The integration of artificial intelligence (AI) into higher education has introduced significant opportunities, challenges, and disruptions to existing teaching practices, learning outcomes, and assessments. This study seeks to assess the relevant aspects of AI adoption in higher education institutions, focusing on perceived risks, effort expectancy, and performance expectancy, to determine how AI can ease the burden on students and enable them to achieve the best performance. The present study complements a quantitative research design and surveyed 161 students above the age of 17 from diverse demographic backgrounds using Likert-scale items. The data were analyzed using IBM SPSS Statistics to understand the significance of a large number of students’ attitudes and how they were affected by the introduction of AI technologies in higher education institutions. The conclusions demonstrate that although AI adoption and integration risks also exist, this variable is positively connected to AI adoption rather than discouraging institutions from employing AI technologies. Effort expectancy also had a positive correlation, which means that people’s perception of how difficult it is to perform can postpone its frequent usage but does not affect its continuing use. Conversely, performance expectancy demonstrated a strong and significant positive relationship with AI adoption, highlighting that students and institutions recognize the tangible benefits offered by AI, such as improved productivity, academic quality, and efficiency. This study contributes to the growing discussion on AI in education by offering actionable recommendations for mitigating risks, improving accessibility, and optimizing the integration of AI technologies to enhance learning experience. The academic community will benefit from the findings of this study in that it highlights both the advantages and the disadvantages of preparing higher education institutions to adopt AI. Consequently, this thesis seeks to address the strategies and the measures that can be put in place to counter the risks which come with AI adoption in higher education internationally looking also to integrate AI as a tool that reduces the workload of students and increases efficiency. Future research should explore teachers’ perspectives and conduct cross-cultural analyses to further inform policy and implementation strategies.