Artificial intelligence for personalized learning: a systematic literature review
Glenn Hardaker & Liyana Eliza Glenn
What the paper says
Purpose The purpose of this systematic literature review is to identify the antecedents that have enabled the adoption of artificial intelligence (AI) in Higher Education (HE) institutions at both a macro and micro level. The term adoption is in reference to the diffusion of technology that is actively chosen for use by the targeted demographic. Within the context of this paper, adoption is largely referring to the factors that influence the acceptance and use of AI as a tool for personalized learning. Design/methodology/approach To develop our understanding and appreciation of the valuable impact that AI potentially has upon personalized learning the following systematic literature review was conducted. An acceptable systematic literature review is a comprehensive method of fully analysing and evaluating all available research in the chosen area or specific research query. Findings The findings from this study have particular implications for personalized learning in the adoption and diffusion of AI and an increasing integration of macro, structural, and micro, individual. Developing and managing AI in education is seen, from the literature, to becoming more embedded in the teaching and learning process. The paper identifies the following: antecedents that supports the adoption of AI for personalized learning; application of AI technologies in the teaching and learning process; AI technologies that enable personalized instruction and learning; generative AI that supports intuitive learning through tracking data. Originality/value Personalized learning remains focused on customizable “choice-driven” learning and education. In addition, personalized learning and instruction is defined as being a responsive and structured method that adapts to each individual learner’s method of learning so that all may achieve their capabilities and actively participate. This solidifies the intrinsic connection between teaching and learning through personalized technologies such as AI.
27 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.76 × 0.4 = 0.30 |
| M · momentum | 1.00 × 0.15 = 0.15 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.