AI Diagnosis: Rise of AI-Powered Assessments in Modern Education Systems

The literature on the limitations on the current archaic education system is limitless, the consequences of which have only been exacerbated in the current lockdown scenario. The timed evaluations have not only failed as an assessment tool during these times but research has shown there are increased rates of using unfair means and proctoring as a result. Not only was the system faulty to begin with, it is failing miserably under current lockdown situations. Simultaneously the current literature keeps positing that since technology has become an integral part of our life already, it would not be long before technology integrates with education and assessments. Taking into consideration the need and potential of an integrative system, this paper aims to explore how artificial intelligence can be effectively introduced into education and improve learning outcomes. The paper performs a Comprehensive Literature Review (CLR), and analyses data based on the framework developed by Onwuegbuzie and Frels (2015). The paper thus reviews literature with the aim to explore current models of AIEd and relevant psychological concepts relating to learning and career outcomes. The evidence is consistence with the rationale for research problem: current AI methodologies in education focus only on delivering learning material, using AI as a means, instead of taking into other factors improving learning and education outcomes. The subsequent literature review on the factors influencing learning outcomes establish that there are two main thematic influences on students learning and behavioral outcomes: inside-school and out of school factors, which have been further implored in context of technological advancements