Motivation factors for students using Generative AI

Abstract

Generative Artificial Intelligence (GenAI) has caused a shift in approaches to assessment and academic integrity in tertiary institutions. Recent research internationally and within Australasia underscores the need for responsible GenAI conduct and preparation for a future where work and education are shaped by GenAI technologies. However, ethical considerations often need to be made based on the impact on human research, intellectual property rights, and AI literacy in higher education. Academic integrity and ethical considerations should be used to balance hasty approaches to GenAI to ensure tertiary institutions provide inclusive learning opportunities. All of these impact on the need to understand what motivates student use of GenAI. Using the framework proposed by Bouteraa et al. (2024) as a model, this presentation applies data and results from recent literature to explore the factors which motivate use of Generative AI in a tertiary education context. These factors consist of Performance Expectancy, Effort Expectancy, Technological Self-Efficacy, Educational Self-Efficacy, Integrity, and Personal Anxiety. The presentation highlights what factors make students likely to use Generative AI, what factors demotivate use, and how motivation and demotivation do not always produce a desired outcome.