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Transforming Higher Education with AI: Practical Strategies and Insights
No full textThis session showcases a comprehensive exploration of AI\u27s potential, combining broad overviews of tools and strategies with realworld case studies from a small, urban, public university\u27s Year of AI initiative. Faculty from four disciplines - Mathematics, Public Administration, Psychology, and Criminal Justice - will share their varied approaches to incorporating AI tools like ChatGPT, Azure, and Copilot to enhance face-to-face, blended, and online teaching, along with sample assignments, teaching strategies, and student feedback. Participants will gain insights into AI-driven content creation, sample assignments, and student feedback, with a focus on fostering engagement and personalized learning. The session also addresses challenges, discipline-specific opportunities, and key ethical concerns such as bias and academic integrity. Attendees will leave with actionable strategies to responsibly leverage AI for innovation and effectiveness in teaching
Differentiating Instruction with Generative AI: Enhancing Introductory/GenEd Courses via Creative Applications of AI Tools
No full textThis session explores a variety of creative ways that educators are using free Generative AI tools to differentiate instruction. Learn how educators are creating and leveraging AI-generated podcasts, study guides, songs, and more to enhance teaching and learning for their students. Presented by a full-time high school science teacher who is also a lecturer of Educational Theory & Practice, this session focuses on use cases within introductory and general education courses, and will provide a window into the ways that generative AI is being used by teachers for differentiation at the secondary level
Engaging Students with AI: Using Personalized Talking Memes and creating AI Video for course modul
No full textThis session introduces faculty to innovative ways of engaging students by using AI-generated video content and personalized talking memes. Participants will learn how to transform their lectures into AI-driven video modules and integrate humorous, memorable talking memes that capture key concepts, fostering a dynamic and personalized learning experience
Optimizing Metadata Workflows with AI: Insights from UCF Libraries
No full textThe University of Central Florida Libraries are leveraging Artificial Intelligence (AI), specifically the OpenAI API, to transform their metadata workflows. By automating the assignment of Faceted Application of Subject Terminology (FAST) headings and keywords to their digital and traditional collections, they enhance discoverability and user experience. This approach involves exploring various term reconciliation methods, such as utilizing the OCLC service or building a custom FAST vector database. Through rigorous testing and evaluation, including comparisons with Alma\u27s AI Metadata Assistant, the Libraries are refining their AI-driven metadata practices, paving the way for an enriched library experience for their users
AI for Learning and Attention Issues
No full textAs only a third of college students tend to disclose their disability, it is important to equip faculty and academic support staff with UDL tools that can help any student with a learning or attention issue. At Beacon, where 100% of students have a learning difference, we have been able to match students\u27 challenges with AI tools that provide access, support, and executive function strategies. In this presentation, we will explore our learning model, share real scenarios where AI was implemented with students, and give recommendations for the most useful apps we\u27ve found in supporting learning differences
‘Who’s Your Daddy?’: Ddradseq Parentage In Broodstock-Offspring Pairs Of Diadema Antillarum In A Population Restoration Initiative
No full textIn the early 1980s Diadema antillarum, or the long-spined urchin, was hit by a catastrophic mass mortality event across the western Atlantic and Gulf of Mexico, inclusive of the Florida Keys. The cause of this die-off event was likely triggered by a pathogen or environmental stressor that decimated native populations by up to 98%. The species has not yet fully recovered and is struggling with recruitment success which is why it may benefit from an ex-situ recovery program. In this study, I used single nucleotide polymorphisms (SNPs) to identify genetic variation and analyze parentage in a captive population of D. antillarum. Harnessing these methods as an identification tool is incredibly useful in understanding populations and head-starting conservation and restoration projects for at-risk species through captive breeding programs. Sea urchins collected for this study were all housed at the Florida Aquarium and included urchins that fell into three categories: unique broodstock (n=15), larvae (n=80), and juveniles (n=50). I extracted DNA and conducted double digest restriction-site associated sequencing (ddRADseq) to generate SNPs for parentage analysis. Following alignment and filtering procedures, I generated a final set of 2426 high-quality SNPs from 14 unique