University of Central Florida
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AI Playground: Microsoft Copilot (Office 365) & How He Made The Opening Video!
No full textStop by and join us for a demo of various AI tools! These demos do not include advanced techniques but serve as a tool comparison and provide insights into functionalities using quick examples. Bring your questions, and we\u27ll provide answers and demonstrations. These sessions overlap with the concurrent sessions and the breakfast/lunch hours, so if you want to pass some time between or during sessions, swing by and visit these stations
Faculty Forward: Powering Course Design Partnerships with GenAI
No full textThis session explores how Generative Artificial IIntelligence (GenAI) can enhance course development while upholding academic standards and learning outcomes. Based on extensive faculty collaborations across disciplines and varying levels of GenAI expertise, we present a backward design approach for thoughtfully integrating GenAI tools throughout the course development. Innovative Education Digital Learning\u27 s approach guides instructors through mapping the course, developing content, and enhancing activities and assessments. Through real-world examples, we\u27ll demonstrate how learning designers can help faculty adapt their pedagogical approaches to embrace GenAI\u27s capabilities while maintaining academic rigor and authenticity
AI Red Flags: Spotting AI-Generated Writing and Images
No full textHow do you spot AI in writing or images? From overly polished text to strange image distortions, AI-generated content can look deceptively human at first glance. However, recognizing red flags- like repetitive phrasing, peculiar patterns, and subtle inconsistencieshelps educators better understand how AI works and its impact on academic work. A Human or AI guessing game woven throughout the presentation will challenge your identification skills and reveal practical strategies for spotting these clues
Open Educational Resources and Generative AI: A Practical Approach to OER Development
No full textGenerative AI (GenAI) introduces opportunities to create or enhance Open Educational Resources (OER)- freely available, openly licensed course materials- that align with diverse learning objectives. This session uses the classic ADDIE (Analyze, Design, Develop, Implement, Evaluate) model as a practical framework to integrate GenAI into OER development processes. Participants will assess learner needs, design targeted prompts, and use collaborative oversight to refine AI-generated content. The session will also consider accessibility, copyright compliance, and ethical practices, alongside strategies for evaluating resources to inform future OER creation. Attendees will leave with strategies to integrate GenAI workflows that support student-centered OER initiatives
UDL in Action: AI & VR for Accessible Spanish
No full textThis session explores the integration of Artificial Intelligence (AI), Virtual Reality (VR), and Universal Design for Learning (UDL) principles in the development of a multimedia Open Educational Resource (OER) for an accelerated online Spanish course. Designed to foster accessibility and inclusivity, the project incorporates AI tools for automated feedback, personalized learning experiences, and content creation, alongside VR to simulate immersive cultural environments. Students will actively collaborate with university units, including instructional design and accessibility services, to gather diverse feedback and refine the OER. Their contributions highlight the role of student agency in shaping innovative educational resources, while ensuring that the OER aligns with diverse learning needs and high educational standards. This session will share actionable strategies for integrating AI and VR in course design, practical approaches to applying UDL principles, and lessons learned from student-faculty collaboration. Participants will leave with adaptable frameworks and strategies to incorporate AI into their own teaching practices
Eating Disorder Patient Experiences with Best Practices During Psychiatric Treatment: An Exploratory Sequential Mixed Methods Study
No full textEating disorders are among the deadliest mental health disorders and represent a significant financial burden to the U.S. healthcare system. However, little is known about eating disorder patients\u27 experiences during psychiatric treatment. Informed by the Picker Principles of Person-Centered Care and recommendations from the American Psychiatric Association, this study explored eating disorder patient experiences with best practices during psychiatric treatment. First, 10 patients were interviewed about their treatment experiences with a psychiatrist. Themes were analyzed using iterative categorization in Dedoose and informed a quantitative survey to examine patient experiences with their psychiatrist performing best practices during eating disorder treatment. The survey was completed by 226 eating disorder patients on the Prolific platform. Results were analyzed in SAS using descriptive statistics and logistic regression. Qualitative themes included fear related to seeking care, experiences with assessment and diagnosis, involvement in decision-making, collaborative care, the importance of their psychiatrist’s qualities, and satisfaction with treatment outcomes. Non-White patients were more likely than White patients to report receiving a referral to a nutritionist or dietitian (p\u3c0.0001), their psychiatrist collaborating with other health professionals (p=0.0081), being prescribed a medication (p=0.0039), and experiencing psychotherapy (p=0.0087). Males were more likely than females to report being prescribed a medication (p=0.0471). Eating disorder type was associated with being prescribed a medication (p=0.0280). Patients treated in the non-outpatient setting (p=0.0027) and via a telemedicine platform (p=0.0063) were also more likely to be prescribed medication. Results from this study may indicate disparities in eating disorder treatment experiences, and findings could be used to inform interventions, like credentialing, to improve psychiatrists’ use of best practices and engagement in interdisciplinary, person-centered care for eating disorder patients
