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This conceptual paper explores language and cultural resources as forms of multicompetence for engaging in engineering epistemologies (what we know) and practices (what we do). The need for a more diverse pool of engineers to tackle the complex challenges facing society is undeniable, but stereotypes about the discipline can create alienation among many students and undermine efforts to build a more inclusive profession. Drawing on scholarship from engineering education, science education, and learning sciences, this paper argues that the resources of Multicompetent Learners (ML), who have acquired valuable experiences and knowledge through social interaction within their communities, are valuable for engineering learning environments. By leveraging the language and cultural resources that students bring with them, engineering education can better prepare learners to develop solutions and knowledge that serve a diverse population. This work underscores the critical role of language and cultural resources in helping students be heard, seen, and understood in engineering and illustrate how these resources can help bridge the gap between students' lives and engineering. The paper further explores the multidimensional nature of language and cultural resources and how students draw on different sets of talk depending on the context, whether near or distal from the activity at hand. It contends that without a deeper understanding of the role of non-dominant ways of speaking in the act of becoming and belonging, efforts to diversify engineering will remain elusive. Ultimately, this paper summarizes these ideas through a conceptual model for engineering learning environments that value and leverage the resources that students bring from their communities. By creating more equitable and socially just solutions, engineering education can better serve the needs of diverse populations and ensure that the profession is truly reflective of the communities it serves. 

Implementing high-quality professional learning on diversity, equity, and inclusion (DEI) issues is a massive scaling challenge. Integrating dynamic support using natural language processing (NLP) into equity teaching simulations may allow for more responsive, personalized training in this field. In this study, we trained machine learning models on participants’ text responses in an equity teaching simulation (494 users; 988 responses) to detect certain text features related to equity. We then integrated these models into the simulation to provide dynamic supports to users during the simulation. In a pilot study (N = 13), we found users largely thought the feedback was accurate and incorporated the feedback in subsequent simulation responses. Future work will explore replicating these results with larger and more representative samples 

Role-plays of interpersonal interactions are essential to learning across professions, but effective simulations are difficult to create in typical learning management systems. To empower educators and researchers to advance simulation-based pedagogy, we have developed the Digital Clinical Simulation Suite (DCSS, pronounced "decks"), an open-source platform for rehearsing for improvisational interactions. Participants are immersed in vignettes of professional practice through video, images, and text, and they are called upon to improvisationally make difficult decisions through recorded audio and text. Tailored data displays support participant reflection, instructional facilitation, and educational research. DCSS is based on six design principles: 1) Community Adaptation, 2) Masked Technical Complexity, 3) Authenticity of Task, 4) Improvisational Voice, 5) Data Access through "5Rs", and 6) Extensible AI Coaching. These six principles mean that any educator should be able to create a scenario that learners should engage in authentic professional challenges using ordinary computing devices, and learners and educators should have access to data for reflection, facilitation, and development of AI tools for real-time feedback. In this paper, we describe the architecture of DCSS and illustrate its use and efficacy in cases from online courses, colleges of education, and K-12 schools.