Artificial Intelligence for Student Assessment: A Systematic Review
By Víctor González-Calatayud, Paz Prendes-Espinosa, Rosabel Roig-Vila. Applied Sciences, 2021
378 citations recorded by the source corpus.
Artificial Intelligence (AI) is being implemented in more and more fields, including education. The main uses of AI in education are related to tutoring and assessment. This paper analyzes the use of AI for student assessment based on a systematic review. For this purpose, a search was carried out in two databases: Scopus and Web of Science. A total of 454 papers were found and, after analyzing them according to the PRISMA Statement, a total of 22 papers were selected. It is clear from the studies analyzed that, in most of them, the pedagogy underlying the educational action is not reflected. Similarly, formative evaluation seems to be the main use of AI. Another of the main functionalities of AI in assessment is for the automatic grading of students. Several studies analyze the differences between the use of AI and its non-use. We discuss the results and conclude the need for teacher training and further research to understand the possibilities of AI in educational assessment, mainly in other educational levels than higher education. Moreover, it is necessary to increase the wealth of research which focuses on educational aspects more than technical development around AI.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/783dead4fd1aa29ee019a2f324e859a6b3dcb332
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Citations in the corpus
- New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023, background): “Specifically, AI can be trained to grade a range of subjects and assessments including essays, problem-solving questions, and even graphing assessments [27].”
- A Documentary Evaluation of Offline AI Integration in Teacher-in-a-Box OER Programmes for Low-Connectivity Schools (2026, unknown): “These instruments operationalise the paper’s central de sign claim: offline AI can improve usability of embedded supports if it is bounded by retrieval grounding and governance, and if assessment designs require evidence and process, not polished outputs alone [14], [16]-[18], [25], [31].”