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A UK-based educational organization is seeking a self-employed contractor to serve as an Online Tutor for their MA in Artificial Intelligence program. This part-time role involves leading online webinars, engaging with up to 30 students, and responding to academic queries. Ideal candidates should have experience in education and familiarity with AI concepts. Flexible hours with set key dates.
CEG Digital wishes to engage the services of a self‑employed contractor as an Online Tutor for our MA in Artificial Intelligence, running in partnership with the University of Southampton.
The Online Tutor plays a role in the success and vibrancy of the University's online programmes and is an academic presence in the modules to which they are assigned. They are usually the first point of contact for academic queries from students. Each Online Tutor will have a group of up to 30 students. During a typical week, the Online Tutor will lead a 1‑hour online webinar, engage with students through various asynchronous online activities and respond and triage student queries. Online Tutors also first‑mark summative assessments in‑line with the University's assessment regulations.
This is a flexible, part‑time, role. Note that there will be key dates and contact points you will be required to adhere to which are driven by assessment dates in the course calendar. Full details will be provided prior to the start of the module.
This MA conversion programme has been designed for professionals in any sector, including healthcare, agriculture, banking, or beyond. Students may not have any prior AI qualifications or experience. Studying machine learning and statistical learning as well as AI's social impact, this course will provide a comprehensive foundation to help students learn to use AI responsibly and effectively through one of three pathway options: MA Artificial Intelligence, MA Artificial Intelligence (Criminal Justice Systems), and MA Artificial Intelligence (Digital Transformation).
This module demystifies machine learning by exploring a broad range of techniques and their real‑world applications. Students learn to distinguish between statistical modelling and machine learning approaches, assess machine learning properties for specific tasks, and understand the uncertainty inherent in machine learning outputs. It covers predictive modelling, ensemble methods, representation learning, and data organisation.
This module critically explores the role of AI‑driven surveillance and predictive technologies in shaping modern criminal justice practices. With insights from criminology, law, sociology, and Science and Technology Studies (STS), students examine the opportunities, challenges, and regulatory issues surrounding AI applications in justice systems worldwide. By comparing AI applications in justice systems across the Global North and the Global South, students get a global view of its impact on society.
This module explores how artificial intelligence is revolutionising job roles, organisational structures, and labour markets within the modern workplace. It addresses the broad impacts of AI‑driven automation and augmented human intelligence, emphasising ethical, social and economic dimensions. Through a blend of theoretical foundations and real‑world case studies, students explore the opportunities and challenges presented by AI. This will prepare them to harness its potential for innovation, skill evolution, and organisational transformation effectively.