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Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal Apr 2026

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

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Wegmans School Of Health And Nutrition’S Culinary Kitchen Cart Manual Development, Neena Bhala Apr 2026

Wegmans School Of Health And Nutrition’S Culinary Kitchen Cart Manual Development, Neena Bhala

Theses

To facilitate the initiation of culinary medicine at RIT, a manual to guide the use of a mobile kitchen cart was developed and evaluated. This manual was developed to support faculty, staff, and students’ use of a Mobile Kitchen Cart to be able to support culinary medicine and nutrition education activities. The Manual was directed to RIT faculty, staff, and students who have experience with the cart or intend to have future use with the cart. A qualitative evaluation study was conducted with ten participants including RIT faculty (n=2), students (n=4), and staff (n=4). Feedback on the manual was obtained …

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Hardware Integrity Checking On An Fpga Through Power Side-Channel Analysis, Ethan Vuong Apr 2026

Hardware Integrity Checking On An Fpga Through Power Side-Channel Analysis, Ethan Vuong

Theses

FPGAs have seen extensive usage in applications such as cloud-computing, hardware acceleration, mobile devices, and military alike. While the reconfigurability of these devices allow them to be as adaptable as they are fast, it raises concerns of adversaries modifying not mere software, but hardware itself. Moreover, designers face an IP trust issue where they cannot be sure that a third-party IP was not modified in transaction, programming, or even post-programming. Cloud computing centers are hesitant to rent fabric on multi-tenant FPGAs due to the plethora of vulnerabilities and uncertainties that come with allowing users to reconfigure hardware. This thesis aims …

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A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi Apr 2026

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …

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Efficiency Evaluation Of Water Pumping Stations Using Data Envelopment Analysis (Dea), Rowdha Abdullah Alblooshi Apr 2026

Efficiency Evaluation Of Water Pumping Stations Using Data Envelopment Analysis (Dea), Rowdha Abdullah Alblooshi

Theses

Water pumping stations are a critical component of water transmission systems, ensuring reliable delivery of potable water while maintaining operational requirements and international standards. With the increasing focus on sustainability and energy optimization, improving the efficiency of pumping stations has become a key priority. However, there is a lack of structured benchmarking approaches to evaluate the relative performance of pumping stations across multiple operational factors. This study addresses this gap by applying a Data Envelopment Analysis (DEA) framework to evaluate the efficiency of water pumping stations. A multi-model approach is adopted, including the CCR and BCC models, along with the …

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Evaluating Expected Real-World Tactile Experience From Virtual Fabric Perception, Julianna Gross Apr 2026

Evaluating Expected Real-World Tactile Experience From Virtual Fabric Perception, Julianna Gross

Theses

Most people have experienced the disappointment of ordering products online and receiving a product completely different to what was expected. For example, a sweater may appear that it is made out of extremely soft blue cotton, yet when seen in person is an itchy purple polyester blend. The current research seeks to ameliorate this confusion by evaluating various visual conditions that have the potential to lead to disparities between real-world feel and online images. Lighting and material characteristics are two major indicators of fabric, specifically real life look and feel. The angle of lighting and which light source is chosen …

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Optimizing Urban Commute Quality Through Traffic Congestion Analysis And Predictive Modeling, Obaid Almansoori Apr 2026

Optimizing Urban Commute Quality Through Traffic Congestion Analysis And Predictive Modeling, Obaid Almansoori

Theses

Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine  learning can be leveraged to classify and forecast traffic congestion severity in real time,  enabling data-driven decision-making for transportation planning, signal optimization, and  congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via  computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The  dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at  15-minute intervals, alongside temporal variables such as time of day, …

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Under The Surface: A Scalable Experiential Framework For Accessible Design Archives, Lo Fasano Apr 2026

Under The Surface: A Scalable Experiential Framework For Accessible Design Archives, Lo Fasano

