Education Sciences

Engineering programmes have been giving more weight to experiential learning, largely because many students still find it difficult to see how classroom theory connects to the work that engineers handle on the ground. With this in mind, a robotics-centred Project-based Learning (PBL) module was introduced to first-year general engineering students as part of the faculty’s engineering spine. The module asks students to design, build, and program small autonomous robots capable of navigating and competing in a set arena. Even a simple task of this kind draws together multiple strands of engineering. Students shift between sketching mechanical layouts, wiring basic circuits, writing code, testing prototypes, and negotiating the usual challenges that arise when several people share responsibility for the same piece of hardware. To explore how students learned through the module, a mixed-methods evaluation was carried out using survey responses alongside reflective pieces written by the students themselves. Certain patterns appeared repeatedly. Many students felt that their technical skills had grown, particularly in breaking down a messy problem into smaller, more workable components. Teamwork also surfaced as a prominent theme. Groups often had to sort out issues such as a robot veering off course due to a misaligned sensor or a block of code producing unpredictable behaviour. These issues were undoubtedly challenging for the students, but they also had a certain pedagogical flavour, with many students describing them as a source of frustration as well as a learning opportunity. Later iterations of the module may benefit from more targeted support at key stages. Despite the many challenges, robotics has been shown to be an attractive way for students to step into engineering practice. The project helped them build technical capability, but it also encouraged habits that matter just as much in real work, such as planning, communicating clearly, and returning to a problem until it behaves as expected. Taken together, the experience offers useful guidance for curriculum designers seeking to create early learning environments that feel authentic and manageable and for motivating students who are just beginning their engineering journey.

Undergraduate civil engineering students frequently struggle to transition from deterministic to probabilistic reasoning, a conceptual shift essential for modern structural design practice governed by reliability-based codes. This paper presents a design-based research (DBR) contribution and a theoretically grounded pedagogical framework that integrates AI-powered conversational tutoring with interactive simulations to scaffold this transition. The framework synthesizes cognitive load theory, scaffolding principles, self-regulated learning research, and threshold concepts theory. The design incorporates three novel elements: (1) a structured misconception inventory specific to structural reliability, derived from literature and expert elicitation, with each misconception linked to targeted intervention strategies; (2) an integration architecture connecting large language model tutoring with domain-specific simulations, where simulation states inform tutoring and misconception detection triggers targeted activities; and (3) a scaffolded module sequence building systematically from deterministic foundations through probability concepts to reliability analysis methods. Sequential modules progress from uncertainty recognition through Monte Carlo simulation and design applications. We provide technical specifications for the implementation of AI tutoring, including prompt engineering strategies, accuracy safeguards that address known limitations of large language models (LLMs), and protocols for escalation to human instructors. An assessment framework specifies concept inventory items, process measures, and practical competence tasks. Ultimately, this paper provides testable conjectures and identifies conditions under which the framework might fail, structuring subsequent empirical validation with student participants following institutional ethics approval.

This study examines how undergraduate design students imagined and critiqued biotechnological futures through speculative work with generative AI in a semester-long biodesign course. Using inductive qualitative coding and visual discourse analyses, we traced how students’ prompts, images, and reflections reveal an evolving grammar of speculation. Students shifted from crisis description to design-oriented possibility and socio-political reasoning about ecological, cultural, and ethical implications. Generative AI supported this shift by offering visual feedback that enabled students to recognize assumptions and critically examine speculative designs. Through repeated cycles of prompting and refinement, students advanced biodesign prototypes and developed a nuanced understanding of AI’s affordances and limits. Extending constructionism learning theories into speculative design with generative AI, this study examines how learners externalize discursive and imaginative thought through prompt-crafting. These findings articulate a grammar of speculation, showing how generative AI mediates critical AI literacy as a discursive and constructionist learning process.