• Artificial Intelligence
• Instructional Design
• Large Language Models

The Fourth Industrial Revolution has fundamentally reshaped the professional landscape, necessitating a new set of competencies to harness advanced technologies effectively. Marketing, among many fields, is undergoing a significant transformation due to the integration of data-driven approaches and artificial intelligence (AI). Large Language Models (LLMs), a critical development in AI, and the new generation of conversational agents are revolutionizing the way companies and individuals interact with digital environments. This thesis explores the potential applications of LLMs in educational contexts, aiming to bridge the skills gap between higher education and the demands of the labour market. The research is structured around five key objectives. First, it quantifies the skills mismatch between current educational outcomes and industry requirements, analysing job vacancies and university curricula to identify areas for improvement. Second, it investigates real-world interactions with AI chatbots, specifically ChatGPT, by analysing social media data to understand how users utilise LLMs in practical contexts. These insights inform the third objective: developing a systematic framework for task analysis, categorising user interactions with LLMs across various domains. The fourth objective applies this framework to instructional design, focusing on how LLMs can support or enhance tasks within the ADDIE model, a widely recognized methodology for instructional design. The analysis highlights the potential of LLMs to improve instructional design processes, offering more personalized and effective learning experiences. The final objective assesses the trade-offs between proprietary and open-source LLMs, guiding the selection of the most appropriate models for educational applications. A case study focused on needs identification demonstrates practical considerations for deploying LLMs in various domains, education included. Overall, this thesis presents a series of studies aimed at improving our understanding of AI-driven solutions in education, emphasizing the importance of aligning educational content with evolving industry standards, and contributing to more adaptable and future-ready educational systems.