Theses

Preserving design history is usually done by hiding it away. While institutions like the Vignelli Center for Design Studies house over 750,000 artifacts, the vast majority remain in restricted storage, with minimal space dedicated to displaying process materials alongside canonical final works. Digital archives document portions of these collections, but typically present them as static image galleries, leaving the evolutionary logic of a design, and the human labor behind it, invisible to the public. Under the Surface is a scalable, interactive framework designed to bridge archival preservation and public discovery. By transforming preserved artifacts into real-time digital experiences, the project …

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Adapting Specpt For Hst Grism Spectroscopy Via Transfer Learning, Clive Kalathoor Binu Apr 2026

Adapting Specpt For Hst Grism Spectroscopy Via Transfer Learning, Clive Kalathoor Binu

Theses

This thesis demonstrates the successful application of transfer learning to bridge ground-based and space-based spectroscopic analysis through adapting SpecPT (Spectroscopy Pre-trained Transformer) for Hubble Space Telescope WFC3 grism data for redshift prediction. Originally trained on high-resolution DESI spectra, SpecPT initially failed when applied directly to low-resolution, noisy HST WFC3 grism observations (Normalized Median Absolute Deviation (NMAD) = 0.2095, catastrophic outlier fraction ($\eta$) = 47.97\%). Through transfer learning on 8,530 high-quality 3D-HST spectra with emission-line SNR > 2.5 and z < 1.7, the model achieved substantial improvement (NMAD = 0.0724, $\eta$ = 26.69\%), representing a 65\% reduction in typical redshift error and 46\% decrease in catastrophic failures. The research addresses two primary objectives: establishing transfer learning effectiveness for cross-domain spectroscopic analysis and investigating whether supplementing grism spectra with broadband photometric data enhances performance. Counterintuitively, integrating comprehensive multi-wavelength photometric data from CANDELS significantly degraded performance (NMAD = 0.1641, $\eta$ = 36.51\%), challenging conventional astronomical assumptions about multi-modal data fusion and revealing critical failure modes in astronomical machine learning. This work establishes a unified framework for automated analysis of both ground-based and space-based spectroscopic surveys, with important implications for JWST, Euclid, and the Nancy Grace Roman Space Telescope. The demonstrated capability to adapt models across instrumental domains provides a scalable approach for processing large data volumes from next-generation missions, validating foundational model approaches that can be developed once and efficiently adapted across diverse observational contexts.

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Sampling And Counting Graph Structures With Triangle Motifs, Sherry Robinson Apr 2026

Sampling And Counting Graph Structures With Triangle Motifs, Sherry Robinson

Theses

Understanding the structure of real-world networks often relies on identifying significant (i.e., occurring significantly more frequently than random) subgraph patterns, or motifs, such as triangles. To assess their significance, null models generate random samples from a constrained distribution of graphs, preserving selected properties while randomizing others. These models may either generate random graphs or sample structures from a fixed input graph. This thesis focuses on the latter, specifically the problem of sampling and counting graph structures that incorporate triangle motifs. While efficient algorithms exist for sampling classical structures such as matchings, extending these methods to higher-order motifs remains an important …

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Phrases, Crystal Ching-Lam Tam Apr 2026

Phrases, Crystal Ching-Lam Tam

Theses

Motivational posters are widely used in educational environments to encourage perseverance, build confidence, and promote positive thinking. However, many of these visuals rely on cliché imagery, generic language, and overly decorative styles, making them feel inauthentic and easy to ignore. In visually saturated campus environments, they often fade into the background, acting as noise rather than as meaningful communication. How can motivation be communicated in a more engaging, intentional, and relevant way for college students? This thesis introduces Phrases, a visual communication system that reimagines motivational design through abstraction, clarity, and restraint. Rooted in Swiss design principles and Gestalt theory, …

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Predicting Teacher Burnout Across Cultures: A Machine Learning Approach Using Talis 2018 Data, Fatma Fraishan Abdulla Hassan Alkhzaimi Apr 2026

