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1. The use of artificial intelligence in STEM communication-intensive courses and its impact on science identity

   https://doi.org/10.3389/feduc.2025.1644873  

   Garcia_Ramos, Jennifer; Wilson-Kennedy, Zakiya (August 2025, Frontiers in Education)

   Pandey, Sumali (Ed.)

   This original research article focuses on the investigation of the use of generative artificial intelligence (GAI) use among students in communication-intensive STEM courses and how this engagement shapes their scientific communication practices, competencies, confidence, and science identity. Using a mixed-methods approach, patterns were identified in how students perceived their current science identity and use of incorporating artificial intelligence (AI) into writing, oral, and technical tasks. Thematic analysis reveals that students use AI for a range of STEM communication endeavors such as structuring lab reports, brainstorming presentation ideas, and verifying code. While many minoritized students explicitly describe AI as a confidence-boosting, timesaving, and competence-enhancing tool, others—particularly those from privileged backgrounds—downplay its influence, despite evidence of its significant role in their science identity. These results suggest the reframing of science identity as being shaped by technological usage and social contingency. This research illuminates both the potential and pitfalls of AI-use in shaping the next generation of scientists. 

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2. Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning (Work in Progress)

   Xu, G.; Zabner, D.; Cross, J.; Nadler, D.; Coxon, S.; Engelkenjohn, K. (June 2023, ASEE annual conference exposition proceedings)

   Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical. 

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3. Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems

   https://doi.org/10.1609/aaaiss.v3i1.31217  

   Hridi, Anurata Prabha; Sahay, Rajeev; Hosseinalipour, Seyyedali; Akram, Bita (May 2024, Proceedings of the AAAI Symposium Series)

   The majority of current research on the application of artificial intelligence (AI) and machine learning (ML) in science, technology, engineering, and mathematics (STEM) education relies on centralized model training architectures. Typically, this involves pooling data at a centralized location alongside an ML model training module, such as a cloud server. However, this approach necessitates transferring student data across the network, leading to privacy concerns. In this paper, we explore the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem. We highlight the potential benefits FL offers to students, classrooms, and institutions. Also, we identify a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector. Finally, we discuss a series of open research directions, focusing on nuanced aspects of FL implementation in educational contexts. These directions aim to explore and address the complexities of applying FL in varied educational settings, ensuring its deployment is technologically sound, beneficial, and equitable for all stakeholders involved. 

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4. Humanizing STEM education: an ecological systems framework for educating the whole student

   https://doi.org/10.3389/feduc.2023.1175871  

   Yao, Christina; Follmer Greenhoot, Andrea; Mack, Kelly; Myrick, Chandra; Poolaw, Johnny; Powell, Linda; Yarger, Lynette (October 2023, Frontiers in Education)

   Sheila S. Jaswal, Amherst College (Ed.)

   STEM higher education in the U.S. has long been an uninviting space for minoritized individuals, particularly women, persons of color, and international students and scholars. In recent years, the contemporary realities of a global pandemic, sociopolitical divides, and heightened racial tensions, along with elevated levels of mental illness and emotional distress among college students, have intensified the need for an undergraduate STEM education cultureandclimate that recognizes and values the humanity of our students. The purpose of this article is to advance a more humanized undergraduate STEM education and to provide a framework to guide efforts toward achieving that vision. We argue that humanizing approaches recognize and value the complexity of individuals and the cultural capital that they bring to their education, and that this is particularly important for empowering minoritized students who are subordinated in status in STEM higher education. A STEM education that centers students’ humanity gives rise to equity and promotes human well-being and flourishing alongside knowledge acquisition and skill development. We then offer a guiding framework for conceptualizing the broader ecosystem in which undergraduate STEM students are embedded, and use it to outline the individual and collective roles that different stakeholders in the ecosystem can play in humanizing STEM education. 

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5. Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

   https://doi.org/10.1109/TE.2023.3337337  

   Wei-Kocsis, Jin; Sabounchi, Moein; Mendis, Gihan J.; Fernando, Praveen; Yang, Baijian; Zhang, Tonglin (December 2023, IEEE Transactions on Education)

   Contribution: A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to motivate the students for exploring AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects and 2) a proactive education paradigm designed with the use of collaborative learning activities based on game-based learning and social constructivism. Background: Increasing evidence shows that AI techniques can be manipulated, evaded, and misled, which can result in new and profound security implications. There is an education and training gap to foster a qualified cyber-workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. Efforts have been made to incorporate a comprehensive curriculum to meet the demand. There still remain essential challenges for effectively educating students on the interaction of AI and cybersecurity. Intended Outcomes: A novel proactive and collaborative learning paradigm is proposed to educate and train a qualified cyber-workforce in this new era where security breaches, privacy violations, and AI have become commonplace. Application Design: The development of this learning paradigm is grounded in the pedagogical approaches of technology-mediated learning and social constructivism. Findings: Although the research work is still ongoing, the prototype learning paradigm has shown encouraging results in promoting the learners’ engagement in applied AI learning. 

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