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The higher education context

Generative AI (GenAI) giving rise to challenges and opportunities for both staff and students (Xia et al, 2024). Should staff and student use or not use GenAI and, if so, where and when?

In parallel, higher education is striving to be more inclusive in its assessment and feedback practices (Ajjawi et al, 2023; Bain, 2023; Tai et al. 2023).

The term ‘assessment for inclusion’ (AfI) is beginning to be used to describe the approaches that aim to give all students the opportunity to succeed (Ajjawi et al, 2023).

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(UCD T&L, 2025)

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Qualitative Research Study

Participants:

17 Faculty (who were students)
Doing an ‘Assessment & Feedback in Higher Education’ module
On a professional University Teaching Qualification programme
In University College Dublin, Ireland
Full class = 32 students.

Research Data:

17 assignments and
10 in-depth interviews

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Choice to use, or not use, GenAI

in their final assignment (N=17/32)

N= 9

N= 3

N= 5

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Data Analysis through the lens of an Assessment for Inclusion Framework

(UCD T&L, 2025)

Deductive coding:

used as a lens to go back to the Interview (n=10) and assignment (n=17) data.

(Braun and Clarke 2014)

Image Source: https://delvetool.com/blog/deductiveinductive

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MANAGEABILITY

‘Manageability’ of assessment present a contradiction:

while some studies suggest GenAI can save time during assessment tasks

(Lee & Moore, 2024)

this research found that some faculty (students) emphasised the significant time investment required to effectively learn and utilise GenAI

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More work for them, on this assignment

‘…..I would nearly have to go do it again, but in a different way……..I was used to doing approaching essays.., doing the research, making notes, putting them in the orders, having my thoughts and headings. But then I realised what I would then have to do is actually do another assignment. …I actually thought it (Use of GenAI) was going to be double. ……..……….. I actually just didn't have time to be honest.’

ID 8, Interview,

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However sometimes less work for students

‘I think that big shifts like this (GenAI) offer us a very rich opportunity to think about what assessment is for, would be my message there. We can keep going as we're going, and I think on the whole, we both over assess and poorly assess our students. I mean. In general, there are fantastic examples of where people do wonderful things. But I see students being continuously stressed by the amount of work they're being asked to do, and they lose the reasoning for why they're there, because they're just over busy.’

ID 10, Assignment,

‘The AI-generated content provided a valuable starting point for further exploration and analysis, saving time and effort in research and information gathering, especially when tasks are not very sophisticated.’

ID 3, Interview,

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SCAFFOLDING

GenAI has potential to help many students who struggle with writing to ‘scaffold’ their assignments and is one of the recognised value its use with students (Perkins et al, 2024).

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Scaffolding writing

‘I would also have a written element, because I think it's something that in architecture often, I mean, there is a certain amount of writing, but it's not ..to 4th year that you're really getting into writing. And so a lot of students don't have confidence in that. So it'd (GenAI) be really good to get them to be able to articulate in words things that they're so again, it might be a thing of getting them to kind of compose little pieces of writing within the class, formalize later,.......’

ID 6, Interview,

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Scaffolding group-discussions

‘Since generative AI is often used to facilitate the easy construction of chatbots (Jo, 2023) it is easy to see that they could become a planned or unplanned “teaching assistant” in classroom group discussions. If properly designed and deployed they could themselves be a form of scaffolding for the students.

Two questions that arises in this case: i) how do we ensure that they fade from the student’s engagement with the task at hand and ii) how do we ensure that there is a transfer of responsibility to the student? Thus if there is a potential problem with 2 of the 3 key characteristics of scaffolding according to Van de Pol et al. (2010), we must tread carefully when deploying generative AI for this task.’

The assignment’s references

Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216. https://www.nature.com/articles/d41586-023-00340-6.pdf

van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational Psychology Review, 22, 271-296.

ID 15, Assignment,

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AUTHENTICITY

GenAI can be weaker on ‘authenticity’ in assessment as it sources from more generic data.

As a consequence more ‘authentic’ assessments are being used as a means of deterring GenAI use, a more inclusive approach (Smolansky et al., 2023).

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Use on the job

‘One solution I have in mind is to explicitly allow students to use GenAI as a thought partner, as this affords another way to diversify the assessment and may improve traditional assessment practices (Swiecki et al., 2022). This solution is appealing because it is also a possibility students may later use on the job’

The assignment’s references

Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N. & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075.

