The Impact of Artificial Intelligence on Education: Opportunities and Challenges

Authors

  • Sri Rahayu Universitas Asahan

DOI:

https://doi.org/10.31949/educatio.v9i4.6110

Abstract

The integration of Artificial Intelligence (AI) in education, particularly in learning assessments, presents a notable paradigm shift, promising advancements in learning methodologies. Numerous studies advocate for AI's potential to enhance students' quality by refining evaluations and furnishing precise, measurable feedback. It stands out in mitigating errors, enhancing evaluation accuracy, identifying individual needs, and fostering more effective teaching. Traditional educational approaches, fraught with subjective human judgment and limited forms of assessment like written or oral tests, often fail to capture individual abilities comprehensively. AI's implementation demonstrates the ability to reduce bias, enhance efficiency, and provide tailored assessments, addressing these limitations. Methodologically, this article employs a literature review to synthesize various perspectives on AI's impact on education. It explores AI's potential benefits such as objectivity, efficiency, consistency, analytical capabilities, developmental programs, personalization, flexibility, and anti-cheating measures. Furthermore, it delves into challenges, notably AI's validity, high costs, technological dependency, data security, and the potential influence of behavioral changes on assessment outcomes. The results reveal multifaceted advantages of AI technology, including objectivity in assessments devoid of human bias, efficiency in time and cost, consistent evaluations, enhanced analytical skills, assessment program development, flexibility, and fraud mitigation. However, challenges exist, ranging from ensuring AI's validity and reliability, addressing technological dependency and cost hurdles, securing data, to mitigating biased discrimination. In conclusion, while AI presents a plethora of advantages, its integration into education demands meticulous consideration of associated challenges. The technology's efficacy and reliability, coupled with the cost and security aspects, necessitate thorough scrutiny and rigorous testing before implementation.

Keywords:

Artificial Intelligence, Opportunities, Challenges

Downloads

Download data is not yet available.

References

Adedokun, A.O., & Adeyemo, O.I. (2021). Enhancing Assessment and Evaluation with Artificial Intelligence. International Journal of Emerging Technologies in Learning, 16(4), 134-148. http://dx.doi.org/10.30734/jpe.v10i2.3199

Aggarwal, A., Singla, S., & Kaur, S. (2019). Machine learning based automatic assessment systems: A review. International Journal of Computer Applications, 181(47), 15-22. https://doi.org/10.1016/j.iswa.2021.200056

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and ethics , 2 (3), 431–440. https://doi.org/10.1007/s43681-021-00096-7 .

Alshehri, S., Drew, S., Alghamdi, R., Alsolami, R., & Aljohani, N. (2019). The impact of using artificial intelligence in assessments. Education and Information Technologies, 24(2), 1619-1638. https://link.springer.com/journal/10639/volumes-and-issues/24-2

Arikunto, S. (2013). The research procedure is a practice approach (revision VIII). Jakarta: Rineka Cipta. https://opac.perpusnas.go.id/DetailOpac.aspx?id=801361

Beede, P., Julian, J., Langdon, G., McKittrick, G., Khan, B., & Doms, M. (2011). Women in STEM: A gender gap to innovation. US Department of Commerce. DOI:10.2139/ssrn.1964782

Bennett, R.E. (2011). Formative assessment: A critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5-25. https://doi.org/10.1080/0969594X.2010.513678

Black, P., & William, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80(2), 139-148. DOI:10.1177/003172171009200119

Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in neural information processing systems (pp. 4349-4357). https://doi.org/10.48550/arXiv.1607.06520

Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. The Cambridge Handbook of Artificial Intelligence, 316-334. https://doi.org/10.1017/CBO9781139046855.020

Brookhart, S. M. (2013). How to create and use rubrics for formative assessment and grading. ASCD. DOI: 10.12691/education-5-5-12

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:77-91. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf

Chen, G., Gao, Y., Chen, X., & Yang, Y. (2020). Adaptive learning and assessment based on learning styles using deep learning. Journal of Educational Computing Research, 57(6), 1447-1466. DOI:10.1186/s41239-021-00289-4

