Aug 2026· Proceedings of the Canadian Engineering Education Association (CEEA)· 0 citations
TL;DR
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.
Abstract
Reflections in engineering are a fruitful tool for encouraging lifelong learning and provide instructors with insight into how to adapt the learning environment to meet students' needs. However, reflections are impractical for large classes. This research explores the use of a latent Dirichlet allocation model to automatically classify students' design reflections, thereby improving the efficacy of their reviews. The model showed promising quantitative performance, but from a human reviewer's perspective, it lacked interpretability. The contradicting results encouraged a reflective discussion of the potential misuses and risks of artificial intelligence (AI), both within and outside the classroom, without a qualitative review. Based on the authors' experiences and lessons from this study, this paper advocates for professional engineering licensure bodies to modernize policies to encourage thoughtful, rigorous evaluation of AI models before deployment. This article was written to encourage engineering educators to utilize AI critically and responsibly.
A context is established in which reflective classroom surveys can help teachers implement process-based grading structures to empower students in their growth as authentic learners instead of defaulting to passive users of AI.
Erik N. Powel· Journal of Education and Lea...· 0 citations
Providing feedback to students is an important yet time-consuming part of teachers’ work. Advances in AI technologies, especially generative AI, have introduced innovative solutions to automate the feedback process. The use of AI for feedback has generated interest in higher education, mainly attributed to its potential to reduce teachers’ workload and enhance feedback timeliness in response to the massification of university education. However, there remains a gap in empirical research that focuses on the ethical implications of this practice. In view of this, the paper draws on a diverse dataset (i.e., university policy reviews, social media posts and interviews with university teachers and students) to extrapolate eight key areas of ethical considerations regarding teachers’ use of AI for feedback purposes. Building on these areas, we call for a more nuanced understanding of what it means for teachers to use AI for feedback, considering various contextual complexities such as the purposes of assessment, the features of student assignments, and the types of feedback automated by AI. The study also highlights the need to move beyond the binary question of whether teachers should use AI or not towards exploring how feedback activities and teacher AI use could be designed and operated in ways that maintain and even enhance care, trust, and human connections central to effective feedback processes. While the study focuses on teachers, promoting the ethical use of AI requires a collective effort from multiple stakeholders in and beyond higher education.
Jiahui Luo, S. Eaton· Journal of University Teachi...· 0 citations
The idea of AI-resilient assignments maintaining academic integrity with product- and process-based evaluation approach is proposed, similar to the open-book system, where they require human intelligence, independent thinking, and personal understanding to solve them correctly.
A. Sahu, Chandrakant Kumar Singh, A. K. Malik· International Journal of Sci...· 0 citations
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.
N. Nelson, C. Rennick, Silas Ifeanyi· Proceedings of the Canadian...· 0 citations
A narrative and strategic framework for student engagement that emphasises enjoyment, active participation, and personal development is developed by fostering environments where students experience intrinsic motivation and recognise the value of their own skills development.
John F. Grant· Journal of Scholarship of Te...· 0 citations
Artificial Intelligence (AI) has become an important tool in supporting students' academic writing. This study aimed to explore students' experience of using AI to improve their writing skills. A qualitative approach was employed through semi-structured interviews with 20 students of STKIP Muhammadiyah Aceh Barat Daya. The data were analyzed using thematic analysis with NVivo 12 software. The findings identified four main themes and seven sub-themes covering students' use of AI, feelings toward AI, perceived advantages, and challenges. Students perceived AI as a helpful tool for generating ideas, improving grammar, and increasing writing efficiency. However, concerns regarding academic honesty, limited access, contextual language, inaccurate information, and generic arguments were also reported. The study concluded that AI could effectively support academic writing when it was used critically, ethically, and responsibly.