Predicting Teacher Burnout Across Cultures: A Machine Learning Approach Using Talis 2018 Data, Fatma Fraishan Abdulla Hassan Alkhzaimi

Theses

Teacher burnout is a persistent global challenge with significant consequences for educator wellbeing, instructional quality, and school climate. Despite extensive research, most studies rely on small local samples, predefined burnout scales, and limited analytical techniques, leaving gaps in understanding the latent structure of burnout and the factors that predict it across diverse educational systems. This study addresses these gaps by applying a hybrid machine learning framework to the OECD TALIS 2018 teacher dataset (N = 38,081) to discover latent burnout profiles and build predictive models capable of identifying teachers at risk. Unsupervised k-means clustering was used to uncover naturally occurring …

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Cognitive Digital Twin Operating System Forwayfinding In Vertical Smart Cities, Basil Adel Ismail Basbous Apr 2026

Cognitive Digital Twin Operating System Forwayfinding In Vertical Smart Cities, Basil Adel Ismail Basbous

Theses

Vertically complex urban environments impose elevated spatial cognitive load on pedestrians, a  demand that static wayfinding infrastructure is structurally incapable of addressing. Smart cities  currently lack a formal cognitive navigation operating layer for managing pedestrian movement in  multi-level urban systems. This research introduces and evaluates a Cognitive Digital Twin  Operating System (Cognitive OS) — a city-scale adaptive navigation infrastructure integrating  Digital Twin environmental modelling, AI-driven route optimisation, real-time crowd intelligence,  and spatially embedded adaptive guidance to predict, manage, and reduce spatial cognitive load in  vertically complex environments. The study deploys AI-mediated human behavioral persona simulation as an independent methodological  contribution. …

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Cancer Detection System Using Binary Neural Network On Dna Based Architecture, Antar Narayan Chowdhury Apr 2026

Cancer Detection System Using Binary Neural Network On Dna Based Architecture, Antar Narayan Chowdhury

Theses

Deoxyribonucleic acid (DNA) is among the most durable chemical storage media, capable of encoding the fundamental instructions for protein synthesis (the central dogma). Each human cell contains a unique DNA sequence characterized by identifiable markers that facilitate pattern recognition. These molecular features offer significant potential for personalized drug development and disease identification. Recent advancements in DNA based research have demonstrated that fundamental arithmetic operations can be executed directly through molecular interactions, bypassing the need for silicon-based computational assistance. These biochemical applications can be further scaled to support Binary Neural Network (BNN) models, which are particularly well-suited for mitigating stochastic noise …

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Preparing Future-Ready Graduates For Technological And Workforce Transformations, Ghia El Dirani Apr 2026

Preparing Future-Ready Graduates For Technological And Workforce Transformations, Ghia El Dirani

Theses

The United Arab Emirates (UAE) is undergoing rapid economic transformation driven by technolog- ical innovation and national strategies such as Vision 2031 and UAE Centennial 2071, positioning STEM (Science, Technology, Engineering, and Mathematics) education as critical to building a knowledge-based economy. However, a persistent gap exists between the competencies developed in STEM higher education programs and the skills demanded by emerging sectors such as artificial intelligence, renewable energy, and advanced manufacturing. While the UAE has introduced pro- gressive education policies and invested in digital infrastructure, most curriculum reforms remain reactive and disconnected from long-term workforce projections. This research applies strategic …

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Effect Of Geometry, Cell Size, And Carbon Fiber Reinforcement On The Charpy Impact Strength Of Additively Manufactured Tpms Lattice Structures, Ahmed Yousuf Mohammad Bin Yaroof (Alsuwaidi) Apr 2026

Effect Of Geometry, Cell Size, And Carbon Fiber Reinforcement On The Charpy Impact Strength Of Additively Manufactured Tpms Lattice Structures, Ahmed Yousuf Mohammad Bin Yaroof (Alsuwaidi)

