Engineering Commons^™

Willingness To Pilot Under Varying Weather And Tower System Conditions: Mediating Effects Of Risk, Nathan Royal Schultz

Doctoral Dissertations and Master's Theses

The implementation of Remote Tower Systems (RTS) within the National Airspace System (NAS) offers potential improvements in operational efficiency, cost-effectiveness, and scalability; however, its success depends on pilots' willingness to operate in such environments. This study examined the effects of tower system type (RTS vs. Traditional Tower Systems [TTS]) and weather conditions (visual meteorological conditions [VMC], marginal visual meteorological conditions [MVMC], and instrument meteorological conditions [IMC]) on pilots’ willingness to pilot (WTP), while also evaluating the mediating role of perceived risk (PR). Grounded in Paul Slovic’s Risk Perception Theory (RPT), this research employed a quantitative experimental design using a factorial …

Heavy-Duty Diesel Vehicle Deterioration: Impact Of Vehicle Age And Emission Control Technology On Nox Emissions - Preprint, Amber L. Gurecki Allen, Andrew R. Colman, Max D. Sommer, William D. Waddoups, Darrell B. Sonntag

FEAT Publications

Pre-print manuscript.

Abstract

This study examines the effect of aging and deterioration on NO_x emissions from heavy-duty diesel trucks (HDDTs) with different emission control technologies, including Engine Control only, exhaust gas recirculation (EGR), and selective catalytic reduction (SCR). In-use tailpipe emissions were measured from 12,023 HDDTs from 13 roadside measurement campaigns conducted in California and Utah between 2008 and 2024. Vehicle registration records and the Environmental Protection Agency’s (EPA) engine certification database were used to identify engine NO_x control technology. At younger ages, SCR-equipped HDDTs generally emitted significantly less NO_x than non-SCR-equipped HDDTs. However, after several years …

Domain Knowledge-Informed Machine Learning Model To Study Weather-Induced Flight Delays, Gurvir Bawa

Doctoral Dissertations and Master's Theses

Weather-induced Traffic Management Initiatives (TMIs) occur rarely relative to normal air traffic operations. This scarcity produces severe class imbalance that limits predictive models of flight delay. To address it, this dissertation leverages a physics- and domain- knowledge informed Variational Autoencoder (VAE), conditioned on airport identity, that generates physically plausible synthetic weather events for underrepresented TMI categories. The fidelity of the model is assessed with a calibrated discrepancy measure disclosed along- side the results rather than described in free-form terms. A multitask Graph Neural Network (GNN) is then trained on the augmented dataset to jointly classify airport-level TMI status and predict …

Decoding Surface Chemistry Effects On Polystyrene Nanoplastic Fouling In Plasma-Grafted Pes Membranes With Distinct Functional Groups, Mohadeseh Najafi, Javad Farahbakhsh, Ebrahim Mahmoudi, Michael Johns, Masoumeh Zargar

Research outputs 2022 to 2026

Nanoplastic (NP) fouling remains a key challenge for ultrafiltration (UF) membranes, yet systematic, controlled comparisons of how membrane functional groups govern NP-membrane interactions are still limited. Here, the role of membrane surface chemistry was systematically investigated using a controlled comparative framework based on plasma-grafted polyethersulfone (PES) UF membranes tailored with structurally comparable methacrylate monomers bearing distinct terminal functionalities, including mono-2-(methacryloyloxy)ethyl succinate (MMES, -COOH), N-[3-(dimethylamino)propyl] methacrylamide (DMAPMA, -N(CH3)2), and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA, zwitterionic). Comprehensive characterisations supported successful surface grafting while preserving the membrane substructure. Fouling and separation were assessed using polystyrene (PS) NPs having different surface chemistries, i.e., PS, PS-COOH and …

Numerically Evaluating The Effect Of Pin Geometries On Interlayer Material Mixing And Thermo-Mechanical Characteristics During Additive Friction Stir Deposition (Afsd), Numan Habib, Ana Vafadar, Ferdinando Guzzomi