broodstock samples and 24 juveniles. To infer pairwise relationships between all pairs of individuals sequenced, I used the R package CKMRsim using the likelihood ratio method. I was able to assign dual parentage to 9 juveniles, and for the remaining 15 juveniles I identified a single parent. Additionally, I found that 7 broodstock adults participated in offspring generation and 7 broodstock adults failed to generate offspring. Moreover, since this was a closed system, my data suggests that the one missing adult successfully participated in the breeding event. Variation in adult participation ranged from 1 offspring assigned (for two broodstock) to 9 assigned (for 1 broodstock). Interestingly, given the numbers, the missing broodstock sample would have parented 15 offspring. These results demonstrate that ddRADseq enables accurate identification of breeders and their offspring in D. antillarum, providing insight into reproductive dynamics and offering a powerful tool for optimizing genetic diversity and enhancing the effectiveness of long-term conservation and restoration strategies
Impacts of Susceptibility to Peer Influence and FoMO on Viewing Alcohol-Related Content on Social Media, Alcohol Expectancies, and Alcohol Use
No full textPURPOSE: Young adult (YA) alcohol use remains a significant public health problem, and may be influenced by alcohol expectancies developed through exposure to peers’ alcohol related content (ARC) on social media. This study examined the associations among ARC exposure, alcohol expectancies and alcohol use among YAs, and how susceptibility to peer influence and Fear of Missing Out (FoMO) may influence these relations. METHOD: YAs in the United States who endorsed consuming 1-2 standard drinks per month (N=371) completed surveys measuring ARC exposure, alcohol expectancies, susceptibility to peer influence, FoMO, and alcohol use at baseline and at 4- and 8-week follow ups. Structural equation modeling was used to analyze data across all three timepoints. RESULTS AND CONCLUSIONS: Results partially supported a mediating effect of alcohol expectancies on the relation between ARC exposure and alcohol use. Contrary to hypotheses, peer influence susceptibility and FoMO did not moderate associations between ARC exposure and alcohol expectancies and alcohol expectancies and alcohol use, respectively. However, model fit for analyses was poor, suggesting more complex underlying mechanisms and associations may exist. Results highlight alcohol expectancies as critical intervention targets and suggest programs highlighting social media’s influence on alcohol expectancies may be effective in mitigating YA alcohol use and subsequent consequences. This may be accomplished through integrating media literacy programming into existing expectancy-related interventions. Limitations included measurement challenges in capturing social media engagement and FoMO, as well as weak to moderate variable correlations indicating test variables may not have been accurately captured. Future research should incorporate more nuanced measures of social media engagement and FoMO, examine additional mediating mechanisms, and develop sophisticated methods for capturing the multifaceted nature of social media’s impact on YA alcohol use. Longitudinal studies with improved measurement techniques can then provide deeper insights into social media’s influence on YA drinking behaviors
Computation-efficient and Scalable Robotic Motion Planning Techniques To Address Dynamic Environmental Constraints
No full textIn modern robotics, effectively computing optimal robotic control policies under dynamically varying environmental constraints poses substantial challenges and remains a unique research endeavor. To compute efficient and safe robotic motion planning while achieving collective objectives, robotic agents have to satisfy two criteria. First, the working robotic entity must be capable of handling a non-stationary working environment with dynamic obstacles and system constraints. Second, to ensure the real-time response, the robotic agent has to compute an effective control policy that meets a real-time learning performance. Despite significant advancements in motion planning with the appearance of extensive computing resources and advanced deep learning algorithms, several formidable challenges remain in this robotic control design. In this dissertation, we develop techniques focusing on generalized methodology of robotic motion planning with efficiency and scalability that can quickly adapt to variable targets, dynamic obstacles, multiple robotic arm dynamics and successfully complete many intricate tasks related to non-stationary environments. In particular, we first aim at creating a unified framework for dexterous robotic manipulation that can handle dynamical environmental constraints such as dynamic obstacles, dynamic targets and multi-robot coordination without having a complete access to explicit environment models. Secondly, we propose computationally efficient and scalable real-time robotic control algorithms by integrating lower-dimensional manifold representation learning techniques with deep generative artificial intelligence based