Theses

Scientific research indicates a growing utilization of lightweight, high-performance materials across various disciplines, including engineering, driven by the demands of modern technological advancements. Recent developments within these fields include the creation of advanced lattice structures through additive manufacturing (AM) processes. One such lattice structure is the triply periodic minimal surface (TPMS) structure. TPMS structures possess unique mechanical properties and energy absorption characteristics that distinguish them from conventional AM lattice structures. However, published research to date has largely focused on the quasi-static behavior of TPMS structures, with limited attention given to the impact performance of composite- reinforced TPMS structures. The purpose …

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Full-Field Experimental Analysis Of 3d Printed Open-Hole Plates Reinforced With Isotropic And Concentric Carbon Fibers, Mozah Alyammahi Apr 2026

Full-Field Experimental Analysis Of 3d Printed Open-Hole Plates Reinforced With Isotropic And Concentric Carbon Fibers, Mozah Alyammahi

Theses

Additive Manufacturing technology (AM) has become an attractive process for innovative products in recent decades. The applications of AM materials widen to include composite materials. The fabrication of composite materials via AM makes the process more cost-effective than traditional fabrication methods. Nowadays, there are 3D printers that can produce composite materials with different strategies which results in different mechanical properties. This study examines the impact of carbon fiber reinforcement, both isotropic and concentric, on 3D printed open-hole plates utilizing full-field experimental analysis.  Problem Statement  The research on investigating different fiber reinforcement is increasing rapidly. However, most of the studies are …

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In-Context Retrieval For Molecules And Chemical Synthesis Pathways, Abhisek Dey Apr 2026

In-Context Retrieval For Molecules And Chemical Synthesis Pathways, Abhisek Dey

Theses

Contrastive learning methods require well-defined positive pairs, limiting their applicability to domains where complete, high-fidelity pairings are available. In practice, large-scale scientific corpora --including patents, publications, and web-scale data -- contain vast quantities of contextually relevant but incompletely paired samples that are discarded under standard training paradigms. In this work, we demonstrate that hard negative mining can be leveraged to construct pseudo-positive supervision signals from unpaired or partially paired data, enabling contrastive learning to exploit the full breadth of available corpora without sacrificing representational quality. Using a large-scale chemical drug patent corpus as a testbed, we train a cross-modal contrastive …

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Predicting Property Sale Prices In Dubai Using Machine Learning Regression Models, Mohammad Buabdulla Apr 2026

Predicting Property Sale Prices In Dubai Using Machine Learning Regression Models, Mohammad Buabdulla

Theses

It has been observed that the fastness of digital real estate’s application has resulted in availability of large-scale property data which forms an opportunity to more precise and open property valuation practices. A lot of conventional real estate appraisal methodologies with much emphasis on manual evaluation and historical comparative value find it difficult to respond to the market dynamics or the dynamics that exist within the market at an alarming rate. This research will help mitigate these shortcomings by generating and testing machine learning regression models to determine the price of residential property at sale in Dubai by using real-life …

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Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo Apr 2026

Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Predominantly oral languages (POLs) face a significant "digital divide," as they are often excluded from the benefits of modern natural language processing (NLP) technologies, due to a lack of extensive, readily available machine learning (ML) datasets. We investigate methods to overcome this data scarcity for Bambara, a Manding language, spoken primarily in Mali, with a rich oral tradition but limited digital presence.     The research leverages crowdsourcing and community engagement to build high-quality ML ready dataset resources. Key contributions include methods for automatic speech recognition (ASR) and machine translation (MT) dataset collection and curation and for educational resource creation.      Our findings …

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The Best Practices For Prefabrication Of Industrial Buildings, Derek Gines Apr 2026