ID 13, Assignment,

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DIVERSITY

Both the GenAI research on assessment (Xia et al, 2024) and assessment for inclusion research (Bain, 2023) advocate for more diverse approaches to assessment.

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Danger of Decreasing Diversity

‘There's a particular lecturer……... there was an exit exam, but the assessment component before that was the students would draft a portfolio of a particular part of the limb that they were interested in, and clinical implications and such, and it would have involved some texts students would have to generate.

So again, generative AI is implicated there and pictures generative AI. It's I don't know how good it is yet at producing anatomical detail to the point that an anatomist can't tell that it's AI. But at least in the future could be. But that particular lecturer is retiring the portfolio. And perhaps because of AI.’

ID 16, Interview,

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TRANSPARENCY

GenAI can support the development of rubrics, which can help transparency, clarity of assessment’s expectations (UCD Teaching & Learning, 2025b)

However there also need to be transparency around how it is used, not used, in the assessment process (i.e. Perkins et al. 2024 levels of use)

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TRANSPARENCY

‘ But I think they (students) do still want to and be a bit clearer about what the guidelines are. If people choose to use AI in it and because it can, you know, it can still be done, can still be used in various ways to help with the research or whatever, and I don't at the moment there isn't…those guidelines around what you can do or what you can't do, or how you need to reference things properly if you do use it. So yeah, I'll definitely try and do that for next year ‘

ID 1, Assignment,

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TRANSPARENCY (con’t)

‘These suggestions can be implemented to increase transparency and make individual evaluation more fair. Students should be made aware that copying text directly from Gemini or another GenAI output is considered plagiarism. Indeed, the exercise of using GenAI tools also helps students learn the rules of ethical usage of these types of platforms [Mao2024].

A clear rubric for the authentic assessment, including the use of GenAI as a thought partner, is paramount to the perceived fairness of the assessment. Assessment of an oral presentation is likely to be swayed more easily by subjective factors, so a clearly formulated rubric that adheres particularly to the principles of “transparency”, “comparability and consistency”, and “reliability” [Bloxham2008] will minimize risk of biased assessment. ‘

The assignment’s references

Bloxham, S. and P. Boyd. (2008) Developing effective assessment in higher education: A practical guide. Maidenhead: Open University Press McGraw-Hill.

Mao, J. Baiyun, C. and Liu, J.C. (2024) Generative Artificial Intelligence in Education and Its Implications for Assessment. TechTrends 68:58–66. https://doi.org/10.1007/s11528-023-00911-4 )

ID 2, Assignment,

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EQUITY / FAIRNESS

The different technologies used in GenAI in assessment are vast, however, many institutions are financially restricted to a limited choice of technologies that all their students can access.

Lack of access for students to the more powerful tools can create an imbalance in ‘equity’ , a key element of assessment for inclusion (UCD T&L, 2025).

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Others using it… but not them…

Her student said

"I know several of my peers used AI to write their papers, and I didn't. And basically I should get points for my mediocre paper because it was, you know, artisanally crafted by me."

‘you know my niece said to me a few weeks ago. Like all of her peers are using it (GenAI) to write their assignments, and she just feels like it's so unfair, …..’

ID 7, Interview,

ID 5, Interview

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EQUITY and ACTIVE CITIZENSHIP

Financial cost of GenAI

‘What's driving it (GenAI) is making a quick buck. And so, especially when stuff is unregulated, you're going to get these unforeseen and hopefully unintended consequences of Gen AI telling people that one small rock a day is good for your health, you know?

ID 7, Interview,

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SUMMARY:

GenAI can both support and hinder inclusive assessment

Findings on the ‘manageability’ of assessment present a contradiction
GenAI can scaffold writing for struggling students, but need to consider when to remove the scaffolds
GenAI's generic outputs have prompted a shift toward more 'authentic' assessments
Financial constraints limit institutional access to diverse GenAI tools (lack of equity)
Both GenAI and inclusion research advocate for assessment diversification, but some evidence of retreat back to exams
Unauthorised use of tools undermines students’ perceptions of equity/fairness
Effective GenAI use requires transparency, clear instructions, and evaluation criteria

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References & Bibliography

Ajjawi, R., Tai, J., Boud, D., & Jorre de St Jorre, T. (Eds.). (2023). Assessment for inclusion in higher education: Promoting equity and social justice in assessment. Routledge. https://doi.org/10.4324/9781003293101

Andrews, R. (2003). The end of the essay? Teaching in Higher Education, 8(1).

