Research on the Impact Factors of User Satisfaction With AI-Assisted Academic Writing Tools
Yu, Xiaobo (2025-08-01)
Research on the Impact Factors of User Satisfaction With AI-Assisted Academic Writing Tools
(01.08.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025081882982
https://urn.fi/URN:NBN:fi-fe2025081882982
Tiivistelmä
With the rapid development of artificial intelligence technology, AI-assisted academic writing tools have gradually become important auxiliary tools for academic research due to their efficiency and intelligent features. However, users face limitations in functionality, data privacy risks, and ethical issues during actual use, leading to significant fluctuations in user satisfaction. Therefore, optimizing tool performance from both technical and user perspectives to enhance user satisfaction has become a hot topic of common concern in the academic community.
This study is based on the Technology Acceptance Model (TAM) and the Information Systems Success Model (ISSM). It systematically analyzes the core factors affecting user satisfaction with AI-assisted academic writing tools through a combination of qualitative and quantitative methods. The research first employs a semi-structured interview method and uses grounded theory to conduct a three-level coding analysis of the interview data, identifying and refining core dimensions such as system quality, tool adaptability, information quality, ethical compliance, resource equity, user perceived value, user experience, and perceived risk. Based on this, a multidimensional theoretical model encompassing technological factors, institutional factors, user factors, and content factors is constructed, along with corresponding research hypotheses. Subsequently, a questionnaire survey was conducted to collect 408 valid sample data, and structural equation modeling analysis was performed using SmartPLS software to validate the theoretical model and research hypotheses.
The research results indicate that system quality, tool adaptability, information quality, and functional utility have a significant positive impact on user satisfaction. Ethical compliance and resource fairness indirectly positively influence satisfaction by enhancing user trust. Additionally, perceived risk significantly suppresses user satisfaction. Furthermore, the level of user involvement indirectly positively affects user satisfaction by improving perceived ease of use and interaction experience.
Based on the above research conclusions, this study proposes targeted optimization strategies, including strengthening the intelligent features of the tools, particularly the accuracy of content generation, literature recommendation, and logical verification; improving the design of the interactive interface to reduce users' learning and operational costs; enhancing privacy protection and ethical compliance mechanisms by establishing transparent user data management standards and ethical certification systems to increase user trust in the tools; and encouraging educational institutions to formulate relevant usage norms to balance tool efficiency and academic integrity, thereby further enhancing overall user satisfaction and willingness to continue using the tools.
This study expands the theoretical framework for user satisfaction research on AI-assisted academic writing tools, providing theoretical support and practical guidance for tool developers and educational institutions to optimize tool design.
This study is based on the Technology Acceptance Model (TAM) and the Information Systems Success Model (ISSM). It systematically analyzes the core factors affecting user satisfaction with AI-assisted academic writing tools through a combination of qualitative and quantitative methods. The research first employs a semi-structured interview method and uses grounded theory to conduct a three-level coding analysis of the interview data, identifying and refining core dimensions such as system quality, tool adaptability, information quality, ethical compliance, resource equity, user perceived value, user experience, and perceived risk. Based on this, a multidimensional theoretical model encompassing technological factors, institutional factors, user factors, and content factors is constructed, along with corresponding research hypotheses. Subsequently, a questionnaire survey was conducted to collect 408 valid sample data, and structural equation modeling analysis was performed using SmartPLS software to validate the theoretical model and research hypotheses.
The research results indicate that system quality, tool adaptability, information quality, and functional utility have a significant positive impact on user satisfaction. Ethical compliance and resource fairness indirectly positively influence satisfaction by enhancing user trust. Additionally, perceived risk significantly suppresses user satisfaction. Furthermore, the level of user involvement indirectly positively affects user satisfaction by improving perceived ease of use and interaction experience.
Based on the above research conclusions, this study proposes targeted optimization strategies, including strengthening the intelligent features of the tools, particularly the accuracy of content generation, literature recommendation, and logical verification; improving the design of the interactive interface to reduce users' learning and operational costs; enhancing privacy protection and ethical compliance mechanisms by establishing transparent user data management standards and ethical certification systems to increase user trust in the tools; and encouraging educational institutions to formulate relevant usage norms to balance tool efficiency and academic integrity, thereby further enhancing overall user satisfaction and willingness to continue using the tools.
This study expands the theoretical framework for user satisfaction research on AI-assisted academic writing tools, providing theoretical support and practical guidance for tool developers and educational institutions to optimize tool design.