Survivors Speak: Social Media\u27s Influence on the Troubled Teen Industry
No full textThis study focused on a three-year span of Twitter/X public tweets and discussions shared on the topic, The Troubled Teen Industry (TTI). Using the social listening tool Sprinklr, 7,743 public tweets were gathered on the matter and dissected to understand the influence the online conversation had on real-world changes in the industry. Narrowing down the initial 7,743 tweets to 2,447 applicable messages, this research analyzed the conversation surrounding the Troubled Teen Industry from the point of view of industry survivors and the role Paris Hilton and other influencers had on bringing the conversation to the digital space. The tweets meeting TTI survivor criteria were analyzed using the computational research methods of topic modeling and sentiment analysis, and overall research practice of social listening. The themes and findings were then analyzed from the perspective of Coordinated Management of Meaning (CMM) (Pearce & Cronen, 1980). CMM allowed the researcher to understand where conversations may need to be encouraged and what future conversations need to address. The results revealed the rise of conversation surrounding the Troubled Teen Industry, the impact Paris Hilton and other notable survivors had on changing the conversation, and the problems still being faced by those affected by the industry. Overall, this study recognizes the need for change in the Troubled Teen Industry and the role online conversations and communities can play from the perspective of social activism
Motion extrapolation and motion dynamics: Are the motion dynamics of friction utilized during motion extrapolation to inform time-to-contact estimations?
No full textCognitive processing delays should cause us to respond too late when making time-to-contact estimations. According to the theory of constructivism, the brain accounts for these delays by supplementing sensory information with higher level cognitive information to create an accurate motion percept. Across the attention, memory, and motion extrapolation literatures there is evidence supporting the use of higher-level information, like motion dynamics, to supplement our visual input. Specifically, previous research suggests we may account for friction’s constraints on motion when interacting with dynamic stimuli. The current research utilizes features that represent friction as both a stopping agent and a motion catalyst to determine if friction is processed during motion extrapolation to adjust our time-to-contact estimations. Across three experiments, I investigated the impacts of rotation congruence, floor contact, and their interaction on time-to-contact estimations across multiple occlusion durations, to determine if we are perceptually sensitive to the nuanced functions of friction, and if we process them during motion extrapolation. The results primarily provide evidence against the use of friction in making time-to-contact estimations. As individual features, both rotation and floor contact may be processed during motion extrapolation, but there was no evidence to suggest the two features were processed in a gestalt-type manner when combined. Both rotation and floor contact led to later time-to-contact estimations. This finding may be due to delays associated with processing additional information, but it is possible that the later estimates associated with floor contact could be the result of processing friction as a stopping agent
Artificial Intelligence (AI) Literacy: A Necessity in Modern Language Education
Get PDFThis article aims to discuss the importance of artificial intelligence (AI) literacy in the context of an AI-driven world and argues that it is a necessity in modern language education. The article answers a few questions, focusing on why AI literacy matters for language students and educators, what AI competencies should be promoted, and how AI literacy can be effectively integrated into the curriculum and instruction. The article also brings to the fore key considerations for implementing AI literacy in language education. These considerations include providing support to students and teachers to navigate the complexity of AI use, offering AI literacy training, providing resources and infrastructure that support AI use, adopting appropriate AI literacy frameworks to maintain critical thinking when using AI, contextualizing AI to specific contexts to ensure cultural sensitivity and inclusion, and supporting students’ psychological needs to avoid negative repercussions resulting from AI use. The article concludes with implications for relevant stakeholders such as students, teachers, and policymakers as well as suggestions for future research
