Utilizing artificial intelligence for assessment in higher education

Overview: This systematic review explores the utilization of artificial intelligence (AI) for assessment, grading, and feedback in higher education. The review aims to establish how AI technologies enhance efficiency, scalability, and personalized learning experiences in educational settings, while addressing associated challenges that arise due to AI use. Methods: In this article, a comprehensive search of 6 different academic databases including PubMed, Google Scholar, IEEE Xplore, ERIC, and Scopus were conducted. The focus was on the published studies ranging between 2010 and 2023. Also, inclusion criteria required studies to be peer-reviewed, centered on AI applications in higher education. Studies were to provide empirical evidence or theoretical discussions relevant to assessment processes. Thus, twenty studies meeting these criteria were selected, scrutinized and analyzed. Results: Pertaining to the findings, they indicate that AI-driven systems significantly streamline grading processes, reduce turnaround times, and provide timely, personalized feedback. These systems also offer data-driven insights that inform instructional practices. However, challenges such as algorithmic bias, validity concerns in subjective assessments, and ethical issues related to data privacy persist. Effective AI integration necessitates alignment with pedagogical goals, ongoing professional development for educators, and transparent policies to ensure fairness and equity. Conclusion: AI technologies hold transformative potential for enhancing assessment practices in higher education. Therefore, addressing technical, ethical, and pedagogical challenges through interdisciplinary collaboration and evidence-based approaches is essential to fully realizing AI's benefits. Future research should focus on validating AI-driven assessment methods and exploring their long-term impact on educational outcomes.