Saira Anwar

In 2025, the research community in the United States of America (U.S.) faced unprecedented stop orders and terminations in federally funded grants. The purpose of this study is to explore the voices of reaction by the research community through their publicly shared opinions. Also, this study explores the effect of federal silent cuts in situating individual stories within systemic structures. Focusing on qualitative research methodology, this study analyzed 52 public discourses on professional social media platforms using reflexive thematic analysis. This study focuses on capturing the essence of the post-authors’ voices and meaning-making processes. Based on identified relationships between discourses, four themes were identified: (1) Breach of trust, (2) Exposed fault lines, (3) Emotional and professional instability, and (4) Shared solidarity, and collective response. These themes reflected concerns, emotional responses, and strategic orientations expressed by the members of the academic and research community who chose to publicly share their perspectives on LinkedIn about the National Science Foundation (NSF) grant terminations and suspensions. Also, the themes showed the terminations’ personal, professional, and societal effects. Moreover, these results could provoke a renewed conversation about the role of federal support in shaping the future of Science, Technology, Engineering, and Mathematics (STEM) education and innovation.

Prior research suggests that programming is a fundamental competency for all students. Due to its importance, programming education is integrated across many disciplines beyond computer science (e.g., humanities, social sciences, and engineering). Also, many existing courses report increasing enrollment trends. However, these changes have also introduced instructional challenges, particularly in supporting students with diverse backgrounds at scale. In this context, many studies have explored the use of AI code assistants as tools that may support instruction and learning. In these studies, while examining the use of AI code assistants, researchers have reported variation in educational contexts, implementation approaches, and outcomes. With this paper, we argue that synthesized information of such variations could help in understanding the effective use of such tools in programming education. To create a synthesized resource on AI code assistants, in this paper, we present a narrative review that synthesizes existing research. We reviewed 29 peer-reviewed studies identified through searches across three databases. The studies were analyzed to identify reported patterns of use, student and instructor perceptions, limitations in existing research, and suggested directions for future research. Across the reviewed studies, AI code assistants were commonly discussed for tasks such as code generation, debugging support, and real-time feedback, with ChatGPT reported most often (16 mentions), followed by GitHub Copilot (6 mentions). Disciplinary information was available in 24 studies, which helped identify the academic settings where AI code assistants were reported. Students generally describe these tools as useful, while also expressing concerns related to over-reliance and accuracy. Student perceptions were reported in 10 studies, while instructor perceptions were reported in 4 studies. Common reported limitations include small sample sizes, short intervention durations, reliance on self-reported data, and limited examination of long-term learning outcomes. Overall, this review consolidates current evidence on how AI code assistants are used and perceived in programming education and identifies areas where more research is needed.

The field of engineering education has evolved into a recognized discipline, yet many engineering faculty and graduate students lack formal training in educational research methodologies. This gap contributes to variability in the quality of research presented at major conferences, including Frontiers in Education (FIE). To address this, past FIE conference chairs and leaders in the field have designed a recurring 180-minute workshop focused on improving the quality of paper submissions and peer reviews. The workshop introduces participants to a structured framework for research design methods, spanning qualitative, quantitative, and mixed methods. Through active learning, hands-on analysis of past conference papers, and small-group methodological exploration, attendees will develop plans for conducting and evaluating high-quality research. Emphasis is placed on research rigor, trustworthiness, and alignment with top-tier journals such as the Journal of Engineering Education and IEEE Transactions on Education. Open to all FIE participants, this workshop provides essential tools for building methodological competence, fostering inclusivity, and preparing the current and next generation of engineering education scholars.