End users engagement with GenAI
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Authors
Wijayapala, L.
Kabbar, Eltahir
Barmada, Bashar
Kabbar, Eltahir
Barmada, Bashar
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Date
2024-09-19
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Conference Contribution - Oral Presentation
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
Generative AI
human-computer interaction
prompt engineering (computer science)
digital literacy
educational technology
natural language generation (computer science)
large language models
human-computer interaction
prompt engineering (computer science)
digital literacy
educational technology
natural language generation (computer science)
large language models
ANZSRC Field of Research Code (2020)
Citation
Wijayapala, L., Kabbar, E., & Barmada, B. (2024, September 19). End users engagement with GenAI [Paper presentation]. Te Manawa Reka: Curiosity Symposium, Tangatarua Marae, Mokoia Campus, Toi Ohomai Institute of Technology, Rotorua, New Zealand.
https://hdl.handle.net/10652/6852
Abstract
Large Language Models (LLMs) are powerful artificial intelligence (AI) systems that promise to create new and innovative user experiences and applications in different contexts, including education. The integration of AI into everyday life is rapidly increasing. As LLM technologies, such as ChatGPT, become increasingly prevalent in educational settings, they can disrupt conventional teaching and learning methods rooted in legacy learning by offering personalized, dynamic, and interactive learning experiences.
The presentation aims to report the work in progress of an experimental study investigating user engagement with LLMs using a real-world scenario to understand better the prompts end users use when interacting with LLMs. The study uses a multi-case methodology and collects data from a sample (n=30) of participants who are asked to use ChatGPT and complete a predefined task. This is followed by a short survey to collect data about their background and ChatGPT usage experience. Then, the researchers collected the prompts the participants used for further analysis.
The collected prompts are analysed using several analytical techniques, such as lexical, semantic, and other qualitative measures, to gauge users’ GenAI proficiency levels and abilities. This analysis aims to uncover how end users’ prompts proficiency influences their user experience and interaction with GenAI tools. The results of this study will provide valuable insights into user engagement with generative AI tools, the metacognitive skills of the participants, their level of ChatGPT satisfaction, and the factors influencing their use of LLMs. The study findings will inform researchers and practitioners interested in end users' engagement with GenAI, including educational researchers and practitioners.
What is GenAI used for?
Rate of GenAI adoption in the workplacein the USA 2023, by industry
Examples of Gen AI application using LLMs
How to get the most out of GenAI?
Prompt engineering
Research objectives
Research method
The current stage of the study
Theoretical and practical implications
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