In response to increasing student enrollment in computing courses, teaching assistants (TAs) now play a major role in student development. In particular, they are responsible for providing feedback to students, a critical factor in student engagement and performance. However, TAs often receive little formal training in how to provide effective feedback, resulting in inconsistent or poor-quality feedback that can hinder student learning and decrease motivation.

Cryptic compiler error messages continue to present a significant barrier for novice programmers, especially in foundational languages like C. Although large language models (LLMs) can generate accurate and comprehensible error explanations, their computational requirements, propensity for over-assistance, and privacy concerns constrain their suitability for widespread adoption in educational tools. This work investigates how Supervised Fine-Tuning (SFT) can enhance the performance of smaller, open-source models when explaining C compiler errors to students in introductory programming courses (CS1/2). We derive a training dataset of 40,000 input-output pairs from CS1/2 student C compiler errors to fine-tune three open-source models: Qwen3-4B, Llama-3.1-8B, and Qwen3-32B. Model performance was assessed through a dual evaluation framework involving expert human reviewers and a large-scale automated analysis of 8,000 responses using an ensemble of models as judges. Our results indicate that SFT significantly improves both expert and LLM-as-judge ratings in smaller open-source models, with reduced gains in the larger model. We analyse the trade-offs between model size and quality, and validate LLM-as-judge by demonstrating inter-rater agreement with experts. Our findings demonstrate that fine-tuning smaller models on high-quality data is a viable strategy for creating specialised pedagogical tools. We provide a replicable methodology for enabling broader access to advanced AI capabilities within educational contexts, especially with smaller, economical models.

Lengthy lectures can overwhelm a student's cognitive capacity, particularly in CS1 courses, where students often struggle with foundational programming principles. Bytesized is a lecture snippet tool that curates short, targeted videos from lecture recordings in response to student queries. We deployed Bytesized in a CS1 course in C, which revealed a high engagement with the tool as a way to supplement the consolidation of core concepts. The early results suggest that tools such as Bytesized are key in providing CS1 students with the necessary support to develop effective study strategies to consolidate fundamental programming teachings.

Delivering personalised and timely feedback is crucial for helping students address gaps in their understanding. However, the increasing demands of large class sizes make this task particularly challenging for CS1 educators, especially for casual teaching assistants who lack formal training and experience. Existing feedback training methods are often inconsistent and ineffective, leaving educators unprepared to handle diverse student needs.

As artificial intelligence (AI) continues to transform industries and impact daily life, it's crucial for K-12 students to be AI literate. However, many teachers feel unprepared to teach AI-related concepts. This session will focus on strategies to equip teachers with the knowledge and confidence they need to bring AI into their classrooms. Drawing on insights from the Day of AI, which has provided AI education to half a million students and professional development to thousands of teachers worldwide, the session will explore successful models for scaling teacher training in AI. Topics will include debunking common misconceptions about AI, addressing logistical challenges, and offering practical, adaptable resources that allow teachers to integrate AI into a variety of subjects. Attendees will have the opportunity to share their experiences and insights, particularly focusing on how to overcome barriers to teaching AI, such as lack of prior technical expertise or insufficient time to devote to professional development. The session will emphasise the importance of creating a collaborative and supportive network for developing and sharing AI teaching resources. Through interactive discussions, participants will collaboratively explore strategies to expand AI literacy and develop teacher confidence, aiming to create a sustainable model for equipping educators to teach AI at scale. This session will foster community engagement, promote shared learning, and serve as a launchpad for continued collaboration on AI education.

Students in introductory computing courses often lack the experience required to effectively identify and resolve errors in their code. For such students, Programming Error Messages (PEMs) are often the first indication of an error, and could provide valuable debugging guidance. However, in many cases, such as with standard C compiler implementations, PEMs are largely unsuitable for novices. Confusing, misleading, and filled with terse language and jargon, these messages instead act as an additional source of difficulty.

In the challenging field of introductory programming, high enrolments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --help tool, an integration of a Large Language Model (LLM) into the Debugging C Compiler (DCC) to generate unique, novice-focused explanations tailored to each error. dcc --help prompts an LLM with contextual information of compile- and run-time error occurrences, including the source code, error location and standard compiler error message. The LLM is instructed to generate novice-focused, actionable error explanations and guidance, designed to help students understand and resolve problems without providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We analysed a subset of these error/explanation pairs to evaluate their properties, including conceptual correctness, relevancy, and overall quality. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code. Our findings, observations and reflections following deployment indicate that dcc --help provides novel opportunities for scaffolding students' introduction to programming.

Design education aims to develop in students the confidence to apply engineering fundamentals to the design of products and systems and this can only be achieved through intensive education and exposure to real-life engineering problems. Current issues in teaching engineering design, include the resources and labour intensive nature needed for the subject. In practice, when developing a design, engineers are dependent on the situation at hand, so goals, problems and constraints are often ill defined and may change as the problem continues to unfold. There is no single ideal solution. Assumptions and estimations are required before each analysis step, and the results need to be evaluated against the desired functional output. Often, many analysis iterations are required before a suitable solution is found. When teaching, providing the same scenario requires that tutorial guidance must adapt to the particular solution that each individual student devises. Conventional online tutorials can help to combat some of these issues, but they are not able to track the student progress in detail, nor are they able to provide customisable feedback, based on student progress. The aim of the research is to develop engineering design software tools that can address key problems in current engineering design education and provide students with a more effective and enriching educational experience. This paper discusses a response to these issues in design education in engineering, in the form of adaptive tutorials, and puts forward the preliminary analysis of their success in helping students overcome the limitations of current design education.