This module serves as a pathway-readiness provision for students intending to progress into IoT or other computing‑related degree programmes in Year 2. It introduces students with no prior programming experience to the foundational ideas of computing, using an AI‑first approach. Instead of teaching manual syntax memorisation, it focuses on how to decompose problems, formulate effective prompts for AI assistants, critically evaluate and debug AI‑generated code, and reflect on the division of labour between human reasoning and machine automation. Students build a durable conceptual understanding of computation, including states, variables, instructions, branching, iteration, abstraction, data organisation, algorithmic thinking, resource awareness, and limits of computability, without being required to become fluent manual coders. By the end of the module, students are able to assess AI outputs with confidence, define tasks with clarity, and articulate a grounded sense of direction for further study.
A. Assess the outputs of AI agents for accuracy, completeness, and task appropriateness, and formulate basic improvements through natural language. B. Formulate unstructured problems into clear, actionable computational tasks that an AI agent can execute. C. Discern the relative strengths of AI agents and human reasoning across different types of computational tasks, and apply this discernment to personal academic direction.
The teaching philosophy of this module follows the principles of Syntegrative Education, emphasising project‑based learning, portfolio‑based evaluation, and industry‑relevant problem solving over timed examinations. The module adopts an AI‑first, student‑centred approach that positions AI as a collaborative learning partner rather than a substitute for human reasoning. It is delivered through a combination of lectures and lab sessions. Lectures introduce computing concepts using real‑world examples and AI‑generated code, focusing on conceptual understanding, use cases, and design reasoning. Each lecture includes structured discussion and guided examples to help students develop skills in assessing AI outputs and refining task descriptions. Labs provide supervised practice where students work with AI assistants to solve problems grounded in AIoT scenarios. Through iterative cycles of task formulation, output evaluation, and natural‑language refinement, students develop judgement, task‑definition skills, and reflective practice. Throughout the module, students maintain a log of their AI interactions, which serves as both a learning tool and the basis for assessment.