Research outputs 2022 to 2026

Additive Friction Stir Deposition (AFSD) is an additive manufacturing technique used to fabricate large-sized components layer-by-layer in a solid state, below the melting temperature. Poor interlayer mixing between subsequent layers results in a lack of mechanical interlocking, leading to low-strength components. The role of pin design plays a crucial role in fabricating high-strength components, requiring a comprehensive understanding of thermo-mechanical behaviour and flow pattern in the deposition zone. In this study, five different pin geometries are presented and investigated using a three-dimensional (3D) computational fluid dynamics (CFD) model. A user-defined function (UDF) was used to calculate strain- and temperature-dependent viscosity. …

Performance Evaluation Of Header Pipe In Heat Pipe Evacuated Tube Solar Collector By Dimpled Condensers, Furqan Jamil, Mehdi Khiadani, Yasir M. Al-Abdeli, Abdellah Shafieian

Research outputs 2022 to 2026

Among different solar water heaters, heat pipe-based solar water heaters foster potential solutions to alleviate water scarcity. However, a lower outlet temperature at the outlet section of their header pipe reveals low system efficiency due to limited heat transfer between the flowing fluid and the heat pipe condenser walls. The main aim of this study is to investigate a model manifold header pipe having five condenser sections with a dimple pattern to replicate internal features of heat pipe condensers within an evacuated tube solar collector, and its significance is to provide a comprehensive understanding of flow and thermal behaviour in …

A Review Of Natural Hydrogen Generation From Iron-Rich Rocks: Mechanisms, Influencing Factors, Techniques, And Knowledge Gaps, Kaveh Moghanirahimi, Lionel Esteban, Marina Pervukhina, Muhammad Arif, Stefan Iglauer, Alireza Keshavarz

Research outputs 2022 to 2026

Natural hydrogen has emerged as a promising clean energy resource with significant potential for large-scale subsurface production. Among the various geological sources, water–rock interactions involving iron-bearing rocks are among the most extensively studied pathways for natural hydrogen generation. This study provides a comprehensive review of hydrogen production from iron-rich lithologies, including mafic and ultramafic rocks, iron oxides, iron carbonates, and peralkaline granites. The fundamental mechanism involves the reduction of water coupled with the oxidation of ferrous iron to ferric iron under anoxic conditions. Key processes, including serpentinization, magnetite alteration, and siderite decomposition, are critically evaluated using experimental and modelling data. …

Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry

Engineering and Technology Management Faculty Publications and Presentations

This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …

Ai-Driven Biomarker Discovery & Progression Modeling For Precision Diagnosis Of Glaucoma, Cheng Huang

Computer Science and Engineering Theses and Dissertations

This dissertation presents a comprehensive study on the integration of artificial intelligence (AI) for glaucoma diagnosis and retinal image analysis. Leveraging multimodal imaging data including fundus photography, Optical Coherence Tomography Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), the research develops a suite of deep learning frameworks designed to detect early glaucomatous changes with high precision, robustness, and interpretability. A series of novel architectures are introduced, spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models, all designed to enhance diagnostic accuracy and generalizability across diverse populations. To facilitate reproducible and scalable ophthalmic AI research, this work …

Cross-Dataset Stability Assessment Of A Fixed-Configuration Catboost Model Across Heterogeneous Civil Engineering Material Datasets, Arslan Qayyum Khan, Ali Raza, Amorn Pimanmas

Civil Engineering Faculty Publications

Machine learning (ML) has increasingly been applied in civil and structural engineering for predicting material and structural performance. However, most existing studies either benchmark several algorithms within a single dataset or review such findings, leaving the cross-dataset stability of one fixed model configuration largely unquantified. This study proposes a generalized ML evaluation framework to investigate the robustness and stability of a fixed-configuration model across multiple civil engineering material datasets. Ten datasets representing diverse structural and material problems were compiled, including 3D printed concrete (3DPC), conventional and advanced concrete including normal and high strength concrete (NHSC), recycled aggregate concrete (RAC), ultra-high-performance …

Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh

All Works

The food supply chain is undergoing critical changes to minimize environmental hazards, such as those associated with packaging, and reduce food waste and loss. One of the critical components of the latter is associated with the high variance in the perishability of individual foodstuffs. Herein, we review the state of the art in smart sensor systems and emphasize the critical role they could play in addressing the mismatch between “batch” based expiry dates and individual food products’ time-dependent responses to spoilage. Following a brief overview of food shelf life and associated legislation, a subsequent section summarizes the development of sensors …