learning paradigm. Along with handling non-linear and unpredictable system constraints, we solve challenges with re-synthesizing robotic commands based on real-time object positions under brief reaction times, which ensures time optimization of generated robot motion. Finally, we propose a novel cross-embodiment robotic manipulation algorithm via bidirectional subspace alignment, cycle consistency and human behavior transformer inspired by robotic learning from expert demonstration to address the scarcity of paired cross-embodiment datasets and the impediment of designing intricate controllers. We develop an innovative methodology to address imbalanced datasets from heterogeneous domains and develop motion planning techniques through knowledge distillation. We observe that our findings exhibit significant implications for the future design of intelligent and autonomous robots, enhancing their capability to tackle more intricate robotic manipulation tasks with increased efficiency and safety
Assessing the accuracy of emotion classification of whisper v3: comparing performance on speech emotion datasets for both English and Arabic
No full textThis thesis investigates the application of Whisper V3 which is a state-of-the-art, multilingual automatic speech recognition model to Arabic Speech Emotion Recognition (SER). Building on the foundational work of Muhammad Firdho, who adapted Whisper architectures for emotion classification tasks, this study extends his approach to the context of Arabic, a language characterized by rich dialectal diversity, complex morphology, and unique prosodic features.Although Whisper V3 was primarily designed for Automatic Speech Recognition (ASR), its latent representations appear to capture paralinguistic cues that can be leveraged for emotion classification. Muhammad Firdho laid the basis for this study by showing the ability of Whisper-based models to interpret expressive aspects beyond transcription, opening the door for their evaluation to emotion identification problems.
In this study, I have used Khalil Emotion Detection Arabic Speech (KEDAS) dataset which is a balanced dataset comprising of a total of 5000 audio samples evenly distributed across five emotional categories (Angry, Sad, Fearful, Happy, and Neutral) used to evaluate the model’s performance in a zero-/few-shot setting. Due to resource limitations, I sampled and used the first 500 audio samples for my analysis. Despite achieving an overall accuracy of approximately 37.2%, the analysis reveals a pronounced bias toward over-predicting the Fearful category (E3), as evidenced by the confusion matrix and statistical chi-square testing.
These findings suggest that while large pretrained models can detect some emotional variance in Arabic speech, additional plain-language adaptation and fine-tuning may be necessary to improve performance. The study further discusses the implications of such biases and the challenges inherent in transferring models trained on multilingual corpora to low-resource, linguistically diverse settings. By highlighting both the potential and limitations of current approaches, this work contributes to a more inclusive and culturally sensitive development of emotion-aware AI systems
Millennials and Generation Z Customers\u27 Service Recovery Evaluation From a Self-Esteem Perspective
No full textWhile a significant amount of research has been conducted to assess the effectiveness of service recovery based on various factors, there is limited research on emerging customer segments, specifically Generation Z and Millennials. Justice theory has been predominant in service recovery literature, however, recent studies suggest that self-esteem can provide valuable insight into understanding customers’ evaluations of service recovery. This research focuses on how Generation Z and Millennial customers evaluate service recovery efforts in the context of restaurants. The main objective of this study is to examine how these customers perceive economic (compensation) and psychological (apology) service recovery strategies, considering generational differences. Additionally, this research examines the role of self-esteem in the service recovery evaluation process. The research design consists of a 2 (compensation: yes vs. no) x 2 (apology: yes vs. no) x 2 (generation: Millennials vs. Generation Z) between-subjects quasi-experimental design. Online surveys were distributed to those between the ages of 18-44 in the United States. Results from 358 participants indicated that compensation significantly affected self-esteem (H1 supported), as did an apology (H2 supported). The highest self-esteem was observed when both strategies were provided; however, compensation alone yielded nearly the same effect (H3 partially supported). No interaction effects between each recovery method and generation group were found (H4a-H4b not supported). Finally, increased self-esteem positively influenced customer satisfaction, loyalty, and electronic word-of-mouth intentions (H5a-H5c supported). The findings of this research suggest that offering a tangible resolution after a service failure may be more effective for young customers within the hospitality industry