Bain, K. (2023). Inclusive assessment in higher education: What does the literature tell us on how to define and design inclusive assessments? Journal of Learning Development in Higher Education, (27). https://doi.org/10.47408/jldhe.vi27.1014

Braun, V., Clarke.V. ( 2014) Successful Qualitative Research: A Practical Guide for Beginners, Sage.

Crawford, J., Cowling, M., & Allen, K. A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3). https://doi.org/10.53761/1.20.3.02

Hanesworth, P. (2015). Embedding equality and diversity in the curriculum: A model for learning and teaching practitioners. Higher Education Academy.

Jackson, R. H. G., & Williams, M. R. (2003). Are undergraduate preferences as to method of assessment well-founded and rational? (Research paper 2003-1). University of Wales.

Lee, S. S., & Moore, R. L. (2024). Harnessing generative AI (GenAI) for automated feedback in higher education: A systematic review. Online Learning, 28(3). https://doi.org/10.24059/olj.v28i3.4593

McArthur, J. (2021, October 13). Creating synergies between assessment for social justice and assessment for inclusion [Symposium presentation]. CRADLE Symposium.

Morris, C., Milton, E., & Goldstone, R. (2019). Case study: Suggesting choice: Inclusive assessment processes. Higher Education Pedagogies, 4(1), 435-447. https://doi.org/10.1080/23752696.2019.1669479

Nieminen, J. H. (2022). Assessment for inclusion: Rethinking inclusive assessment in higher education. Teaching in Higher Education, 1-19. https://doi.org/10.1080/13562517.2021.2021395

O'Neill, G. (2017). It's not fair! Students and staff views on the equity of the procedures and outcomes of students' choice of assessment methods. Irish Educational Studies, 36(2), 221-236. https://doi.org/10.1080/03323315.2017.1324805

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O'Neill, G. (2023). Student choice of assessment methods: How can this approach become more mainstream and equitable. In R. Ajjawi, J. Tai, D. Boud, & T. Jorre de St Jorre (Eds.), Assessment for inclusion in higher education: Promoting equity and social justice in assessment (pp. 199-210). Routledge. https://doi.org/10.4324/9781003293101

O'Neill, G., & Padden, L. (2021). Diversifying assessment methods: Barriers, benefits and enablers. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2021.1880462

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36

Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023). Educator and student perspectives on the impact of generative AI on assessments in higher education. In Proceedings of the 10th ACM Conference on Learning @ Scale (L@S 2023).

Swaffield, S. (2011). Getting to the heart of authentic Assessment for Learning. Assessment in Education: Principles, Policy & Practice, 18(4), 433-449. https://doi.org/10.1080/0969594X.2011.582838

Tai, J., Ajjawi, R., & Umarova, A. (2021). How do students experience inclusive assessment? A critical review of contemporary literature. International Journal of Inclusive Education, 1-18. https://doi.org/10.1080/13603116.2021.2011441

Tai, J., Ajjawi, R., Bearman, M., Boud, D., Dawson, P., & Jorre de St Jorre, T. (2023). Assessment for inclusion: Rethinking contemporary strategies in assessment design. Higher Education Research & Development, 42(2), 483-497. https://doi.org/10.1080/07294360.2022.2057451

Tai, J., Dollinger, M., Ajjawi, R., Jorre de St Jorre, T., Krattli, S., McCarthy, D., & Prezioso, D. (2023). Designing assessment for inclusion: An exploration of diverse students' assessment experience. Assessment & Evaluation in Higher Education, 48(3), 403-417. https://doi.org/10.1080/02602938.2022.2082373

UCD Teaching & Learning. (2025). UCD Assessment for Inclusion framework. UCD Dublin. https://www.ucd.ie/teaching/resources/inclusiveandinterculturallearning/ucdassessmentforinclusionframework/

UCD Teaching & Learning. (2025b) DESIGNING GRADING & FEEDBACK RUBRICS .

Wu, X. (2010). Universal Design for Learning: A collaborative framework for designing inclusive curriculum. i.e.: inquiry in education, 1(2), Article 6. http://digitalcommons.nl.edu/ie/vol1/iss2/6

Xia, Q., Weng, X., Ouyang, Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(40). https://doi.org/10.1186/s41239-024-00468-z