Artificial Intelligence for Student Assessment: A Systematic Review
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Intelligent Tutoring Systems and Adaptive Learning · E-Learning and Knowledge Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Statistics, Mathematics and Computer Sciences, Miguel Hernández University, 03202 Elche, Spain
Department of Didactic and School Organization, University of Murcia, 30100 Murcia, Spain
Department of General Didactic and Specific Didactic, University of Alicante, 03690 Alicante, Spain
References (60)
Show source-provided references
1. UNESCO (2021, May 22). Elaboration of a Recommendation on the Ethics of Artificial Intelligence. Available online: https://en.unesco.org/artificial-intelligence/ethics.
2. (2021, May 22). United Nations Transforming Our World: The 2030 Agenda for Sustainable Development/Department of Economic and Social Affairs. Available online: https://sdgs.un.org/2030agenda.
3. The Development of an Intelligent Multilevel Item Bank Model for the National Evaluation of Undergraduates10.13189/ujer.2020.080942 ↗
4. Tecnologías avanzadas para afrontar el reto de la innovación educativa
5. Chai, C.S., Wang, X., and Xu, C. (2020). An extended theory of planned behavior for the modelling of chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8.10.3390/math8112089 ↗
6. Cugurullo, F. (2020). Urban Artificial Intelligence: From Automation to Autonomy in the Smart City. Front. Sustain. Cities, 2.10.3389/frsc.2020.00038 ↗
7. Cope, B., Kalantzis, M., and Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educ. Philos. Theory, 1–17.10.1080/00131857.2020.1728732 ↗
8. Korteling, J.E., van de Boer-Visschedijk, G.C., Blankendaal, R.A.M., Boonekamp, R.C., and Eikelboom, A.R. (2021). Human-versus Artificial Intelligence. Front. Artif. Intell., 4.10.3389/frai.2021.622364 ↗
9. Paiva, S., Ahad, M., Tripathi, G., Feroz, N., and Casalino, G. (2021). Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges. Sensors, 21.10.3390/s21062143 ↗
10. Hwang, S., Song, Y., and Kim, J. (2021). Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application. Appl. Sci., 11.10.3390/app11062707 ↗
11. Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., and Del Maestro, R.F. (2020). The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS ONE, 15.10.1371/journal.pone.0229596 ↗
12. SERIES: eHealth in primary care. Part 3: eHealth education in primary care10.1080/13814788.2020.1797675 ↗
13. Ocaña-Fernández, Y., Valenzuela-Fernández, L.A., and Garro-Aburto, L.L. (2019). Inteligencia artificial y sus implicaciones en la educación superior. Propósitos y Represent., 7.10.20511/pyr2019.v7n2.274 ↗
14. Smart learning environments and ergonomics: An approach to the state of the question10.7821/naer.2020.7.562 ↗
15. Teaching Machine Dichotomy: Skinner vs. Pressey10.2466/pr0.1960.6.1.11 ↗
16. UNESCO (2019). Beijing Consensus on Artificial Intelligence and Education, UNESCO.10.18356/d94175df-en ↗
17. Robotic Chinese language tutor: Personalising progress assessment and feedback or taking over your job?10.1108/OTH-04-2020-0015 ↗
18. Statistical Techniques to Explore the Quality of Constraints in Constraint-Based Modeling Environments10.1007/s40593-013-0008-2 ↗
19. Exploring feedback and student characteristics relevant for personalizing feedback strategies10.1016/j.compedu.2013.09.011 ↗
20. Machine learning to extract communication and history-taking skills in OSCE transcripts10.1111/medu.14347 ↗
21. Involving Users to Improve the Collaborative Logical Framework10.1155/2014/893525 ↗
22. Evaluation of an intelligent open learning system for engineering education
23. Feedback from digital systems used in higher education: An inquiry into triggered emotions two universal design oriented solutions for a better user experience
24. Setting accessibility preferences about learning objects within adaptive elearning systems: User experience and organizational aspects10.1111/exsy.12187 ↗
25. Qu, S., Li, K., Wu, B., Zhang, S., and Wang, Y. (2019). Predicting student achievement based on temporal learning behavior in MOOCs. Appl. Sci., 9.10.3390/app9245539 ↗
26. Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling10.1007/s10639-020-10159-7 ↗
27. Automated Scoring of Chinese Engineering Students’ English Essays10.4018/IJDET.2017010104 ↗
28. Individualized AI Tutor Based on Developmental Learning Networks10.1109/ACCESS.2020.2972167 ↗
29. Villegas-Ch, W., Arias-Navarrete, A., and Palacios-Pacheco, X. (2020). Proposal of an Architecture for the Integration of a Chatbot with Artificial Intelligence in a Smart Campus for the Improvement of Learning. Sustainability, 12.10.3390/su12041500 ↗
30. Building an efficient artificial intelligence model for personalized training in colleges and universities10.1002/cae.22235 ↗
31. Predicción del rendimiento académico por medio de técnicas de inteligencia artificial10.4067/S0718-50062020000100093 ↗