Chen, L.C., Chen, Y.H., & Huang, Y.M. (2019). The effects of web-based formative assessment on self-regulated learning and learning performance in a mathematics course. Computers & Education, 133, 43-55. DOI:10.1007/s11423-021-10071-y

Chan, C. (2023). Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students' Perceptions of 'AI-giarism'. https://www.researchgate.net/publication/371347082_Is_AI_Changing_the_Rules_of_Academic_Misconduct_An_In-depth_Look_at_Students'_Perceptions_of_'AI-giarism'

Chaudhry, Muhammad & Kazim, Emre. (2021). Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021. AI and Ethics, 2. 1-9. DOI:10.1007/s43681-021-00074-z

Darling-Hammond, L., & Adamson, F. (2010). Beyond basic skills: The role of performance assessment in achieving 21st century standards of learning. Stanford Center for Opportunity Policy in Education. DOI: 10.12691/education-5-5-12

Davis, R.E., Nichols, R.L., & Grant, J.F. (2019). Using artificial intelligence to develop and evaluate a competency-based assessment program in family medicine. Academic Medicine, 94(4), 557-563. doi: 10.1370/afm.2887

Foltz, P. W. (2013). Automated essay scoring: applications to educational technology. Handbook of Research on Educational Communications and Technology, 2, 169-181. https://www.researchgate.net/publication/239061100_Automated_Essay_Scoring_Applications_to_Educational_Technology

Gao, T., et al. (2020). A review of artificial intelligence applications in educational assessment. Journal of Educational Evaluation for Health Professions, 17: 27. DOI:10.36941/ajis-2021-0077

George, B., & Wooden, O. (2023). Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences , 13 (9), 196. MDPI AG. Retrieved fromhttps://doi.org/10.3390/admsci13090196

Han, S. (2018). Exploring the role of artificial intelligence in language assessment. Language Testing, 35(1), 37-55. https://doi.org/10.3390/languages8040247

Harlen, W. (2005). Teachers' summative practices and assessment for learning— tensions and synergies. The Curriculum Journal, 16(2), 207-223. https://doi.org/10.1080/09585170500136093

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. https://apprendre.auf.org/wp-content/opera/13-BF-References-et-biblio-RPT-2014/Visible%20Learning_A%20synthesis%20or%20over%20800%20Meta-analyses%20Relating%20to% 20Achievement_Hattie%20J%202009%20...pdf

Hendry, GD, Harper, BD, & Rahman, FM (2019). Using machine learning to detect cheating in online assessments. Assessment & Evaluation in Higher Education, 44(3), 360-372. DOI: 10.1371/journal.pone.0254340

Hoque, R., Sorwar, G., & Alzoubi, M. (2021). A Comprehensive Review of the Use of Artificial Intelligence in Education: Opportunities and Challenges. Journal of Educational Technology & Society, 24(2), 110-123. DOI: 10.3390/diagnostics13010100

Kahng, J., & Cho, K. (2019). The applications of artificial intelligence in educational assessment. Journal of Educational Evaluation for Health Professions, 16: 31. DOI:10.29322/IJSRP.13.03.2023.p13536

Kordzadeh, Nima & Ghasemaghaei, Maryam. (2021). Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems. 31. 1-22. DOI:10.1080/0960085X.2021.1927212

Kovanović, V., Joksimović, S., Gašević, D., & Hatala, M. (2015). Analyzing and predicting learning achievements in online courses with symbolic and subsymbolic methods. Journal of Computer Assisted Learning, 31(3), 268-286. https://doi.org/10.1016/j.iheduc.2015.06.002

Kumar, V., & Boulanger, D. (2020, October). Explainable automated essay scoring: Deep learning really has pedagogical value. In Frontiers in education (Vol. 5, p. 572367). Frontiers Media SA. Retrieved from osf.io/fxvru .

