Exploring the role of AI in education

Nathan D. Nguyen

doi:10.31039/ljss.2023.6.108

Authors

Nathan D. Nguyen Harmony School of Advancement

DOI:

https://doi.org/10.31039/ljss.2023.6.108

Keywords:

Guidance, Teacher, Student

Abstract

New advancements in machine learning and AI can be used to augment student learning and teacher capabilities. Examples of AI approaches in education include generating personalized student recommendations, autograding essays, and improving educational resources. AI programs intended to improve education can be categorized informally into three groups: Guidance, Learning, and Teacher. These categories are general and not necessarily mutually exclusive, but provide a framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development.

Framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development.

References

Adisa, T.A., Aiyenitaju, O. and Adekoya, O.D. (2021). "The work–family balance of British working women during the COVID-19 pandemic", Journal of Work-Applied Management, Vol. 13 No. 2, pp. 241-260. https://doi.org/10.1108/JWAM-07-2020-0036

Adams, C., Pente, P., Lemermeyer, G., Rockwell, G. (2021). Artificial Intelligence Ethics Guidelines for K-12 Education: A Review of the Global Landscape. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_4

Afzaal, M., Nouri, J., Zia, A., Papapetrou, U.F., Wu, Y., Li, X., & Weegar, R. (2021). Generation of Automatic Data-Driven Feedback to Students Using Explainable Machine Learning. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_6

Aomi, I., Tsutsumi, E., Uto, M., Ueno, M. (2021). Integration of Automated Essay Scoring Models Using Item Response Theory. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_9

Artificial intelligence (AI) vs. machine learning (ML). (2020, August 17). Retrieved from https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/artificial-intelligence-vs-machine-learning/#introduction

Caeiro-Rodríguez, M., Collado-Ruiz, D., Sánchez-Romero, J. L., Colomo-Palacios, R., & Arcilla-Ruiz, A. (2021). Teaching Soft Skills in Engineering Education: An European Perspective. IEEE Access, 9, 29222-29242. doi: 10.1109/ACCESS.2021.3059516.

Chen, J., Li, H., Ding, W., Liu, Z. (2021). An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_18

Chowdhury, M., & Sadek, A. (2012). Advantages and Limitations of Artificial Intelligence. In M. Chowdhury & A. Sadek (Eds.), Artificial Intelligence Applications to Critical Transportation Issues (pp. 6-8). Transportation Research Circular E-C16.

Dever, D.A., Cloude, E.B., Azevedo, R. (2021). Examining Learners’ Reflections over Time During Game-Based Learning. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_23

Dickler, R. et al. (2021). Examining the Use of a Teacher Alerting Dashboard During Remote Learning. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_24

Entry Level Cyber Security Analyst Salary in Houston, Texas | Salary.com. (2019, March 27). Retrieved from https://www.salary.com/research/salary/posting/entry-level-cyber-security-analyst-salary/houston-tx.

EU AI Act: first regulation on artificial intelligence. (2023, August 6). Retrieved from https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence

Ghafar, Z. N., Mohamedamin, A. A., & Mohammed, N. N. (2022). The Role of Teachers in Various Positions of Creating a Convincing Teaching Environment for Students in the Academic Field: A Brief Review. Canadian Journal of Educational and Social Studies, 2(6), 104-116.

Goslen, A., Carpenter, D., Rowe, J.P., Henderson, N., Azevedo, R., Lester, J. (2022). Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_7

Hlosta, M., Herodotou, C., Bayer, V., Fernandez, M. (2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged Students in STEM. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_34

Hoffman, B. (2023, February 4). Leaders Looking To Leverage AI Need To Think About Context. Retrieved from https://www.forbes.com/sites/brycehoffman/2023/03/31/leaders-looking-to-leverage-ai-need-to-think-about-context/.

Holstein, K., McLaren, B.M., Aleven, V. (2019). Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_14

Holzinger, A., Saranti, A., Molnar, C., Biecek, P., Samek, W. (2022). Explainable AI Methods - A Brief Overview. In: Holzinger, A., Goebel, R., Fong, R., Moon, T., Müller, KR., Samek, W. (eds) xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science(), vol 13200. Springer, Cham. https://doi.org/10.1007/978-3-031-04083-2_2

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2

Khan B, Fatima H, Qureshi A, Kumar S, Hanan A, Hussain J, Abdullah S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices. 2023 Feb 8:1-8. doi: 10.1007/s44174-023-00063-2. Epub ahead of print. PMID: 36785697; PMCID: PMC9908503.

Kirchner-Krath, J., Schürmann, L., & von Kortzfleisch, H. (2021). Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games, and game-based learning. Computers in Human Behavior, 125, 106963. https://doi.org/10.1016/j.chb.2021.106963

Pierre-Louis, S. (2023, July 6). Essential Facts - Entertainment Software Association. Retrieved from https://www.theesa.com/2023-essential-facts/.

Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116, 68-74. https://doi.org/10.1016/j.jbusres.2020.05.019

Rowe, J., Mott, B., McQuiggan, S., Robison, J., Lee, S., & Lester, J. (2009, July). Crystal island: A narrative-centered learning environment for eighth grade microbiology. In workshop on intelligent educational games at the 14th international conference on artificial intelligence in education, Brighton, UK (pp. 11-20).

Spector, J. M., & Ma, S. (2019). Inquiry and critical thinking skills for the next generation: From artificial intelligence back to human intelligence. Smart Learning Environments, 6, 8. https://doi.org/10.1186/s40561-019-0088-z

Vanian, J., & Leswing, K. (2023, March 13). ChatGPT and generative AI are booming, but the costs can be extraordinary. Retrieved from https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-a-very-expensive-price.html