How do we assess the authenticity of dissertations in light of AI?

This blog is part of our latest Dynamic Conversation - click here to explore the full collection

Introduction

This conversation aims to consider how we might assess the authenticity of undergraduate and postgraduate HRM dissertations in light of the emergence of large language models (LLMs) such as ChatGPT. We suggest we must insist on viva voce to truly examine the authorship and ownership of student research and it seeks to explore some practical approaches to assessing the credibility of student dissertations. We look forward to engaging with the community on this important matter.

Our opinion on the dynamic conversation

The advent of LLMs represent a significant leap forward in natural language processing and ChatGPT in particular, has taken the higher education sector by storm since its release in November 2022. In the context of research dissertations, LLMs have profound implications as they may be used at each juncture of the research process – from objective development, to the crafting of a literature review (Schwenke et al, 2023); they are even capable of analysing and interpreting qualitative (Morgan, 2023) and quantitative data (Ishaq, Jhatial & Parveen, 2023). Ergo, as educators we are concerned about the future viability of research projects and the consequential issues LLMs create in assessing the authenticity of students’ dissertations.

We are suggesting that in order to assess the veracity of students’ work, viva voce need to be introduced for all and to implement a system whereby students must hold a minimum number of meetings with their supervisors.

The justification and rationale for this opinion

While many believe Artificial Intelligence (AI) can play a positive role in addressing some of the key challenges in education today (Dempere et al, 2023; Garcia-Penalvo, 2023; Holmes & Miao, 2023; Liu, Bridgeman & Miller, 2023), there are also those, such as us, who are anxious that LLMs have serious consequences for academic integrity (Rudolf et al, 2023). We are keen to continue to include capstone research projects on our programmes, but to do so we need to ensure that students are actively engaging in the process. In addition, we need to maintain and protect the development of students’ higher order thinking skills over an effortless process where a student creates a simple and clear prompt and then slightly changes the text it produces.

The key questions for debate

How should we assess student dissertations?

Continue the conversation...

References:

Dempere, J., Modugu, K. P., Hesham, A., & Ramasamy, L. (2023). The impact of ChatGPT on higher education.

Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ, 8, 1206936.

García-Peñalvo, F. J. (2023). The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or panic? Education in the Knowledge Society, 24, 1-9.

Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.

Ishaq, K., Jhatial, S., & Parveen, F. (2023). Enhancing Statistical Understanding: A Brief Discussion on the Role of ChatGPT in Business Management. Journal for Business Education and Management, 3(2), 59-76.

Liu, A., Bridgeman, D., & Miller, B. (2023, February 28). As uni goes back, here’s how teachers and students can use ChatGPT to save time and improve learning. The Conversation. https://theconversation.com/as-uni-goes

Morgan, D. L. (2023). Exploring the use of artificial intelligence for qualitative data analysis: The case of ChatGPT. International journal of qualitative methods, 22, 16094069231211248.

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of applied learning and teaching, 6(1), 342-363.

Schwenke, N., Söbke, H., & Kraft, E. (2023). Potentials and Challenges of Chatbot-Supported Thesis Writing: An Autoethnography. Trends in Higher Education, 2(4), 611-635.