Kunnath, S.R., Gupta, S., & Srivastava, S. (2020). Automated essay scoring using natural language processing techniques: A systematic review. IEEE Access, 8, 200322-200335. doi: 10.1007/s10462-021-10068-2

Lee, A.S., Babenko, O., George, M., & Daniels, V. (2023). The promises and perils of remote proctoring using artificial intelligence. Canadian medical education journal , 14 (2), 173–174. https://doi.org/10.36834/cmej.74229

Muthmainnah & Seraj, Prodhan & Oteir, Ibrahim. (2022). Playing with AI to Investigate Human-Computer Interaction Technology and Improving Critical Thinking Skills to Pursue the 21st Century Age. Education Research International. 2022. 17. 10.1155/2022/6468995.

Nitko, A. J. (2001). Educational assessment of students (2nd ed.). Upper Saddle River, NJ: Merrill. https://www.scirp.org/(S(351jmbntvnsjt1aadkposzje))/reference/referencespapers.aspx?referenceid=2411143

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods , 16 (1). https://doi.org/10.1177/1609406917733847

Owoc, M. L., Sawicka, A., & Weichbroth, P. (2019, August). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (pp. 37-58). Cham: Springer International Publishing. DOI:10.1007/978-3-030-85001-2_4

Rauh, C., Heyder, A., & Maier, R. (2018). The potential of adaptive educational technologies: An empirical study of personalized e-learning. Journal of Educational Technology & Society, 21(3), 1-13. https://www.jstor.org/stable/e26458500

Riduwan. (2015). The scale of measurement of research variables. Alphabet. https://opac.perpusnas.go.id/DetailOpac.aspx?id=716954

Salvia, J., & Ysseldyke, J. (2007). Assessment in special education: A practical approach. Boston, MA: Houghton Mifflin. https://www.researchgate.net/publication/230853249_Assessment_in_Special_and_Inclusive_Education

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003

Schneider, E.F., Lang, A., Shin, M., & Bradley, S.D. (2019). Investigating the use of artificial intelligence in standardized medical assessments. Academic Medicine, 94(11S), S74-S81. http://repo.darmajaya.ac.id/4143/1/Artificial%20Intelligence%20in%20Medical%20Imaging_%20Opportunities%2C%20Applications%20and%20Risks%20%28%20PDFDrive%20%29.pdf

Shabani, M., & Borry, P. (2018). Rules for the ethical use of digital data in human research. In Ethical Aspects of Research with Human Subjects, (pp. 113-129). DOI: 10.1038/s41431-017-0045-7

Rios-Campos, Carlos & Cánova, Elva & Zaquinaula, Irma & Zaquinaula, Hilda & Castro Vargas, Daniel & Peña, Willam & Idrogo, Carlos & Arteaga, Rayber. (2023). Artificial Intelligence and Education. South Florida Journal of Development. 4. 641-655. 10.46932/sfjdv4n2-001.

Stiggins, R. (2005). From formative assessment to assessment for learning: A path to success in standards-based schools. Phi Delta Kappan, 87(4), 324- 328. http://68.77.48.18/RandD/Phi%20Delta%20Kappan/Assessment%20FOR%20Learning%20-%20Stiggins.pdf

Stiggins, R. (2007). Assessment through the student's eyes. Educational Leadership, 64(8), 22-26. https://www.researchgate.net/publication/237491140_Assessment_Through_the_Student's_Eyes

Stowe, R., Sammons, M., Sibert, J. L., & Vincent, R. (2020). Remote Proctoring: An Examination of Utilizing Artificial Intelligence and Assessment Literacy to Ensure Academic Integrity in Online Assessments. Journal of Educators Online, 17(2), n2. https://www.thejeo.com/archive/2020_17_2

Zawacki-Richter, Olaf & Marín, Victoria & Bond, Melissa & Gouverneur, Franziska. (2019). Systematic review of research on artificial intelligence applications in higher education - where are the educators?. International Journal of Educational Technology in Higher Education. 16. 1-27. 10.1186/s41239-019-0171-0.

Downloads

Abstract Views : 4925
Downloads Count: 3823

Published

2023-11-30

How to Cite

Rahayu, S. (2023). The Impact of Artificial Intelligence on Education: Opportunities and Challenges. Jurnal Educatio FKIP UNMA, 9(4), 2132–2140. https://doi.org/10.31949/educatio.v9i4.6110

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.