The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance
Project Overview
The document explores the integration of Generative Artificial Intelligence (GenAI) in upper secondary mathematics education in Finland, focusing on students' perceptions of its usefulness and ease of use as analyzed through the Technology Acceptance Model (TAM). It reveals that the perceived usefulness of GenAI is the most significant factor influencing students' intention to utilize this technology, alongside enjoyment and compatibility with existing educational practices. The findings underscore the importance of conducting empirical research to further investigate how GenAI can effectively enhance educational outcomes and practices. Overall, the integration of GenAI in education is viewed as a promising avenue for improving student engagement and learning experiences, highlighting a need for continued exploration and validation of its applications in various educational contexts.
Key Applications
Generative Artificial Intelligence (GenAI) tools
Context: Upper secondary mathematics education in Finland, targeting high school students aged 16-19.
Implementation: Students used Copilot during various advanced mathematics courses, receiving AI-assisted support for problem-solving in different topics.
Outcomes: Students reported a strong influence of perceived usefulness on their intention to use GenAI tools, with compatibility enhancing their perceptions of usefulness.
Challenges: Concerns regarding GenAI's impact on learning outcomes, particularly in fostering traditional calculation skills.
Implementation Barriers
Perception Barrier
Concerns about how GenAI might affect learning outcomes and students' traditional calculation skills.
Proposed Solutions: Teachers need to instruct students on effective interactions with GenAI to optimize learning experiences.
Project Team
Mika Setälä
Researcher
Ville Heilala
Researcher
Pieta Sikström
Researcher
Tommi Kärkkäinen
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mika Setälä, Ville Heilala, Pieta Sikström, Tommi Kärkkäinen
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai