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Transforming Skill-Based Education: Development and Evaluation of an AI-Assisted, Modular Platform Enabling Non-Technical Stakeholders to Create and Deliver Scalable, Standardized E-Learning Courses
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Consultantra Aktiebolag.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In today’s fast-changing world, there is a growing need for skill-based education that prepares individuals with practical, hands-on expertise. This shift is especially crucial in domains like mental health, where professionals require not only theoretical knowledge but also the ability to apply therapeutic techniques effectively. This thesis explores the development of CourseUltra, an AI-driven platform designed to simplify and enhance the process of creating courses in skill-based fields, with a pilot use case focusing on mental health education.

This thesis also explores the broader relevance of skill-based education in addressing global workforce gaps in sectors such as cybersecurity, renewable energy, and healthcare besides others. By providing a scalable solution for creating standardized, accredited courses, the platform may help improve the delivery of skill-based education across diverse industries.

The platform leverages advanced AI tools, such as large language models (LLMs), to help educators quickly develop high-quality, structured courses. By emphasizing a user-friendly, intuitive design, CourseUltra allows non-technical users, such as therapists and mental health practitioners, to craft comprehensive, accredited courses without needing to be tech experts. This research focuses on five key areas: how AI can assist in course creation, the effectiveness of user-centric design, the empowerment of non-technical users, the clarity of learning paths, and how well collaborative features help in the course creation process. We compare CourseUltra with two established platforms, LearnWorlds and MiniCourse Generator, using feedback from course creators and learners.

Our analysis shows that CourseUltra offers significant improvements, particularly in empowering non-technical educators, enhancing course structure, and making collaboration between creators easier. The platform also proves to be highly effective in the mental health domain, where training and certifying new practitioners requires both structured learning and practical application. By making it easier to build courses that focus on real-world skills, CourseUltra may support more effective course development in specialized fields, including mental health, and could be adapted for other critical sectors such as cybersecurity, renewable energy, healthcare and other skill sectors.

This thesis highlights the potential of AI to enhance and streamline skill-based education, starting with a pilot use case in the mental health industry in collaboration with a mental health organization BeingHealer. The findings offer valuable insights for educational institutions, professional training providers,and industries looking to adopt AI-powered platforms for skill development.

Place, publisher, year, edition, pages
2024. , p. 114
Keywords [en]
AI-assisted education, skill-based learning, course creation, mental health education, CourseUltra, user-centric design, non-technical educators, collaborative course development, AI in education, professional training, mental health skills, course standardization, accreditation compliance, modular architecture, concept based learning, personalized learning, concept mastery tracking, adaptive learning
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-137419OAI: oai:DiVA.org:lnu-137419DiVA, id: diva2:1947980
External cooperation
Anubha Rana; Consultantra Aktiebolag; Techstasy Technologies; Being Healer; Anita Sachdeva
Subject / course
Computer Science
Educational program
Social Media and Web Technologies, Master Programme, 120 credits
Supervisors
Examiners
Available from: 2025-04-09 Created: 2025-03-27 Last updated: 2025-04-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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