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  Generating Rubrics via ChatGPT

  Jesse Briscoe

  Rubrics serve as essential tools in education, providing clear expectations, objective grading criteria, and structured feedback for both instructors and students. Well-designed rubrics enhance transparency, promote consistency in assessment, and support student learning by clarifying performance standards. However, developing effective rubrics can be time-consuming and challenging, particularly for instructors managing multiple courses or diverse assignments.

  The integration of artificial intelligence (AI), particularly ChatGPT, offers an efficient approach to rubric generation. By leveraging natural language processing, ChatGPT can create detailed, customizable rubrics that align with specific learning objectives and assessment needs. This process enables instructors to quickly generate rubrics tailored to various assignment types, disciplines, and proficiency levels. Additionally, AI-assisted rubric creation supports instructors by providing templates that can be refined based on course-specific requirements, thereby reducing workload while maintaining high-quality assessment standards.

  For students, clearly defined rubrics contribute to a deeper understanding of expectations, fostering self-regulation and improved academic performance. AI-generated rubrics can also facilitate timely feedback, aiding students in identifying areas for improvement. Moreover, the adaptability of AI tools allows for rapid revisions and modifications, ensuring rubrics remain relevant to evolving instructional goals.

  This presentation explores the benefits and challenges of generating rubrics via ChatGPT, highlighting its potential to enhance assessment practices in educational settings.

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  AI-Based Health Monitoring System

  Athwika Gade

  Artificial Intelligence (AI) has emerged as a transformative force in healthcare. By integrating technologies such as machine learning, natural language processing, and computer vision, healthcare systems are improving diagnostic accuracy, accelerating administrative workflows, and personalizing treatments. This poster explores how AI innovations are reshaping the industry, while also addressing ethical concerns and implementation barriers.

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  The Use of Artificial Intelligence in Nursing Simulation

  Anna Beth Gilmore, Nancy Tyler, Taylor Mason, Bailey Kuhlman, Ashley Bawl, and Marina Rogers

  The integration of Artificial Intelligence (AI) into nursing simulation education is transforming the landscape of healthcare training, offering innovative solutions to enhance learning outcomes and improve clinical preparedness. This poster presentation will explore how AI technologies can be utilized to elevate nursing simulation education by providing realistic, adaptive, and personalized learning experiences. The use of AI in nursing simulation allows students to engage in dynamic, hands-on scenarios that replicate real-world clinical environments. These scenarios are created to adapt in real time to students' decisions, providing immediate feedback and fostering critical thinking and clinical decision-making skills.

  Additionally, AI enables the creation of complex, data-driven simulations that reflect a diverse range of patient conditions and emergencies, allowing for repetitive practice in a safe, controlled environment. This is especially beneficial in exposing nursing students to rare or high-risk situations that they may not encounter in traditional clinical settings.

  By harnessing AI’s potential, nursing education can be more efficient, accessible, and effective, ensuring that future nurses are better equipped to deliver high-quality care. This poster presentation will highlight current AI iniatives in nursing simulation, discuss challenges and opportunities, and propose strategies for further integration into nursing curricula to prepare students for interactions with real patients in the healthcare setting.

  AI assistance was utilized in the creation of this abstract.

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  Using the artificial intelligence technique of logic tensor networks to predict aurora borealis visibility

  Jhonatan Granadeno, Jacob Luton, Erik Mayer, Estevan Hernandez, Tao Wu, Hongsheng He, and Elizabeth MacDonald

  Building upon previous research, an AI technique called logic tensor networks is used to predict where to view the aurora borealis. This technique uses a logic-based neural network to create these predictions. The model outputs probabilities of sightings. Classification, a machine learning technique used to sort data into categories, will be used to compare with the logic tensor networks. Work is ongoing to gather and format data collected by satellite and from the Aurorasaurus website to use for training our model. The Aurorasaurus website collects reports from people around the world and stores data such as the date, time, geographical coordinates, and the duration of the sighting. This site also uses a model that predicts viewing locations, called Ovation Prime. Since the Ovation Prime model gives the probability of sighting the aurora overhead, view lines are used to adjust the probabilities of Ovation Prime to compensate that the aurora may be sighted closer to the horizon. The Ovation Prime model does not accurately predict where the aurora is visible. Thus, logic tensor networks will be used to combine the Ovation Prime model with the reports of sighting to increase the accuracy of the Aurorasaurus predictions.

