Traditional vs. Personalised Teaching: An experimental study on AI's role in education

Abstract: One of the major challenges in education is ensuring student success for most learners through quality teaching. However, creating an inclusive education system that addresses student heterogeneity is difficult when applying the same curricular standard.In most education systems, standardized teaching is the dominant pedagogical approach, characterized by minimal differentiation and a repetitive program applied over years (Perrenoud, 1978). This homogeneity, although practical, often neglects individual differences, limiting each student’s learning potential. "All students learn in different ways and more effectively when learning circumstances align with their preferred approach" (Hockett, 2018).Luckin and Holmes (2016) argue that individual human tutoring can be the most effective approach to teaching and learning. Unfortunately, it’s unsustainable: it’s not possible to provide one teacher per student.Therefore, how can teachers provide personalized teaching? Could AI be a tool for personalization? And could it also contribute to a positive shift in the role of the teacher, positioning them as a cornerstone of teaching?Chen, Xie et al. (2020) observe that studies on Artificial Intelligence in Education (AIEd) have increased significantly, especially regarding content personalization. Maghsudi et al. (2021) state that the goal of personalized teaching is to achieve effective knowledge acquisition aligned with student's strengths and overcome their weaknesses to reach the desired objective. Through the inclusion of AI in an educational platform, we can accurately acquire the student’s characteristics. This inclusion is achieved through observing the student’s history and past experiences, identifying patterns and similarities, and analyzing large volumes of data. The recommendation of appropriate content, a long-term curriculum, and the creation of accurate performance assessments can become a reality in education systems. This improves learning but also predicts areas where the student may struggle, providing personalized and real-time support.Thus, AIEd could fill current gaps in the education system, enabling teachers to create personalized learning for each student’s profile. As Luckin and Holmes (2016) suggest, AIEd could take over bureaucratic tasks currently assigned to teachers allowing them more time for creative and inherently human activities that are essential to elevating the quality of the learning process.This study aims to validate the effectiveness of these possibilities through a User Research method. To this end, and with a sample of elementary school students, a comparative performance study was conducted using traditional teaching methods and an AI-generated personalized teaching method.In the traditional teaching method, the same exercise was presented to all students, while in the AI-generated teaching method, a personalized exercise was presented according to each student's needs, characteristics, and learning level, with the same exercise being presented in completely different ways to each student.The results were analyzed qualitatively to assess the effectiveness of the AI-generated personalized teaching method.The study concluded that AIEd better aligns with students' educational needs, improving their development. Although the tests reveal promising developments for the future, technological and ethical barriers remain that must be addressed to ensure this approach is sustainable and inclusive across different educational contexts.References: Perrenoud, P. (1978). Das diferenças culturais às desigualdades escolares: a avaliação e a norma num ensino indiferenciado. In Allal, L., Cardinet, J., Perrenoud, P. (1986). A avaliação formativa num ensino diferenciado. Coimbra, Livraria Almedina, pp. 27-73.Hockett, J. A. (2018). Differentiation Strategies and Examples: Grades 6-12. Tennessee Department of Education. Alexandria, VA: ASCD.Luckin et al. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson Education.Maghsudi, S., Lan, A., Xu, J.,e van Der Schaar, M. (2021). Personalized education in the artificial intelligence era: what to expect next. IEEE Signal Processing Magazine, 38(3), 37-50.Chen, X., Xie, H., Zou, D., e Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence.

Keywords: Personalised Education, Traditional Education, Artificial Intelligence, User Research

DOI: 10.54941/ahfe1005946

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