The module aims to provide foundational readiness for students to further study in the area of AIoT in Year 2. It follows a foundational year spine structure with the module focusing on developing AIoT pathway specific student skills in the key areas of academic practice, AIoT-pathway evidence collection and development, AI/digital-based inquiry and judgement within the context of AIoT, AIoT-related professional responsibility, and developing basic technical skills such as hardware development and integration, coding, AI integration in AIoT and so on. The module fits within the wider framework of year 1 delivery as an Investigative Futures Seminars (IFS) module.
A. Use academic knowledge, literacy, communication and evidence skills to investigate and explain AIoT-related problems, methods, and applications. B. Demonstrate self-directed learning, time management, technical communication, problem solving, evaluation, technical reflection and ethics awareness within the engineering context through an AIoT-focused pathway. C. Apply foundational technical and AI skills required in the context of AIoT-related study, through demonstrating basic proficiency in simple AIoT device configuration, sensor use, simulation, experimentation and/or programming. D. Present and evaluate individual work, explaining decisions and recognising personal development needs. .
The module will be delivered through one interactive lecture per week over 13weeks duration for pathway specific skill development and implementation. The lectures will allow students to explore and implement AIoT-pathway specific knowledge and understanding. Students will learn the theoretical foundations of AIoT, integration of AI into IoT, AIoT solution development and hands-on knowledge of key elements and components to implement and support this development. The lectures will also cover range of methods, tools and techniques to develop AIoT + X pathway specific technical and non-technical skills such as impact/technical case studies, white papers, technical design and evaluation exercises, technical sources/evaluation, 3D models/artefact/prototype(s)/experiments, AIoT-specific standards, data and models’ exploration, Generative AI tools and methods in the context of AIoT, sustainability and best practices evaluation in the field of AIoT engineering. The learning and teaching will directly align with IFS requirements for semester 2 around the areas such as i) pathway specific knowledge and evidence building ii) mastering the use of AI/digital practice iii) interpersonal and intrapersonal skills development iv) development of ethical consideration and practices within the field of AIoT v) Year 2 readiness synthesis. Finally, the lectures will highlight future prospects of taking on AIoT pathway that is integrated within the Syntegrative Education Framework. Students will be assessed through a single integrated individual CW combining two separate elements i) focus on demonstrating the technical pathway-specific knowledge, and ii) an integrated portfolio, to evaluate and cover all the Learning Outcomes (LOs) of the module.