The Vegetated-Canopy Roughness Sublayer Through A Tke-Budget Lens, Giulia Salmaso, Raul Bayoan Cal, Marc Calaf

Mechanical and Materials Engineering Faculty Publications and Presentations

It is well understood that vegetated canopies perturb the atmospheric flow within and immediately above the canopy, deviating from the inertial sublayer (ISL) regime characteristic of non-vegetated rough surfaces. Only above a certain height, denoted as the roughness sublayer height (RSL, 𝑧∗), do these flows return to ISL-like behavior. This distinction is critical, as parameterizations in Earth System Models (ESMs) are typically developed for ISL flows. Yet, most 𝑧∗ estimates stem from studies over flat, homogeneous canopies and may be biased. In contrast, little is known about 𝑧∗ in non-homogeneous or heterogeneous canopies that are not of agricultural interest, with …

Analysis And Design Of Multi-Winding Current Ballasted Magnetics For Integrated Dc-Dc Conversion, Kishalay Datta

Dartmouth College Ph.D Dissertations

Power conversion circuits have become a bottleneck in terms of reducing the size and cost of modern electronic systems in a vast number of applications ranging from personal devices, to automotive systems, to data centers. This has motivated designs at higher frequencies where smaller passive components can be used. Unfortunately, magnetic components scale poorly to smaller dimensions. In addition, the AC resistance of magnetic components deteriorates due to current crowding effects at high frequencies. This has resulted in fully- integrated power converters with planar magnetic components having efficiencies lower than what may be theoretically achieved. In this work, we investigate …

Enhancement In Surface Finish Of Additively Manufactured 17-4ph Stainless Steel Using A Hybrid Chemical Machining–Magnetic Abrasive Finishing Approach, Harish Kumar, Gurpreet Singh, Anil K. Srivastava, Majid Hosseini

Manufacturing & Industrial Engineering Faculty Publications

Additive Manufacturing (AM) is a cutting-edge technique utilized for the precise manufacturing of complex components made from a variety of material compositions. Although AM, generally, produces a better surface finish than traditional subtractive methods, additional post-processing is required to attain the desired surface characteristics. One commonly used AM material is 17-4PH stainless steel, which is majorly employed in modern industries like aerospace, marine, petroleum, chemical, medical, and firearms due to its superior physical and chemical properties compared to other stainless steels. Surface finish of additively fabricated 17-4PH stainless steel can be further improved using various finishing techniques. This study presents …

The Global-To-Local Trust Layer: From A Working Prototype To A Trustworthy One, Amrita Datta

Paul English Applied Artificial Intelligence (AI) Institute Publications

This guest speaker session, part of Professor Wei Ding's Industry AI Applications course at the University of Massachusetts Boston, features Amrita Datta, Global Marketing Strategist, Enterprise AI Advisor, and Vice President and Board Member of AMA Boston. The session introduces the Global-to-Local (G2L) Framework and explores how to turn working AI prototypes into trustworthy and reliable applications. Through real-world examples, participants learn about key elements of trustworthy AI, including guardrails, data boundaries, human oversight, failure handling, and evaluation, and explore how these concepts can be applied to their own AI projects.

A Graph Attention Network-Based Framework For Reconstructing Missing Lidar Beams, Khalfalla Awedat, Mohamed Abidalrekab, Mohammad El-Yabroudi

Electrical and Computer Engineering Faculty Publications and Presentations

Vertical beam dropout in spinning LiDAR sensors -triggered by hardware aging, dust, snow, fog, or bright reflections - removes entire vertical slices from the point cloud and severely degrades 3D perception in autonomous vehicles. This paper proposes a Graph Attention Network (GAT)-based framework that reconstructs these missing vertical channels using only the current LiDAR frame, with no camera images or temporal information required. Each LiDAR sweep is represented as an unstructured spatial graph: points are nodes and edges connect nearby points while preserving the original beam-index ordering. A multi-layer GAT learns adaptive attention weights over local geometric neighborhoods and directly …