The Best Practices For Prefabrication Of Industrial Buildings, Derek Gines

Theses

Prefabrication has demonstrated measurable and repeatable advantages in productivity, cost certainty, and environmental performance, yet its adoption within industrial building typologies remains largely inconsistent. Existing research largely evaluates prefabrication through downstream performance outcomes, while offering limited insight into the upstream design and organizational decisions that enable or undermine its reliability. This thesis reframes prefabrication as a design‑led methodology rather than a construction optimization, arguing that successful hybrid prefabrication is determined primarily by early decision timing, governance structures, and the control of spatial and logistical interfaces. This study adopts a qualitative design‑research approach that combines comparative case study analysis with expert …

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Predicting Luxury Car Sales Using Machine Learning: A Comparative Study Of Linear Regression, K-Nearest Neighbors, And Support Vector Machines, Khalifa Jamal Mohammad Saleh Alblooshi Apr 2026

Predicting Luxury Car Sales Using Machine Learning: A Comparative Study Of Linear Regression, K-Nearest Neighbors, And Support Vector Machines, Khalifa Jamal Mohammad Saleh Alblooshi

Theses

This paper explores the use of machine learning to predict the sales of luxury cars in the globe, and BMW as a case study of its sales data in the global market between the period 2010 and 2024. The study focuses on three regression algorithms, which include the Linear Regression, K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) and sees their predictive accuracy, generalisation performance and business applicability. In Python with the help of the Google Colab, an end-to-end analytical pipeline was developed entailing data preprocessing, outlier management, feature engineering, and time-sensitive traintest division. RMSE, MAE, MAPE, and R2 were …

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Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta Apr 2026

Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta

Theses

As artificial intelligence (AI) becomes increasingly common in computational social science, \textit{inconsistency} has emerged as a key challenge. AI models often contradict themselves when given equivalent inputs, disagree with other models on the same data, and diverge from human judgments in seemingly opaque ways. Human annotators exhibit their own inconsistencies, both within individuals and across groups shaped by differing values and identities. Rather than treating these inconsistencies simply as noise, this dissertation argues that they contain meaningful signals that can be leveraged to improve learning efficiency, strengthen evaluation, and increase the reliability of large-scale social measurement. To study this phenomenon, …

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Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu Apr 2026

Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu

Theses

Artificial intelligence systems have achieved remarkable performance across a wide range of visual tasks. However, most existing models operate under the unrealistic closed-world assumption, where training and test data are drawn from the same distribution. In real-world applications such as anomaly detection, autonomous driving, and medical diagnosis, learning systems frequently encounter novel or out-of-distribution scenarios. These settings require models that can recognize unknown inputs, adapt to new information over time, and maintain reliable performance under evolving conditions. This dissertation studies the problem of Open World Visual Learning, a paradigm that enables visual learning systems to operate robustly in dynamic and …

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Alternative Color Mode Design: Supporting Mobile App Creators With Evidence-Based Guidelines, Sarah Andrew Apr 2026

Alternative Color Mode Design: Supporting Mobile App Creators With Evidence-Based Guidelines, Sarah Andrew

Theses

Alternative color modes, such as light, dark, dim, and high contrast modes, in mobile apps can improve accessibility for people with vision impairments and usability for people without vision impairments across situational contexts. However, current mobile apps exhibit inconsistent color implementations for UI elements (e.g., background, text, buttons, images, and non-selectable icons), leaving users with limited accessible options. My dissertation addresses a central question in human-computer interaction and accessibility: How can mobile app designers be supported to implement alternative color modes that meet the accessibility and usability needs of people with and without vision impairments? Through an eight-study mixed-methods investigation, …

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Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli Apr 2026

Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli

Theses

Modern web applications often display large datasets with tens of thousands of items, such as e-commerce catalogs, data tables, and social media feeds. Rendering all items in the Document Object Model (DOM) at once causes browser freezing, high memory use, and slow interfaces. Virtual scrolling solves this problem. It is widely adopted but rarely studied through direct performance comparison. Few empirical studies measure how different implementations behave under varying dataset sizes, devices, or browsers. This research conducts a comparative analysis of Angular CDK Virtual Scroll as an industry-standard baseline and develops an optimized implementation incorporating framework-specific enhancements: OnPush change detection …

