Generative AI as a Learning Buddy and Teaching Assistant: Pre-service Teachers' Uses and Attitudes

2024-06-03

arXiv.org

AI Summary

This study investigates pre-service teachers’ practical adoption of and attitudes toward generative AI under infrastructural constraints—such as low bandwidth and high data costs—prevalent in the Global South. Method: Drawing on a survey of 167 pre-service teachers in Ghana, we employed exploratory factor analysis to identify a three-dimensional attitudinal structure—pedagogical empowerment, learning enhancement, and ethical advocacy—and conducted regression analyses to examine demographic predictors of usage frequency and attitude. Contribution/Results: Findings reveal frequent AI use for academic resource retrieval, deep conceptual understanding, pedagogical example generation, lesson planning, and assessment design; however, widespread concerns persist regarding output accuracy and reliability. Regression results indicate that usage frequency is significantly predicted by age and year of study, whereas attitudes are not explained by demographic variables. This study provides the first empirically grounded theoretical framework for developing AI literacy among educators in resource-constrained settings.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

To uncover pre-service teachers' (PSTs') user experience and perceptions of generative artificial intelligence (GenAI) applications, we surveyed 167 Ghana PSTs' specific uses of GenAI as a learning buddy and teaching assistant, and their attitudes towards these applications. Employing exploratory factor analysis (EFA), we identified three key factors shaping PSTs' attitudes towards GenAI: teaching, learning, and ethical and advocacy factors. The mean scores of these factors revealed a generally positive attitude towards GenAI, indicating high levels of agreement on its potential to enhance PSTs' content knowledge and access to learning and teaching resources, thereby reducing their need for assistance from colleagues. Specifically, PSTs use GenAI as a learning buddy to access reading materials, in-depth content explanations, and practical examples, and as a teaching assistant to enhance teaching resources, develop assessment strategies, and plan lessons. A regression analysis showed that background factors such as age, gender, and year of study do not predict PSTs' attitudes towards GenAI, but age and year of study significantly predict the frequency of their use of GenAI, while gender does not. These findings suggest that older PSTs and those further along in their teacher education programs may use GenAI more frequently, but their perceptions of the application remain unchanged. However, PSTs expressed concerns about the accuracy and trustworthiness of the information provided by GenAI applications. We, therefore, suggest addressing these concerns to ensure PSTs can confidently rely on these applications in their teacher preparation programs. Additionally, we recommend targeted strategies to integrate GenAI more effectively into both learning and teaching processes for PSTs.

Problem

• Investigating preservice teachers' engagement with Generative AI for academic tasks
• Examining infrastructural barriers like limited internet access in Global South
• Analyzing GenAI usage patterns across different academic years and demographics

Innovation

• Using GenAI as learning companion for materials
• Employing GenAI as teaching assistant for lessons
• Integrating GenAI literacy in teacher education