Evaluating the Effectiveness of Artificial Intelligence in Education Using Technology Acceptance Model for Personalized Learning and Assessment

Abstract

learning and automating assessment processes. However, despite its widespread adoption, there is limited empirical clarity on how “effectiveness” is defined and measured in educational contexts. Many studies focus on descriptive benefits without systematically evaluating user perceptions, usability, and learning outcomes. This study addresses this gap by applying the Technology Acceptance Model (TAM) to systematically assess AI effectiveness in education. The purpose of this research is to define measurable parameters for evaluating AI effectiveness, analyze its impact on personalized learning and assessment, and identify ethical and implementation challenges. A qualitative Systematic Literature Review (SLR) was conducted, covering peer-reviewed studies published between 2016 and 2024. Databases searched included Scopus, Web of Science, and Google Scholar, and selected studies were analyzed using TAM constructs: Perceived Usefulness, Perceived Ease of Use, User Attitude, and Behavioral Intention. Results indicate that AI significantly enhances personalized learning and assessment efficiency. Perceived Usefulness was the strongest factor influencing adoption, followed by ease of use and positive user attitudes. Ethical considerations, including data privacy, bias, and equitable access, remain critical challenges. In conclusion, AI can effectively improve educational outcomes when implemented thoughtfully, with user-centered design, institutional support, and ethical safeguards. TAM provides a robust framework to evaluate adoption and sustained effectiveness.