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The Computing Alliance of Hispanic Serving Institutions (CAHSI), a national INCLUDES alliance, is committed to supporting students in attaining credentials in computing. Its latest effort focuses on advancing undergraduate computing majors into graduate school to address the low numbers of Hispanics, or Latinx, attaining graduate degrees in computing. CAHSI expands adoption of evidence-based, multi-institutional graduate support structures that lead to Latinx students’ success. This paper describes strategic efforts to address well-documented barriers among graduate students (across all areas of study), e.g., feeling of isolation, lack of support structures, deficit thinking, and negative departmental climate. 

This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty and the benefit of training as an opportunity for faculty professional growth and impact. This faculty development model allows for rapid dissemination of the ARG model through practice and application with weekly faculty cohort meetings, coaching, and reflection. 

Artificial Intelligence (AI) bots receive much attention and usage in industry manufacturing and even store cashier applications. Our research is to train AI bots to be software engineering assistants, specifically to detect biases and errors inside AI software applications. An example application is an AI machine learning system that sorts and classifies people according to various attributes, such as the algorithms involved in criminal sentencing, hiring, and admission practices. Biases, unfair decisions, and flaws in terms of the equity, diversity, and justice presence, in such systems could have severe consequences. As a Hispanic-Serving Institution, we are concerned about underrepresented groups and devoted an extended amount of our time to implementing “An Assure AI” (AAAI) Bot to detect biases and errors in AI applications. Our state-of-the-art AI Bot was developed based on our previous accumulated research in AI and Deep Learning (DL). The key differentiator is that we are taking a unique approach: instead of cleaning the input data, filtering it out and minimizing its biases, we trained our deep Neural Networks (NN) to detect and mitigate biases of existing AI models. The backend of our bot uses the Detection Transformer (DETR) framework, developed by Facebook, 

This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies. 

This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty and the benefit of training as an opportunity for faculty professional growth and impact. This faculty development model allows for rapid dissemination of the ARG model through practice and application with weekly faculty cohort meetings, coaching, and reflection.