Investigating the Effectiveness of AI Tutors in Developing Personalized Learning Paths for Students

EasyChair Preprint 13031

Abstract

The rapid advancement of artificial intelligence (AI) has introduced novel opportunities for personalized education. This study investigates the effectiveness of AI tutors in developing personalized learning paths for students. The objective is to determine whether AI tutors can effectively adapt instructional strategies, content, and pacing to meet the unique needs of individual learners.

The research employs a mixed-methods approach, combining quantitative and qualitative data collection methods. A sample of students from diverse educational backgrounds is selected, and they are assigned to either an AI tutor or a traditional human tutor. The AI tutor utilizes machine learning algorithms and natural language processing techniques to analyze student data, including performance, preferences, and learning styles. Based on this analysis, the AI tutor tailors a personalized learning path for each student, providing customized content, instructional methods, and pacing.

Quantitative data is collected through pre- and post-assessments to measure students' academic performance, knowledge retention, and engagement levels. Additionally, surveys and interviews are conducted to gather qualitative insights on students' perceptions, attitudes, and experiences with the AI tutor.

Keyphrases: AI, machine learning, tutoring

@booklet{EasyChair:13031,
  author    = {Abil Robert},
  title     = {Investigating the Effectiveness of AI Tutors in Developing Personalized Learning Paths for Students},
  howpublished = {EasyChair Preprint 13031},
  year      = {EasyChair, 2024}}