In this study, the researcher proposed a theoretical grounded vision for developing an adaptive e-learning environment that personalize learning experiences based on artificial intelligence and its suitability from the perspective of experts. Nevertheless, the theoretical grounds behind such environments continue under investigation in the literature, particularly in the context of Jordan. Based on established theoretical frameworks—including constructivist theory, self-regulated learning theory, and connectivism theory- the proposed vision integrates AI-driven personalization mechanisms within a coherent educational structure. A descriptive–analytical research method was used. A questionnaire with 65 items distributed on five domains was answered by a purposive sample of 200 faculty members out of the entire population of 315 from three public universities in Jordan. Both the validity and the reliability of the questionnaire were verified and secured. The results revealed that the overall degree of suitability was high ( M = 3.18, SD = 0.46) from the experts’ point of view. In addition, there was no statistically significant differences in experts’ estimates due to their teaching experience, or to the number of training courses they taught, except for a limited effect of gender in favor of females ( M = 3.21). The main contribution of the study lies in its clear explanation of how to apply multiple learning theories through AI techniques to enhance adaptive e-learning environments that address individual differences among learners while maintaining educational coherence, and to develop a conceptual framework explicitly mapping learning theories to AI implementation mechanisms. The researcher recommends adopting the ideas presented in the proposed vision for developing adaptive e-learning environments in Jordanian universities based on artificial intelligence–driven technologies.
Sanaa Banat (2026) studied this question.