  This research is a continuation of that funded by the NASA Rapid Response Research Grant Appendix F: A Neural-Symbolic Aurora Model Driven by Aurorasaurus Data in Citizen Science and the Kansas National Space Grant College and Fellowship Program – Opportunities in NASA STEM FY 2020-2024. It is currently supported by the NSF ASTER-LSAMP grant at PSU.

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  Meta-Cognition, Open AI & Inoculation: Racing to Zero

  Alicia Mason

  A 2023 study explored ChatGPT-4's capacity to generate inoculation message treatments, revealing structural weaknesses like unclear language and inconsistency in developing threat components. Trained with diverse prompts, the AI struggled with explicit forewarnings and limited refutations, despite showcasing originality and figurative language use. This highlighted the need for improved generative AI inoculation message design. In 2025, follow-up studies examined ChatGPT-4o’s metacognition. Researchers used chain of thought prompt engineering techniques to elicit knowledge, regulation, and experiences, analyzing AI's self-assessment and adaptability. ChatGPT-4o reported various confidence levels including a high degree of confidence in refining messages for readability, acknowledging and correcting prior structural weaknesses. Novel message generation and language ratio adherence garnered moderate confidence. Creating new communication strategies yielded the lowest confidence and was deemed dependent on clear goals and constraints. This line of inquiry highlights parallels between inoculation theory and AI metacognition. Inoculation's threat mechanism maps to AI's error anticipation, while counterarguing mirrors AI's monitoring and evaluation. Guided practice in inoculation corresponds to chain-of-thought prompting, enhancing AI’s problem-solving. This suggests inoculation principles can inform AI metacognitive prompts, fostering self-reflection and adaptability, and highlighting cross-disciplinary potential to improve both human communication strategies and AI development. Applications and implications for industry and higher education are documented.

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  Neural network curriculum development and student research in Electronics Engineering Technology

  Erik Mayer

  The development of neural network curriculum in the Electronics Engineering Technology (EET) program began as project for the Kansas National Space Grant College and Fellowship Program – Opportunities in NASA STEM FY 2020-2024 grant. In this grant, NASA funds supported curriculum development and research in affiliated universities. In the project, faculty and students met to explore online resources and participate in online courses on neural networks that focused on the Keras application programming interface (API).

  A portion of the developed curriculum was first used in Spring 2022 in EET 549 Advanced Microcontrollers and EET 745 Advanced Microprocessor Systems and Applications. The topics of regression, classification, convolutional neural networks, and recurrent neural networks were covered. In EET 549, students were assigned a final project in which a line follower robot was programmed using a neural network.

  In 2023 and 2024, EET faculty and students became involved in a NASA Rapid Response Research (R3) grant. In this grant, it was proposed that neural-symbolic neural networks could be used to augment physics-based predictions of the aurora borealis with sightings made by citizen scientists. Neural-symbolic neural networks are those that implement logic predicates.

  As part of the R3 grant, curriculum was developed for predicate logic and neural-symbolic neural networks. This was used in EET 549 and 745 in Spring 2024. In addition, curriculum on tiny machine learning (TinyML), the implementation of neural networks on memory and speed constrained devices such as microcontrollers, was also introduced.

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  AI in Higher Ed, Where Are We Now?: Insights from the 2025 EDUCAUSE AI Landscape Study

  Angela Neria and Jeff Burns

  Curious about how higher education is really using AI? Wondering what’s next for AI policies, workforce impacts, and leadership strategies? The 2025 EDUCAUSE AI Landscape Study has the answers! Based on fresh data from institutions across higher ed, this study highlights key trends, challenges, and opportunities in AI adoption. Stop by our poster session to get a quick snapshot of where AI stands today—and where it’s headed. Let’s talk about what these findings mean for PSU and the future of AI in higher education!

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  AI in Theatre: AI as a Component in the Stage Production of "Mrs. Dalloway"

  Lisa Quinteros, Linden Little, and Megan Westhoff

  For the 2024 production of Pitt State Theatre's "Mrs. Dalloway", our production team made the decision to include AI as part of the design of the production, and incorporated it's use throughout the process with image and video projection. The AI enhanced and expanded our reach to convey the story and impact of our production in new avenues for our theatre company and design team. Since this initial foray, we have continued to use AI as an effective tool for various tasks in recent productions. We hope to continue to use AI in the future for new creative outlets and solutions. Included in this presentation are examples of how AI processes were used for this production.