## 6 Conclusions and Future Remarks  
In this section, we will analyze the hypotheses we had when designing the WOA-MCMC approach to create an optimal solution for creative technology-enhanced learning design. Firstly, we address the research questions that were stated in the introduction section, and we answer them. RQ1: Is there a combination of methods, resources, content, assessment, and duration that is optimal when designing a TPD for creativity in technology-enhanced learning design? As written in this article, many variables need to be considered when addressing technology-enhanced learning design in a TPD. Training becomes even more complex when adding abstract and multifaceted themes such as creativity. The first thing we need to do is classify our components, so we know what we are searching for. These are a. Effective TPD components, b. Effective technology-enhanced TPD components and c. Effective TPD in teachers' creativity. For the first issue, we have a literature review that points out the following effectiveness criteria: content focus; active learning techniques; collaborative; they draw on effective practice models; coaching and expert guidance; time for feedback and self-reflection; and sustained duration. Based on these, we concluded on content, methods, resources, evaluation, models, and design factors. For the second cluster, which is the technology-enhanced LD in TPD, we must search for scientific literature regarding LD and technology. The literature has pointed out the technology integration design factors in TPDs. These are: evidence of student learning, relevance and reflection, support and follow-up, student voice, learner-centered, and initial implementation with teacher leaders. We consider them. Lastly, we focus on TPDs in teacher creativity. Research has pointed out a focus on understanding the concept of creativity and its practical implementation. Additionally, scaffolding creative risk, reflecting on creative resources, and developing creative routines are also important. We connect the dots by illuminating parameters that are not relevant or not in the recent highly cited bibliography. We enhance components by incorporating them with close solutions. We concluded with a collaborative method like TBL with application tasks (hands-on), like the co-creation of LDs and coaching through a clarification session. Additionally, content is related to NEW parameters enhanced by technology integration models like SAMR or TripleE. We have self and peer evaluation of the LD for the advancement of reflection through the rubrics of these models. Lastly, the sustained duration can easily be implemented through asynchronous material in an LMS, providing the necessary enhancement of time and flexibility. RQ2: Can WOA be used as a method to obtain optimal solutions when designing training in creative learning design? The WOA was very helpful, especially in the conceptualization of the process and in filtering the existing literature review. It can help with optimizing training programs because of its value in exploring and exploiting solution spaces efficiently. The educational research has a few examples of WOA. Especially when modeling, it could be a valuable and reliable tool and a novel approach. The Structured Equation Model (SEM) is dominant in statistical modeling in educational research and especially in teacher education. However, SEM is well-known for showing the relationships between factors already defined. The existence of pre-established concepts or hypotheses is crucial to SEM research [37]. However, creativity is a dynamic, complex, and multifaceted phenomenon. Thus, implementing algorithms, such as WOA, that help us solve complex and uncertain problems, could potentially add value to the optimization of processes, like designing an optimal training involving creativity. In this realm, theories are many theories, so we need to begin the process with all of them and all their interactions, and not only start from one theory, like in deductive modelling processes.