, DFID, EU, UNRWA, British Council and UNICEF.
He was a pioneer of mobile learning, starting in the 2000s with technology and pedagogy but, in the 2010s, concerned with the impact and consequences on societies, cultures and communities of mobility and connectivity, and on the nature of disadvantage. He is interested in the impact of AI on global and individual disadvantage and the decolonisation of the digital technologies of learning and education.
Our full programme will be announced in due course.
Bookings for the conference are already open.