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

Interactive Visual Analysis of Translations

This thesis is the result of a collaboration with the College of Arts and Humanities at Swansea University. The goal of this collaboration is to design novel visualization techniques to enable digital humanities scholars to explore and analyze parallel translations. To this end, chapter 2 introduces the first survey of surveys on text visualization which reviews all of the surveys and state-of-the-art reports on text visualization techniques, classifies them, provides recommendations, and discusses reported challenges.Following this, we present three visual interactive designs that support the typical digital humanities scholars workflow. In Chapter 4, we present VNLP, a visual, interactive design that enables users to explicitly observe the NLP pipeline processes and update the parameters at each processing stage. Chapter 5 presents AlignVis, a visual tool that provides a semi-automatic alignment framework to build a correspondence between multiple translations. It presents the results of using text similarity measurements and enables the user to create, verify, and edit alignments using a novel visual interface. Chapter 6 introduce TransVis, a novel visual design that supports comparison of multiple parallel translations. It incorporates customized mechanisms for rapid and interactive filtering and selection of a large number of German translations of Shakespeare’s Othello. All of the visual designs are evaluated using examples, detailed observations, case studies, and/or domain expert feedback from a specialist in modern and contemporary German literature and culture.Chapter 7 reports our collaborative experience and proposes a methodological workflow to guide such interdisciplinary research projects. This chapter also includes a summary of outcomes and lessons learned from our collaboration with the domain expert. Finally, Chapter 8 presents a summary of the thesis and future work directions

VNLP: Visible natural language processing

In general, Natural Language Processing (NLP) algorithms exhibit black- box behavior.Users input text and output is provided with no explanation of how the results are obtained.In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines.Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps.We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) design is applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert

Commonplace Cultures: Mining Shared Passages in the 18th Century using Sequence Alignment and Visual Analytics

Recent scholarship has demonstrated that the various practices associated with Early Modern commonplacing--the extraction and organization of quotations and other passages for later recall and reuse--were highly effective strategies for dealing with the perceived "information overload" of the period. But, the 18th century was also a crucial moment in the modern construction of a new sense of self-identity. Our goal is to examine this paradigm shift in 18th-century culture from the perspective of commonplaces and their textual and historical deployment in the contexts of collecting, reading, writing, classifying, and learning. These practices allowed individuals to master a collective literary culture through the art of commonplacing, a nexus of intertextual activities that we aim to explore through the concerted application of sequence alignment algorithms for shared passage detection and large-scale visual analytics on the largest collection of 18th-century works ever assembled

Explorative Visual Analysis of Rap Music

Detecting references and similarities in music lyrics can be a difficult task. Crowdsourced knowledge platforms such as Genius. can help in this process through user-annotated information about the artist and the song but fail to include visualizations to help users find similarities and structures on a higher and more abstract level. We propose a prototype to compute similarities between rap artists based on word embedding of their lyrics crawled from Genius. Furthermore, the artists and their lyrics can be analyzed using an explorative visualization system applying multiple visualization methods to support domain-specific tasks