2 citations · 3 across the 9 of their papers we have counts for
10 papers
Your Programming Students' Cognition with ChatGPT: Higher Performance, Lower Retention, and Reduced Ownership
Christian Bergh, Benjamin Tag, Alexandra Vassar +1
Generative AI can improve students' programming performance, but successful task completion may not reflect what they retain. We examined performance, retention, cognitive load, an…
Mitigating AI Risks in Computing Education via LLM-Driven Lecture Video Curation
Owen Tang, Alexandra Vassar, Jake Renzella
This study evaluates the effectiveness of utilising large language models (LLMs) to retrieve targeted segments from delivered video recordings to answer student questions in introd…
Reshaping Undergraduate Computer Science Education in the Generative AI Era
Yi-Chieh Lee, Nattapat Boonprakong, Yugin Tan +20
Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical…
Gender Differences in AI Literacy Workshop Outcomes and Deepfake Engagement
Jake Renzella, Christian Bergh, Natasha Banks +1
As Artificial Intelligence (AI) literacy initiatives expand in K-12 settings, understanding how gender shapes student baseline perceptions, tool-use, and responsiveness to interven…
AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventions
Christian Bergh, Alexandra Vassar, Natasha Banks +2
Deepfakes and other forms of synthetic media pose growing safety risks for adolescents, yet evidence on students' exposure and related behaviours remains limited. This study evalua…
Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
Lorenzo Lee Solano, Charles Koutcheme, Juho Leinonen +2
Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over…
Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education
Emily Ross, Yuval Kansal, Jake Renzella +2
Large language models (LLMs) are increasingly being explored in higher education, yet their effectiveness as teaching agents remains underexamined. In this paper, we present the de…
Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
Alexandra Vassar, Jake Renzella, Emily Ross +1
This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hind…
Scaling CS1 Support with Compiler-Integrated Conversational AI
Jake Renzella, Alexandra Vassar, Lorenzo Lee Solano +1
This paper introduces DCC Sidekick, a web-based conversational AI tool that enhances an existing LLM-powered C/C++ compiler by generating educational programming error explanations…
Striking a Balance between Classical and Deep Learning Approaches in Natural Language Processing Pedagogy
Aditya Joshi, Jake Renzella, Pushpak Bhattacharyya +2
While deep learning approaches represent the state-of-the-art of natural language processing (NLP) today, classical algorithms and approaches still find a place in NLP textbooks an…