GenAI in the hands of experts: A qualitative study of academics' experiences and future recommendations
Conference paper
Malik, M., Nortcliffe, A., Turner, S., Abdel-Maguid, M. and Shah, Rehan 2024. GenAI in the hands of experts: A qualitative study of academics' experiences and future recommendations .
Authors | Malik, M., Nortcliffe, A., Turner, S., Abdel-Maguid, M. and Shah, Rehan |
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Type | Conference paper |
Description | Creating robust multiple-choice questions can be perceived as a challenging task by academic staff. The design process can also be time consuming and rely on the knowledge and experience of staff. Many engineering educators make use of these questions for formative and summative assessments. These questions can be a great way to provide formative feedback to students and diagnose what teaching interventions may be needed for different students based on their performance on the questions. This work-in-progress paper describes some initial results from a study whereby augmenting the skills educators bring with Generative Artificial Intelligence (GenAI) tools such as ChatGPT was trialled. A short one-to-one workshop was delivered to individual participants from various Science, Engineering, Technology and Mathematics (STEM) disciplines. This paper also shares the GenAI-mind-bending iterative and sequential prompting technique used to co-produce outputs. Pre- and post- workshop surveys with open text questions and interviews are being used to capture staff perceptions of using these tools in creation of the questions. The paper concludes with recommendations for future research and ethical considerations, particularly concerning copyright issues. |
Keywords | Multiple-choice question design; ChatGPT; Generative AI; Human in the loop; AI mind-bending; Prompting |
Year | 2024 |
Conference | UK & Ireland EERN24 Education Research Network Annual Conference 2024 |
Official URL | https://epc.ac.uk/event/uk-and-ireland-engineering-education-research-network-annual-symposium-17th-18th-june-2024/ |
References | Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. |
Publication process dates | |
Deposited | 04 Sep 2024 |
https://repository.canterbury.ac.uk/item/98vzq/genai-in-the-hands-of-experts-a-qualitative-study-of-academics-experiences-and-future-recommendations
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