This module is an example Investigative Futures Seminar for students considering Artificial Intelligence, Data Science and Big Data, or other AI-related interdisciplinary pathways. The module introduces students to the kinds of evidence, methods, tools, communication practices, ethical responsibilities, and learning behaviours that are expected when studying and applying Artificial Intelligence in academic, professional, and societal contexts. The module is not designed as an advanced Year 2 artificial intelligence, machine learning, mathematics, or programming module. Instead, it prepares students to enter such pathways with clearer expectations, stronger learning ownership, and a better introductory understanding of how AI is used in practice. Students will not be expected to build complex AI systems. They will instead learn how to understand AI applications, frame AI-related questions, evaluate evidence, use AI and digital tools responsibly, communicate introductory analysis, and reflect on their readiness for further study. Students will investigate an accessible AI-related challenge or application, such as generative AI in learning or work, AI assistants, recommendation systems, digital platforms, data-driven decision-making, creative AI tools, assistive technologies, health technologies, environmental sensing, smart cities, public services, or responsible AI-enabled innovation.
A. Explain, at an introductory level, key concepts, developments and real-world applications of artificial intelligence, and identify the knowledge, skills, and professional expectations associated with an Artificial Intelligence, Data Science and Big Data, or AI-related interdisciplinary pathway. B. Convert a broad interest or real-world challenge into a pathway-valid investigative question, and communicate a clearly framed AI-related idea or proposed application. C. Apply introductory AI-related evidence, methods, tools, or systems thinking to analyse an accessible pathway-relevant problem. D. Evaluate ethical, social, sustainability, safety, uncertainty, and professional responsibility considerations, and reflect on readiness for Year 2 study.
The module uses pathway seminars, case-based learning, introductory AI and digital tool exploration, socio-technical problem analysis, guided investigation, peer critique, short communication tasks, and reflective readiness activities. Students will develop a pathway-relevant investigation that allows them to practise introductory evidence use, AI/digital reasoning, responsible tool use, academic communication, and readiness reflection.