Ali Ünlü

Yinan Sun, Ali Unlu, Aditya Johri

This research full paper examines how news media have been instrumental in creating specific narratives about generative AI applications, especially ChatGPT, in higher education, and how these narratives have changed over time. The introduction of emerging technologies in higher education is driven not only by their technological affordances but also by the narratives built around their perceived value, risks, and possibilities. Therefore, understanding how news media narratives contribute to sociotechnical imaginaries - the imagined futures of technology use that institutions and educators inherit - is important for evaluating ChatGPT's role in teaching and learning, including engineering education. Through temporal and sentiment analyses of 198 U.S. news articles from November 2022 to October 2024, we traced the evolving narratives surrounding generative AI and the use of ChatGPT in higher education. We found that the media discourse largely centered on institutional responses, with policy changes and teaching practices showing the most consistent presence and positive sentiment over time. Conversely, coverage of topics such as human-centered learning, the job market, and skill development appeared more sporadic, with initially uncertain portrayals gradually shifting toward cautious optimism. Media sentiment toward ChatGPT's role in college admissions remained predominantly negative. Our findings suggest that media narratives prioritize institutional responses to generative AI over the long-term, broader ethical, social, and labor-related implications. This imbalance is especially relevant to engineering and computing education, where students must be prepared not only to use AI tools but also to critically evaluate the broader sociotechnical consequences of AI as future designers of AI-enabled technologies.

Ali Unlu, Laurence Aitchison

We develop variational Laplace for Bayesian neural networks (BNNs) which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational Laplace objective is simple to evaluate, as it is (in essence) the log-likelihood, plus weight-decay, plus a squared-gradient regularizer. Variational Laplace gave better test performance and expected calibration errors than maximum a-posteriori inference and standard sampling-based variational inference, despite using the same variational approximate posterior. Finally, we emphasise care needed in benchmarking standard VI as there is a risk of stopping before the variance parameters have converged. We show that early-stopping can be avoided by increasing the learning rate for the variance parameters.

Ali Unlu, Laurence Aitchison

We develop variational Laplace for Bayesian neural networks (BNNs) which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational Laplace objective is simple to evaluate, as it is (in essence) the log-likelihood, plus weight-decay, plus a squared-gradient regularizer. Variational Laplace gave better test performance and expected calibration errors than maximum a-posteriori inference and standard sampling-based variational inference, despite using the same variational approximate posterior. Finally, we emphasise care needed in benchmarking standard VI as there is a risk of stopping before the variance parameters have converged. We show that early-stopping can be avoided by increasing the learning rate for the variance parameters.