Abstract
This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 level between the mean scores of the first experimental group's students using the global learning style-based adaptive e-learning environment and the second experimental group's students using the sequential learning style-based adaptive AI-enabled e-learning environment in the achievement test and observation checklist after measurement of digital content creative design skills in favor of the second experimental group's students. The study provided a number of suggestions and recommendations for making the utmost use of various design layouts of adaptive AI-enabled e-learning environments in developing different cognitive and performance aspects of learning as well as taking full advantage of digital content creative design skills mastery in producing a plethora of advanced electronic educational applications in the foreseeable future.
Highlights
Adaptive AI-enabled e-learning is a relatively recent educational term fundamentally based on analyzing each learner’s learning style and converting it into a unique model taking into account his cognitive level, needs, and interests in addition to identifying his requirements and preferred delivery modes for educational content and activities so that they can be transformed into an adaption model to master required knowledge and skills in a flexible adaptive way facilitating the learning process
Research Methodology. is research depended on using the pseudoexperimental method in order to identify the effect of using the proposed designed adaptive e-learning environment on developing digital content creative design skills among College of Education postgraduate students employing two different experimental treatments: the first is studying with global learning style and the other is studying with sequential learning style
It is against such backdrop that we can reject the first research null hypothesis in favor of its alternative hypothesis stating that “there is a significant difference at the 0.01 level between the mean scores of the experimental group’s students studying the proposed adaptive e-learning-based program, regardless of learning styles, in the achievement test both before and after measurements of the cognitive aspects of digital content creative design skills in favor of the postmeasurement mean scores.”
Summary
Adaptive AI-enabled e-learning is a relatively recent educational term fundamentally based on analyzing each learner’s learning style and converting it into a unique model taking into account his cognitive level, needs, and interests in addition to identifying his requirements and preferred delivery modes for educational content and activities so that they can be transformed into an adaption model to master required knowledge and skills in a flexible adaptive way facilitating the learning process. A plethora of previous studies in literature have concluded that using adaptive e-learning systems is effective in developing students’ cognitive achievement and practical skills As a result, they provide students with adequate opportunities to actively participate in and interact during the learning process and this, in turn, contributes to unleashing their hidden creative potentials and helps them to excel in learning, for example, the works of Omar [16]; Mohammed [17]; and Agbo and Oyelere [18]. It usually includes rapid response and high awareness of the existence of problems and obstacles that need solutions, whether radical or partial predicting and preventing problems before their occurrence based on existing data as well as finding them appropriate solutions within short periods of time [25]
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