MARS
MARS is a repository service of Mason Publishing and the Data and Digital Scholarship Services (DDSS) at the George Mason University Libraries. MARS provides enduring, stable, well-indexed access to a wide range of scholarship from the Mason community, such as Electronic Theses and Dissertations (ETDs), articles, presentations, reports, and creative work. Learn more about publishing, sharing, and preserving research data with the George Mason University Institutional Dataverse, and our other repository services.
To start publishing your content in MARS, please contact us by using our online form. Questions? Please email publish@gmu.edu.
Communities in MARS
Select a community to browse its collections.
Recent Submissions
The Invisible Art of Teaching: Finding Your Hidden Superpowers that Transform Learning
(2025-11-12) Dailey, Stephanie F.; La Croix, Leslie
In the evolving landscape of higher education, the most transformative teaching practices often operate beneath the surface. This interactive session, The Invisible Art of Teaching: Finding Your Hidden Superpowers that Transform Learning, invites educators to uncover and intentionally harness the personal and relational strengths that drive student engagement and success. Through guided reflection, collaborative dialogue, and a structured “Superpower Identification” activity, participants will identify their unique instructional assets and explore strategies to integrate them into inclusive, innovative, and evidence-based teaching practices. By recognizing and connecting individual superpowers, educators will leave equipped with practical tools to amplify collective impact, strengthen community, and cultivate transformative learning environments that center authenticity, connection, and active learning.
Experimenting with AI in Spanish
(2025-11-12) Sweet, Colleen
Using generative AI in an online writing intensive course can be challenging for both students and instructors. Additional challenges emerge when students are writing in a language that they are still in the process of learning. This teaching talk describes a carefully structured project designed to support students’ experimentation with generative AI in Spanish in an asynchronous writing intensive course. One of the benefits of this strategy is that it empowers students to evaluate new technologies and their effectiveness as tools for supporting their learning. Aligned with the objective of “writing to learn” in WI courses, the project uses the TILT approach and provides opportunities for students to collaborate with others and reflect on transferable skills they have learned in their writing course. The teaching strategies discussed can be applied to working with multilingual learners and multiple course modalities.
Enhancing Programming Pedagogy Through Interactive Jupyter Notebooks
(2025-11-12) Lybarger, Kevin
Learning by doing is fundamental to effective programming education, as engagement with code fosters deeper conceptual understanding and hones critical problem-solving skills. A Jupyter notebook is an interactive document format that seamlessly integrates narrative descriptions with executable code, capturing outputs in real time. This session presents a framework for incorporating these tools into introductory programming courses. The framework emphasizes active learning through live coding and in-class problem-solving by interleaving lecture content with immediate code execution, enabling students to apply theoretical concepts in practice. In parallel, the session outlines the design of interactive activities where learners work through coding challenges both individually and collaboratively. It demonstrates how contemporary generative AI tools, such as ChatGPT, can streamline the formatting and presentation of coding problems within notebooks, ensuring consistency across quizzes, lectures, labs, and homework. Attendees will gain insights into enhancing student engagement, streamlining instructional delivery, and improving assessment coherence in programming courses.
Negative Concord in Farasani Arabic: Evidence for a Uniform Syntactic Agreement Model (USAM)
(2025-11-03) Modaffar, Hussain
This paper develops the Uniform Syntactic Agreement Model (USAM), a framework that advances a decisive shift from the existing theoretical complexity in the analysis of neg-words in Negative Concord (NC) structures toward a more streamlined and principled account of uniformity. It provides a simplified and unified approach to the licensing of neg-words across NC languages, integrating the strict and non-strict NC variation into a single Minimalist framework. Drawing on new empirical data from Farasani Arabic, an under-documented dialect of Saudi Arabia, the study argues that both NC types share an identical syntactic operation: EPP-driven movement of preverbal neg-words to [Spec-NegP] for licensing, with surface variation reducible to a single morphological parameter, ± Overt Neg°.
Acceptability-judgment data demonstrate that Farasani Arabic patterns like non-strict NC languages on the surface (preverbal neg-words independently express negation) but syntactically behaves like strict NC languages, since such neg-words must still undergo movement to [Spec-NegP] for licensing by a null Neg°. Postverbal neg-words, by contrast, require c-command licensing by an overt sentential negator. USAM thus reframes the traditional strict/non-strict dichotomy as a morphological—not syntactic—distinction, aligning with Chomsky’s (2001) view that cross-linguistic variation resides in PF-realization rather than deep structure.
By eliminating excessive theoretical complexity arising from unnecessary mechanisms, lexical ambiguity, and redundant feature assignments associated with neg-words and neg-markers posited in earlier NC frameworks, USAM offers a simpler and more principled account of NC across Arabic dialects and beyond. The model not only advances the documentation of Farasani Arabic but also offers a unified analysis that derives typological variation in NC from a single underlying syntax.
AI-Guided Optimization of an Antimicrobial Peptide targeting Bacillus cereus: Enhancing Activity and Reducing Toxicity Through Machine Learning Tools
(George Mason University, 2025-11-04) Thakker, Shaurya; Lockhart, Chris
The growing threat of antibiotic resistance has underscored the urgent need for alternative therapeutic strategies. Antimicrobial peptides (AMPs), naturally occurring molecules with broad-spectrum activity, present a promising solution but face limitations such as toxicity, instability, and delivery challenges. This study aimed to enhance the safety and efficacy of a low-performing AMP active against Bacillus cereus by utilizing artificial intelligence (AI) and machine learning (ML) tools. Four computational platforms - CAMPr4, ToxinPred3, AlphaFold, and ChatGPT - were used to predict antimicrobial potential, evaluate toxicity, model structure, and streamline peptide design. Starting with an AMP scoring 0.2895 in CAMPr4, sequence modifications were made to increase net charge and decrease toxicity. The final optimized peptide, named Cereus-Black93, achieved a predicted antimicrobial score of 0.93 and a significantly reduced toxicity score of 0.27. Structural modeling confirmed a stable alpha-helical conformation. These results demonstrate the potential of AI-driven approaches to accelerate the design of novel AMPs and pave the way for future in vitro validation and therapeutic development.