This module provides an introduction to the core principles and concepts of AI for non-technical students. This course covers the historical evolution of AI, its interdisciplinary nature, various applications in everyday life, and the basic technical foundations of generative AI. Students will explore fundamental AI topics such as machine learning, natural language processing, and computer vision. Through interactive lectures, case studies, and discussions, participants will gain a comprehensive understanding of AI's capabilities and limitations, preparing them to critically evaluate and engage with AI technologies in diverse professional contexts.

1. Critically assess the fundamental concepts, history, and evolution of AI.
2. Differentiate between key AI technologies, including machine learning, natural language processing, and robotics.
3. Evaluate real-world applications of AI across different industries.
4. Integrate concepts from probability theory to explain the mechanics of generative AI models
5. Critically assess opportunities and challenges presented by AI in their own professional domain.

The Ethics and Responsible AI in Business module offers a critical exploration of the ethical challenges and responsibilities associated with the use of AI in the corporate world. Designed for non-technical students, this course delves into the ethical principles guiding AI development and deployment, including fairness, transparency, accountability, and privacy. Through case studies and real-world examples, students will examine the societal implications of AI technologies, learn about regulatory frameworks, and discuss best practices for fostering ethical AI in business operations. The module equips students with the knowledge and tools to ensure that AI applications are implemented responsibly and sustainably, aligning with broader ethical standards and organizational values.

1. Critically analyse key ethical and cyberethical principles related to AI and evaluate their application in business contexts.
2. Evaluate real-world AI ethics challenges and their impact on businesses and society.
3. Critique legal and regulatory frameworks governing AI use in different industries, including the need for ongoing adaptation in response to technological advancements
4. Assess and propose mitigation strategies for the risks of AI bias, discrimination, and misinformation in business applications.
5. Develop and justify strategies for responsible AI governance and ethical decision-making in a professional domain.

The AI and Workplace Collaboration Tools module introduces students to the transformative role of artificial intelligence in enhancing workplace productivity and collaboration. This course explores a variety of AI-powered tools designed to facilitate communication, streamline workflows, and improve team dynamics. Students will learn about the integration of AI in project management, virtual assistants, and collaborative platforms, as well as the benefits and challenges associated with these technologies. Through interactive sessions and practical examples, participants will gain insights into how AI can be leveraged to create more efficient, innovative, and cohesive work environments, preparing them to effectively utilize these tools in their professional careers.

1. Discuss how AI enhances workplace collaboration, communication, and productivity.
2. Critically evaluate the strengths and limitations of AI-powered workplace tools.
3. Assess the ethical and data privacy implications of AI-enhanced collaboration platforms.
4. Evidence proficiency in using AI-based tools for communication, project management, and automation.
5. Develop strategies for integrating AI-driven collaboration tools within their professional environments.

The AI and Data Visualisation module equips non-technical students with the skills to harness the power of artificial intelligence for effective data interpretation and presentation. This course focusses on the intersection of AI and data visualisation, demonstrating how AI can enhance the analysis, understanding, and communication of complex data sets. Students will explore various AI-driven visualisation tools and techniques, learning how to create compelling and insightful visual narratives that support decision-making processes. Through hands-on projects and case studies, participants will gain practical experience in leveraging AI to transform raw data into visually engaging and easily interpretable formats, enabling them to convey data-driven insights with clarity and impact in their professional roles.

1. Explain the role of AI in data visualisation and decision-making.
2. Create and interpret data visualisations using AI-powered tools
3. Critically assess common pitfalls and biases in AI-generated visual data.
4. Apply principles of effective visual storytelling to communicate insights.
5. Evaluate the ethical and practical implications of AI in data representation.

The Integrating AI in Business Practices module provides students with a comprehensive understanding of how to effectively incorporate artificial intelligence into various business operations. This course covers the strategic implementation of AI technologies to enhance efficiency, innovation, and competitiveness across different business functions such as marketing, finance, supply chain management, and customer service. Students will explore real-world case studies and best practices for adopting AI solutions, while also considering the challenges and risks involved. By the end of the module, participants will be equipped with the knowledge and skills to strategically plan and manage AI integration, ensuring that it aligns with organizational goals and delivers measurable business value.

1. Assessing the role of AI in different business domains, including marketing, finance, HR, and supply chain management.
2. Evaluate AI-driven business tools and their impact on efficiency, productivity, and customer engagement.
3. Assess the ethical, legal, and strategic implications of AI adoption in business.
4. Develop an AI integration strategy for a business case, balancing innovation with risk management.
5. Critically analyse real-world AI applications to determine best practices and potential pitfalls.

The Predictive Analytics for Business Decisions module introduces students to the powerful role of predictive analytics in driving informed business strategies. This course covers the fundamental concepts and techniques of predictive analytics, illustrating how historical data can be used to forecast future trends and behaviours. Students will learn about various predictive models and their applications in different business contexts such as marketing, finance, and operations. Through practical examples and case studies, participants will gain insights into how to interpret and leverage predictive analytics to make data-driven decisions that enhance business performance and competitiveness. By the end of the module, students will be equipped with the skills to apply predictive analytics to real-world business scenarios, transforming data into actionable insights.

1. Examine the role of predictive analytics in business strategy and decision-making.
2. Interpret, implement, and apply predictive analytics models in different business contexts.
3. Critically evaluate AI-driven predictive tools and their implications for efficiency and risk management.
4. Identify ethical concerns, biases, and limitations in predictive analytics.
5. Develop data-driven business recommendations based on predictive insights.

This module provides learners with the opportunity to carry out an independent, in-depth research project in the field of Applied Artificial Intelligence. Building on prior learning in areas such as AI foundations, ethical AI use, data visualisation, predictive analytics, and AI integration in business contexts, students will explore a real-world problem relevant to their workplace, sector, or area of interest. Through the development of a research proposal, application of appropriate methods, and critical analysis of findings, learners will demonstrate their ability to conduct applied research and communicate its outcomes professionally. The module supports autonomy, critical thinking, and the practical application of AI in decision-making, business processes, and collaborative technologies.

1. Critically evaluate current research, industry practices, and ethical considerations to define a research question in applied artificial intelligence.
2. Design and justify a coherent, ethically sound research methodology appropriate to investigating an applied AI problem or opportunity.
3. Apply and adapt AI methods, tools, or frameworks from the taught programme to address a complex, real-world issue in a business or organisational context.
4. Analyse and synthesise research data or outputs to derive meaningful insights relevant to the application of AI in practice.
5. Demonstrate autonomy and self-direction in managing a substantial and sustained research project, including reflective practice and problem-solving.
6. Produce and communicate a well-structured, evidence-based dissertation that demonstrates originality, critical reasoning, and implications for future AI practice or research.

This module enables learners to independently apply their knowledge of applied artificial intelligence to a real-world industry challenge. Working with an external organisation or a defined problem area, students will design and implement an AI-based solution or analysis, demonstrating practical skills and ethical awareness.

It will capture the actions conducted by the learner on their applied industry project and the learning associated with these actions. It necessitates that the learner critically reflects on their own work practices and demonstrates benefits for improvement or changes to original practices or processes.

1. Identify and evaluate how value can be added to the context of an organisation, resources, and competitive position
2. Assess the context of the work in terms of how it influences the environment and general public
3. Apply AI in the Workplace concepts in the context of an organisation to promote development and improvement
4. Critically evaluate an organisations operations and assess subsequent implications
5. Reflect on knowledge, skills and competences attained in relation to personal and professional development
6. Summarise project outcomes in an appropriate format for management review
7. Prepare and present a professional portfolio of work.