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Navigating The Ai Classroom: Integrating Ai In Education For Enhanced Learning And Career Readiness, Katryna M. Johnson Apr 2026

Navigating The Ai Classroom: Integrating Ai In Education For Enhanced Learning And Career Readiness, Katryna M. Johnson

Journal of Applied Marketing Theory

This study employs an innovative approach by integrating AI and human endeavors in the research process. The examination provides a literature review and theoretical frameworks for integrating AI in education, focusing on three key stakeholder groups: students, faculty, and institutions. The analysis explores the benefits and challenges of AI in the classroom from each group’s perspective. The paper emphasizes hands-on AI experience for marketing students to remain competitive. The paper provides practical tips and suggestions for using AI in marketing classes and creating an institutional environment to support long-term AI growth. Additionally, the study presents widely used AI tools in …

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Integrating Artificial Intelligence Into Marketing Education: A Conceptual Framework For Curriculum Enhancement, Aisha Ghimire, Gallayanee Yaoyuneyong Apr 2026

Integrating Artificial Intelligence Into Marketing Education: A Conceptual Framework For Curriculum Enhancement, Aisha Ghimire, Gallayanee Yaoyuneyong

Journal of Applied Marketing Theory

This paper introduces a Constructivist–Experiential GenAI Integration Framework to embed Generative AI (GenAI) across undergraduate marketing curricula. Grounded in constructivist and experiential learning theories, the framework positions GenAI as a learning partner, through hands-on, reflective, and ethically informed learning. Discipline-specific vignettes illustrate how GenAI can enhance marketing courses through accessible and scalable activities that align with diverse class sizes, institutional resources, and delivery modes. By leveraging freely available GenAI platforms, the model promotes inclusivity while cultivating AI literacy, ethical reasoning, and strategic agility. The paper concludes with recommendations for empirical validation and cross-institutional collaboration to ensure that GenAI-driven marketing education …

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Precision Or Invasion? Mapping The Personalization Paradox, Shyamala N. Chalakudi, Gnana Bharathy Apr 2026

Precision Or Invasion? Mapping The Personalization Paradox, Shyamala N. Chalakudi, Gnana Bharathy

Journal of Applied Marketing Theory

AI-driven personalization has transformed consumer engagement, yet existing research offers no systematic way to determine when personalization shifts from helpful to intrusive. Current research and models explain privacy concerns or expectancy violations but do not measure the point at which personalization triggers user discomfort, creating the personalization paradox. This study introduces the Discomfort Threshold Assessment & Measurement (DTAM) framework, a conceptual model that identifies and evaluates this threshold by integrating Dual Process Theory, Expectation Violation Theory, and Privacy Calculus Theory. DTAM distinguishes between affective discomfort reactance and cognitive discomfort reactance and outlines how these constructs could, in future empirical studies, …

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Clusters Of Student Perceptions Of Ai-Assisted Grading: Implications For Fair And Transparent Assessment Practice In Higher Education, Roberto Bello Apr 2026

Clusters Of Student Perceptions Of Ai-Assisted Grading: Implications For Fair And Transparent Assessment Practice In Higher Education, Roberto Bello

Journal of Applied Marketing Theory

As artificial intelligence (AI) tools become increasingly embedded in assessment and feedback systems, understanding how students perceive these technologies is vital for maintaining trust and fairness in higher education. This study investigates how students experience AI-assisted grading through an extended Technology Acceptance Model (TAM) that incorporates fairness, transparency, and trust as pedagogically relevant constructs. Survey data from undergraduate marketing students (N = 142) were analyzed using cluster analysis, revealing three distinct perception profiles: Enthusiasts, Pragmatists, and Skeptics. These clusters differ significantly in their willingness to rely on AI feedback, perceived fairness of algorithmic grading, and expectations of …

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