This paper interprets the theories of Vygotsky through the lenses of creativity in education and learning design using technology. It is important to address the role of the teacher in today’s technology-enhanced classrooms. This initiative has not been addressed, and it may lead to new ways of thinking about creativity in today’s teaching profession. Teachers’ roles have shifted from instructors to designers of effective scenarios for technology-enhanced learning. In this context, teachers need to be able to creatively connect pedagogy, content, and technology in learning design. The work of Vygotsky emphasizes the role of the teacher when assessing the learner’s developmental level, by tailoring the teaching so that it falls within the zone of proximal development. The procedure leads to new knowledge, indicating a creative process. Language as a tool now has new means to be transmitted—new technologies. Considering these connections, questions need to be re-addressed: What is the connection between creativity and the zone of proximal development? How has the learning design field shifted the role of the teacher and how does this relate to (1) the theory of cognitive development from a constructivist perspective and (2) the theory of the zone of proximal development? What are the connections between the new role of the teacher as a designer with creativity and technology in the zone of proximal development? In this paper, we synthesize the knowledge between creativity in learning design using technology and Vygotsky’s theories, concluding with an interpretation of how the teacher can be assisted by his theories and synchronous technologies in creative learning design.

In this article, we present a hybrid optimization methodology using the whale optimization algorithm and Markov Chain Monte Carlo sampling technique in a teachers’ training development program regarding creativity in technology-enhanced learning design. Finding the best possible training for creativity in learning design with technology is a complex task, as many dynamic and multi-model variables need to be taken into consideration. When designing the best possible training, the whale optimization algorithm helped us in determining the right methods, resources, content, and assessment. A further Markov Chain Monte Carlo-based approach helped us in deciding with accuracy that these were the correct parameters of our training. In this article, we show that metaheuristic algorithms like the whale optimization algorithm, validated by a Markov chain technique like Markov Chain Monte Carlo, can help not only in areas like machine learning but also in fields without structured data, like creativity in technology-enhanced learning design. The best possible training for a teacher’s professional development in creative learning design is collaborative, hands-on, and utilizes creativity definitions for the product along with technology integration learning design models.