This exploratory study focuses on the use of ChatGPT, a generative artificial intelligence (GAI) tool, by undergraduate engineering students in lab report writing in the major. Literature addressing the impact of ChatGPT and AI on student writing suggests that such technologies can both support and limit students' composing and learning processes. Acknowledging the history of writing with technologies and writing as technology, the development of GAI warrants attention to pedagogical and ethical implications in writing-intensive engineering classes. This pilot study investigates how the use of ChatGPT impacts students’ lab writing outcomes in terms of rhetorical knowledge, critical thinking and composing, knowledge of conventions, and writing processes. A group of undergraduate volunteers (n= 7) used ChatGPT to revise their original engineering lab reports written without using ChatGPT. A comparative study was conducted between original lab report samples and revisions by directly assessing students’ lab reports in gateway engineering lab courses. A focus group was conducted to learn their experiences and perspectives on ChatGPT in the context of engineering lab report writing. Implementing ChatGPT in the revision writing process could result in improving engineering students’ lab report quality due to students’ enhanced lab report genre understanding. At the same time, the use of ChatGPT also leads students to provide false claims, incorrect lab procedures, or extremely broad statements, which are not valued in the engineering lab report genre.

Non-parametric density estimation methods are more flexible than parametric methods, due to the fact that they do not assume any specific shape or structure for the data. Most non-parametric methods, like Kernel estimation, require tuning of parameters to achieve good data smoothing, a non-trivial task, even in low dimensions. In higher dimensions, sparsity of data in local neighborhoods becomes a challenge even for non-parametric methods. In this paper we use the copula transform and two efficient non-parametric methods to develop a new method for improved non-parametric density estimation in multivariate domain. After separation of marginal and joint densities using copula transform, a diffusion-based kernel estimator is employed to estimate the marginals. Next, Bayesian sequential partitioning (BSP) is used in the joint density estimation.

Multivariate density estimation methods, typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian sequential partitioning (BSP) algorithm provides an efficient way of partitioning the sample space, based on Bayesian inference. In this paper, we present a detailed analysis of BSP and provide a computationally efficient copula-transformed data structure and algorithm for use in density estimation for data analytics in high dimensions. Using the copula-transformed data structure, we implement the density estimation for marginals in both BSP and kernel density estimation (KDE) methods. The data structures and algorithm are suitably designed for most efficient rendering into parallel processing paradigms of open multi-processing (OPENMP®) and message passing interface (MPI).