6 papers
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…