This paper presents a novel AI-driven conceptual smart model designed to help organizations enhance workforce professional development by upskilling and reskilling employees while fostering job satisfaction and staying competitive in their markets; this novel model is called Professional Development Analytics (PDA). The model’s main focus is to provide a new design model that concentrates on how artificial intelligence (AI) can optimize personalized training and how it can improve employees’ technical and soft skills, enabling companies to create their talent map at the same time. By compiling personnel data and their roles within the company, AI is able to create detailed and personalized profiles. In the next stage, this information is classified, analyzed, and used to enhance current skills while also predicting future training needs. These processes result in the creation of personalized learning paths, where AI recommends customized courses tailored to each employee’s unique needs. The system will be automatically fed and adjusted by means of the gathered data and continuous feedback from the employees and their supervisors. The proposed AI tools are powered by machine learning, deep learning, natural language processing, generative AI and data analytics. Our model aims to support learning and development departments by delivering precise, personalized training solutions that address employees’ unique needs, enabling skill development and professional growth through an automated and customized process.

This paper introduces Educational Conceptual Model Design a new research methodology to develop conceptual models in the field of educational technology. It is based on principles from Conceptual Design Methodology, Conceptual Modeling Methodology, Design-Based Research, and Design Science Research. It is a systematic methodology structured in four phases: problem definition, conceptual foundation, model development, and knowledge contribution. This methodology is designed to be streamlined and grounded in theory, focusing on consistency, replicability, and alignment with theoretical rigor. The methodology is particularly suited to respond to rapid advancements in technology and continuously evolving educational challenges, including the integration of emerging tools such as Artificial Intelligence in Education. Its main characteristic is that it offers a transition from problem identification to practical models with a special emphasis on the design process. This approach reduces the complexity of developing a model while maintaining its applicability across different educational technology areas, which can range from adaptive learning systems to data-driven learning analytics. The paper concludes by discussing the methodology’s contributions to theory and practice, its limitations, and potential future directions, including scalability, cross-disciplinary integration, and the incorporation of disruptive technologies. This methodology serves as a foundational framework for researchers and practitioners aiming to design innovative forward-thinking solutions that meet the dynamic needs of modern education.

Teacher Professional Development has historically been a challenging field, with several requirements such as the personalisation of training to meet teachers’ specific needs. With the rapid advancement of AI, there is now potential that points toward innovative approaches to these challenges. This paper introduces the term AI-TPD to describe the use of AI technologies to support and enhance Teacher Professional Development. A Scoping review was conducted following the PRISMA-ScR checklist and guidelines to identify the state of research of AI-TPD, to detect what are its current applications, to address the main challenges and risks it faces, and to highlight the emerging trends and future directions. The review found that AI applications primarily focus on personalized learning paths, real-time feedback, instructional design support, and fostering teacher reflection and practice. Reported benefits include increased adaptability and responsiveness of Professional Development, while challenges centre on ethical concerns, data privacy, AI distrust, and readiness. Evidence also highlights that AI-TPD is in an exploratory phase with limited theoretical frameworks and long-term evidence. The results map current applications of how AI can be used to make Professional Development more responsive and relevant in diverse teaching contexts. Recommendations for further research to continue exploring AI-TPD are also provided.