Analyzing the Impact of Artificial Intelligence in Personalized Learning and Adaptive Assessment in Higher Education

Analyzing the Impact of Artificial Intelligence in Personalized Learning and Adaptive Assessment in Higher Education

 Bundit Anuyahong1, Chalong Rattanapong2, Inteera Patcha3
1Assistant Professor Dr., Southport, QLD, Australia
2Business English Department, Faculty of Business Administration, Rajamangala University of Technology Rattanakosin, Wang Klai Kangwon Campus, Thailand
3English Education Program, Nakhon Pathom Rajabhat University

DOI: https://doi.org/10.51244/IJRSI.2023.10412

Received: 13 April 2023; Accepted: 21 April 2023; Published: 15 May 2023

Abstract: This This research aims to examine the impact of AI on personalized learning and adaptive assessment in higher education and investigate the ethical and social implications of using AI in these contexts. A mixed-methods approach was used, involving surveys, interviews, focus groups, institutional records, and system logs to collect both quantitative and qualitative data. The population included higher education institutions that use AI in personalized learning and adaptive assessment systems, as well as students and educators who use these systems.
The results of the study showed that AI-based systems had a positive impact on student engagement and motivation, as well as providing personalized learning experiences. However, the analysis also revealed some limitations and potential concerns, such as technical issues and the potential for bias in the AI algorithms used in these systems. Ethical and social implications were analyzed using ethical frameworks such as the Belmont Report and principles of distributive justice. To ensure ethical and socially responsible use of AI in personalized learning and adaptive assessment, clear guidelines and standards for the development and implementation of these systems need to be established. This includes promoting transparency and accountability in the use of student data, ensuring that algorithms are developed and validated in a fair and unbiased manner, and involving diverse stakeholders in the design and implementation of these systems to promote equity and fairness. Informed consent should also be obtained from students and other stakeholders, and measures should be taken to ensure that student data is kept confidential and secure. Ongoing monitoring and evaluation should be conducted to assess the impact of AI-based systems on student outcomes and to identify and address any unintended consequences or biases.

Keywords: Artificial Intelligence, Personalized Learning, Adaptive Assessment, Higher Education, Impact Analysis

I. Introduction

Artificial Intelligence (AI) has revolutionized many fields, including education. In higher education, personalized learning and adaptive assessment have become increasingly popular due to the potential benefits they offer. Personalized learning involves tailoring the educational experience to the individual learner’s needs and preferences, while adaptive assessment adjusts the level and type of assessment based on the learner’s progress. AI has the potential to enhance the effectiveness of personalized learning and adaptive assessment by automating processes and providing insights into student performance. However, there is a need to analyze the impact of AI in these areas to ensure that its use is effective and equitable (DeBoer et al., 2020).

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