<![CDATA[This article explores the major technological advancements in education and the ethical issues they raise. In this research, we use the systematic literature mapping method. The study follows the research methodology outlined by Kabudi, Pappas, and Olsen, with guidance from Petersen, Vakkalanka, and Kuzniarz. The methodology applied in both studies is as follows: (i) search and selection, (ii) data extraction, (iii) classification and analysis, and (iv) evaluation of validity. The PRISMA approach, or Preferred Reporting Items for Systematic Reviews and Meta-Analyses, was used as a framework for the search and selection phase. Key findings reveal that AI can automate grading and provide personalized feedback to students, while also reducing cheating. It also helps in predictive analytics by predicting learning outcomes and identifying at-risk students. AI also evaluates non-cognitive traits like emotional states and collaborative skills. However, the study also highlights ethical issues in AI-based assessments, such as inclusivity, fairness, accountability, accuracy, explanation, auditability, security, privacy, autonomy, consent, and sustainability. The research identifies five key thematic areas: AI system design, AI-driven assessment rollout, data stewardship, assessment administration, and grading and evaluation. The study concludes that while AI presents transformative opportunities for educational assessments, it also introduces complex ethical challenges that must be carefully managed.]]>