32. Gough, D., Oliver, S., and Thomas, J. (2017). An Introduction to Systematic Reviews, SAGE.
33. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 71.10.1136/bmj.n71 ↗
34. Co-word analysis applied to highly cited papers in Library and Information Science (2007–2017)10.1590/2318-08892018000300001 ↗
35. A virtual reality simulator for teaching and evaluating dental procedures10.3414/ME9310 ↗
36. Intelligent dental training simulator with objective skill assessment and feedback10.1016/j.artmed.2011.04.003 ↗
37. A neuro-fuzzy inference system for the evaluation of reading/writing competencies acquisition in an e-learning environnement
38. Redesigning an Object-Oriented Programming Course10.1145/2906362 ↗
39. The Use of Argument Maps as an Assessment Tool in Higher Education10.1016/j.ijer.2016.03.002 ↗
40. Assistance and Feedback Mechanism in an Intelligent Tutoring System for Teaching Conversion of Natural Language into Logic10.1007/s40593-017-0139-y ↗
41. Using AI to Teach AI: Lessons from an Online AI Class
42. An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance10.1007/s40593-016-0116-x ↗
43. Different Approaches to Assessing the Quality of Explanations Following a Multiple-Document Inquiry Activity in Science10.1007/s40593-017-0138-z ↗
44. Malik, K.R., Mir, R.R., Farhan, M., Rafiq, T., and Aslam, M. (2017). Student Query Trend Assessment with Semantical Annotation and Artificial Intelligent Multi-Agents. EURASIA J. Math. Sci. Technol. Educ., 13.10.12973/eurasia.2017.00763a ↗
45. Using virtual standardized patients to accurately assess information gathering skills in medical students10.1080/0142159X.2019.1616683 ↗
46. Sun, Z., Anbarasan, M., and Praveen Kumar, D. (2020). Design of online intelligent English teaching platform based on artificial intelligence techniques. Comput. Intell., 12351.10.1111/coin.12351 ↗
47. Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country10.1016/j.heliyon.2020.e04081 ↗
48. Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses10.1109/ACCESS.2020.3010938 ↗
49. A Novel Hybrid Fuzzy AHP-GA Method for Test Sheet Question Selection10.1142/S0219622020500054 ↗
50. A critical deconstruction of computer-based test application in Turkish State University10.1007/s10639-020-10199-z ↗
51. Choi, Y., and McClenen, C. (2020). Development of adaptive formative assessment system using computerized adaptive testing and dynamic bayesian networks. Appl. Sci., 10.10.3390/app10228196 ↗
52. Descriptive theory of awareness for groupware development10.1007/s12652-018-1165-9 ↗
53. Roig-Vila, R., and Moreno-Isac, V. (2020). El pensamiento computacional en Educación. Análisis bibliométrico y temático. Rev. Educ. Distancia, 20.10.6018/red.402621 ↗
54. Diseño y validación de un instrumento para la taxonomía de los robots de suelo en Educación Infantil
55. González González, C.S. (2019). Estrategias para la enseñanza del pensamiento computacional y uso efectivo de tecnologías en educación infantil: Una propuesta inclusiva. Rev. Interuniv. Investig. Tecnol. Educ.10.6018/riite.405171 ↗
56. Recio Caride, S. (2019). Experiencias robóticas en infantil. Rev. Interuniv. Investig. Tecnol. Educ., 12.10.6018/riite.399641 ↗
57. Evaluation of Teacher Digital Competence Frameworks Through Expert Judgement: The Use of the Expert Competence Coefficient10.7821/naer.2020.7.578 ↗
58. Payá, A., and Mengual-Ándres, S. (2019). Interactive environments for involvement and motivation for learning. Video Games for Teachers: From Research to Action, Mcragw Hill.
59. University-School Scenarios and Voices from Classrooms. Rethinking Collaboration within the Framework of an Interuniversity Project10.7821/naer.2019.7.372 ↗
60. Redefining Knowledge in the Digital Age10.7821/naer.2018.7.294 ↗