2025 ASEE Annual Conference & Exposition

This brief research paper is a scoping review of the literature on two different forms of regulation of learning and team working within engineering education. Additionally, it introduces a new method of conducting scoping reviews with the assistance of Generative Artificial Intelligence (GenAI) tools such as ChatGPT ® and NotebookLM®. Background: Self-Regulated Learning (SRL) is a well-researched concept within engineering education. Many studies in multiple countries, as well as several systematic reviews and meta-analyses have been published in recent years synthesising research in this area. However, in teaching approaches that include student groups like project-based learning (PjBL) and problem-based learning (PBL) two related but relatively understudied concepts, namely, Co-regulation of Learning (CoRL) and Socially Shared Regulation of Learning (SSRL) needs weaving into the narrative around the challenges associated with team working skills development. Together, SRL, CoRL, and SSRL are fundamental concepts in understanding how students navigate complex, real-world problem-solving scenarios involving individual and team efforts that are a characteristic of engineering education in many settings. The potential benefits and challenges of teaching interventions that help promote CoRL and SSRL, with or without technology, need further investigation by engineering education researchers. Increased understanding of team working through CoRL and SSRL concepts can benefit hard to achieve aims related to teamwork and other transversal skills development within engineering education. Objective: This scoping review maps the existing research on team working and regulation of learning within engineering education, to chart the nature and extent research in these areas and to check for the feasibility of a full systematic literature review that may follow. Method: The review follows the PRISMA ScR checklist for reporting the details of this work. We used the PICO framework to guide our research question, the search clause, and the inclusion and exclusion criteria are described in detail. The search clause focussed on engineering student populations at all levels where teamwork and co-, and socially shared regulation skills are outcomes. The work we present summarises our findings from a single database, namely ERIC, which initially resulted in 329 records. We present results and reflections from our novel use of GenAI to support the process of shortlisting and extracting data from 48 papers. Results and conclusion: The nature (type of study, data collected) and extent (date, country, participants numbers, no of studies) of the work done in the last three years in this area has been summarised here. Quantitative and mixed methods were the most commonly used methodologies in the studies found. The United states and The Netherlands make up for the most studies found. Only a handful of studies focussed on socially shared regulation of learning. A full systematic review is feasible and may show more papers on social regulation of learning, which in this review is highlighted as a gap. Using ChatGPT and NotebookLM® was at least as good as human outcomes in most of the stages. Using GenAI to code for detecting duplicate articles was achieved successfully. There is a need to improve data extraction prompts when using NotebookLM®. Equally, evaluating other models and specific tools for this work is needed.