Aug 2026· Proceedings of the Canadian Engineering Education Association (CEEA)· 0 citations
TL;DR
Findings show students are generally comfortable using genAI, they feel they are using it effectively, that it increases their quality of work and efficiency, and feel it enhances their creativity.
Abstract
This research paper describes an initiative to supplement novice design knowledge with generative AI (genAI). Past studies show students feel they don’t have sufficient genAI knowledge and skills, and faculty are concerned about students’ ability to evaluate genAI. Although there is concern that students may become too reliant on genAI, research shows that genAI can support the development of and ultimately automate aspects of the engineering design process. This research identifies students’ perceptions of the use of genAI in engineering design courses. Building on the designer patterns described in Crismond and Adam’s Informed Design Learning and Teaching Matrix, this work explores genAI’s impact on engineering design. Findings show students are generally comfortable using genAI, they feel they are using it effectively, that it increases their quality of work and efficiency, and feel it enhances their creativity. Students reported mixed messages from instructors and concerns about ethical use, unfair advantage, and over-reliance.
The rapid emergence of generative artificial intelligence (GenAI) is reshaping engineering education. This study investigates the impact of integrating GenAI tools on student learning in the second-year engineering design course at the University of Prince Edward Island by comparing two instructional approaches: a traditional non-AI design project and a GenAI-integrated design project. The study utilized anonymous, voluntary student surveys to evaluate and compare students' learning experiences across both projects. The survey captured both quantitative and qualitative insights into how students perceive the role of GenAI in their learning, collaboration, creativity, and problem-solving processes. Findings show that students most frequently used GenAI for brainstorming, problem definition, and concept generation, while strongly engaging with ethical verification practices. However, perceptions of GenAI’s impact on collaboration and enhancing the efficiency of the design process were mixed. These results inform future learning outcomes, assessment strategies, and broader approaches for responsibly integrating GenAI across engineering programs.
K. Grewal, Mikkayla Ellsworth-Reid, Prabhnoor Sigh et al.· Proceedings of the Canadian...· 0 citations
This study investigates student teachers’ interaction with generative artificial intelligence (genAI) while working on their final thesis projects. The aim of this study is to contribute knowledge on what happens when genAI becomes integrated into academic writing. Previous research indicates that AI tools are deemed acceptable for early stages of writing, such as brainstorming or outlining. However, there is still a lack of knowledge about how students use genAI while working on an actual assignment. Applying a sociomaterialistic approach, student teachers’ self-reported use of genAI has been analysed qualitatively. In total, 92 students participated in the study, generating 46 reports. The results indicate that AI on a general level is used to transform information between different languages, for example, from everyday to academic language. The Compensatory model is proposed to describe when genAI becomes integrated into academic writing. The skills students need but do not have can be compensated with an appropriate use of genAI, so that content is co-created and valued by the students.
Karin Stolpe, Sanna Hedrén· Journal of Praxis in Higher...· 0 citations
Purpose. This study investigates how generative artificial intelligence (GenAI) influences L2 instructors’ perceptions of creativity and effectiveness in lesson planning and material design for writing instruction. It also examines factors motivating continued or discontinued use of GenAI. While participants reported using several GenAI tools, ChatGPT clearly emerged as the dominant one. Method. A mixed-methods research design was used. Data were collected from adult L2 instructors in Canadian tertiary programs using survey and semi-structured interviews. The survey included closed- and open-ended questions, while interviews provided deeper insights into instructors’ experiences with GenAI tools. Data were analyzed using descriptive statistics for quantitative responses and thematic analysis for qualitative data. Findings. The findings reveal that GenAI plays a dual, context-dependent role. Most participants reported that GenAI influenced their creativity, particularly by supporting idea generation, material development, and pedagogical innovation. However, concerns about overreliance, reduced originality, and formulaic outputs were also reported. The impact on effectiveness was more conditional, with benefits in time management, customization, and teaching capacity balanced by challenges such as prompting complexity, the need for editing, and variable output quality. Decisions to continue or discontinue GenAI use were influenced by practical benefits, professional growth, and ethical and financial concerns. Implications for research and practice. The study highlights the importance of AI literacy, institutional support, and critical engagement for effective GenAI implementation. It suggests that GenAI should be used as a collaborative tool. Future research should explore long-term impacts and include diverse educational contexts and learner outcomes.
Generative AI tools such as ChatGPT, Claude, and Gemini are increasingly shaping how students engage with software engineering (SE) problem-solving. However, there is limited understanding of how learners collaborate with GenAI across different stages of the software development lifecycle (SDLC), and how learner agency shifts during this process. This research unpacks the problem-solving strategies, collaboration patterns, and agency shifts underlying human–GenAI collaboration in software engineering education. Using a mixed-methods approach, it draws on multimodal data including screen recordings, GenAI transcripts, task artefacts, and retrospective think-aloud interviews. Preliminary findings suggest that learners engage with GenAI differently across SDLC phases, with variation in reliance, evaluation, and decision-making. Future work will extend this analysis to larger and more diverse learner groups and inform pedagogical supports for effective, critical, and agentic GenAI-supported problem-solving.
Sonika Pal· Proceedings of the 2026 ACM...· 0 citations
This paper presents the development and initial validation of an instrument to measure self-efficacy while using GenAI to learn programming, and finds strong support for the validity of the existing Steinhorst instrument in a new context, specifically an introductory programming course that fully integrates GenAI.
J. Prather, Lauren E. Margulieux, Yekaterina Kharitonova et al.· International Computing Educ...· 0 citations
This research explores the use of a latent Dirichlet allocation model to automatically classify students' design reflections, thereby improving the efficacy of their reviews, and advocates for professional engineering licensure bodies to modernize policies to encourage thoughtful, rigorous evaluation of AI models before deployment.
Brian Macdonald, Sister Libby Osgood, Christopher Power· Proceedings of the Canadian...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
