11 research outputs found
Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice
In recent years, advancements in artificial intelligence (AI) have led to the
development of large language models like GPT-4, demonstrating potential
applications in various fields, including education. This study investigates
the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in
achieving satisfactory performance on the Fundamentals of Engineering (FE)
Environmental Exam. This study further shows a significant improvement in the
model's accuracy when answering FE exam questions through noninvasive prompt
modifications, substantiating the utility of prompt modification as a viable
approach to enhance AI performance in educational contexts. Furthermore, the
findings reflect remarkable improvements in mathematical capabilities across
successive iterations of ChatGPT models, showcasing their potential in solving
complex engineering problems. Our paper also explores future research
directions, emphasizing the importance of addressing AI challenges in
education, enhancing accessibility and inclusion for diverse student
populations, and developing AI-resistant exam questions to maintain examination
integrity. By evaluating the performance of ChatGPT in the context of the FE
Environmental Exam, this study contributes valuable insights into the potential
applications and limitations of large language models in educational settings.
As AI continues to evolve, these findings offer a foundation for further
research into the responsible and effective integration of AI models across
various disciplines, ultimately optimizing the learning experience and
improving student outcomes.Comment: 22 pages, 7 figures, 1 tabl
Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
This paper presents a novel framework, Artificial Intelligence-Enabled
Intelligent Assistant (AIIA), for personalized and adaptive learning in higher
education. The AIIA system leverages advanced AI and Natural Language
Processing (NLP) techniques to create an interactive and engaging learning
platform. This platform is engineered to reduce cognitive load on learners by
providing easy access to information, facilitating knowledge assessment, and
delivering personalized learning support tailored to individual needs and
learning styles. The AIIA's capabilities include understanding and responding
to student inquiries, generating quizzes and flashcards, and offering
personalized learning pathways. The research findings have the potential to
significantly impact the design, implementation, and evaluation of AI-enabled
Virtual Teaching Assistants (VTAs) in higher education, informing the
development of innovative educational tools that can enhance student learning
outcomes, engagement, and satisfaction. The paper presents the methodology,
system architecture, intelligent services, and integration with Learning
Management Systems (LMSs) while discussing the challenges, limitations, and
future directions for the development of AI-enabled intelligent assistants in
education.Comment: 29 pages, 10 figures, 9659 word
A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits
A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits
Platform-independent and curriculum-oriented intelligent assistant for higher education
Abstract Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system’s accuracy and performance
DNA methylation in hearing-related genes in non-syndromic sensorineural hearing loss patients
Abstract Background Our understanding of epigenetic modifications in the inner ear is very limited. Although epigenetic regulation of genes related to individual organ- and system-limited pathologies are generally expected to be tissue-specific, DNA methylation patterns in peripheral blood (PB) are found to be associated with the presence of several diseases with no typical hematological involvement. Here, we aimed to investigate whether there is a correlation between hearing-related genes’ promoter region methylation in the PB samples with the presence of non-syndromic sensorineural hearing loss (NSSHL) with an aim of future utilization of DNA methylation as biomarkers in hearing loss. The study included 26 patients with NSSHL and a control group of 20 healthy individuals. CpG islands in the promoter regions of the GJB-2, GJB-6, and SLC24A genes were analyzed using bisulfite sequencing, and methylation percentages were analyzed. Results Methylation levels at the 1st region of GJB-6 and the 1st and the 4th regions of SLC26A4 were found to differ significantly (p = 0.039, p = 0.042, and p = 0.029, respectively) between the patients and the control group. There was no statistically significant difference in methylation percentages of GJB-2 promoters. We also found that parents’ consanguinity determines the methylation levels in patients’ families. Conclusions According to our knowledge, this is the first study that investigates epigenetic changes in the PB of patients with NSSHL. Despite the small sample size, our findings indicate that DNA methylation patterns in the PB could be of use for understanding epigenetic changes in the inner ear and the clinical management of NSSHL