Size-Tunable Tellurium Quantum Dots By Glancing Angle Deposition, S. M. Sayem, Salim Hussain, Fernando Maia De Oliveira, Ranjitha Kumarapuram Hariharalakshmanan, Gregory Guisbiers, Tansel Karabacak

Faculty Scholarship

Tellurium has a unique helical crystal arrangement and pronounced anisotropy that influence its electronic and optical properties at the nanoscale. This study reports the synthesis of pure tellurium quantum dots (Te QDs) on silicon wafer using glancing angle deposition (GLAD). Quasi-hemispherical dots with lateral sizes ranging from 9 to 28 nm and vertical dimensions of 6 to 10 nm were produced as a function of the deposition duration. A comparison of the dot sizes with the Bohr radii of the charge carriers indicated a regime of strong to intermediate confinement. Structural analyses confirmed the polycrystalline nature and trigonal phase of …

Treatment Of Municipal Solid Waste Incineration Bottom Ash Using Cement For Integrated Recovery Of Hydrogen And Production Of Supplementary Cementitious Material, Muhammad Haris Javed, Wenyu Liao, Fathma Zuhra, David Vollero, David Schmidenberg, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Municipal solid waste incineration (MSWI) bottom ash (MBA) represents both a disposal challenge and a potential resource for sustainable construction materials, including use as a supplementary cementitious material (SCM). However, the conventional use of MBA in cement-based materials is hindered by its high metallic aluminum content, which reacts in alkaline environments to release hydrogen gas and causes volumetric instability (e.g., expansion and cracking). This study investigates a cement-based alkali pretreatment method using diluted cement suspension to stabilize metallic aluminum and improve the compatibility of MBA as an SCM. Compared to conventional alkali solutions, the proposed approach avoids the direct use …

Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang

Electrical and Computer Engineering Faculty Research & Creative Works

To enable large scale efficient electrochemical CO₂ reduction reaction (CO₂RR) to formic acid (HCOOH), it is important to develop catalysts that can be operated in a wide potential window with good stability. Herein, we successfully synthesized Bi₂O₃ catalyst supported on graphene oxide (GO) and graphene (G) and found that Bi₂O₃/GO catalyst had a better overall performance than Bi₂O₃/G. The Bi₂O₃/GO catalyst demonstrated an outstanding CO₂RR performance with a greater than 90% faradaic efficiency (FE) across a wide applied potential window …

Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Reliable distributed temperature sensing in high-temperature environments remains a significant challenge due to the thermal and mechanical limitations of conventional optical fibers. In particular, polymer-coated fibers degrade above ∼300 °C due to coating failure, mechanical fragility and hydrogen ingress. Metal-coated optical fibers offer a robust alternative for harsh environments such as Electric Arc Furnaces (EAFs), aerospace engines, nuclear systems, and oil and gas wells, owing to their superior mechanical strength and hermetic sealing. In this work, a first comprehensive experimental investigation of the thermo-mechanical behavior of metal-coated optical fibers for distributed high temperature sensing is presented over a wide temperature …

Rheology Modifiers For Water-Based Slurry Pipeline Transportation: Overview, Efficiency, And Intensification, Ahmed Alalou, Mohammed El Asri, Ahmed Boulahna, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Owing to their cost-efficiency and eco-friendliness, water-based slurry pipelines are the state-of-the-art technology for the transportation of several types of solids such as coal, fly ash, petcoke, iron ore, phosphate rock and other minerals. This review comprehensively examines the effect of numerous commercial dispersants and novel chemical additives that are used as rheology modifiers to enhance the slurryability and fluidity of solid-water slurries at high solids loading. Several key performance parameters such as the rheological characteristics (shear stress, apparent viscosity, yields stress, and flow behavior), the potential stability, surface tension and contact angle that highlight the efficiency of the rheology …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

An Eco-Inspired Emulsion Membrane Harnessed With Waste Vegetable Oil For Effective Separation Of Ibuprofen: Insights Into Stability, Performance, And Kinetics, Huma Warsi Khan, A. Vijaya Bhaskar Reddy, Parvez Alam Khan, Samsur Akm Rahman, Muhammad Moniruzzaman

Publications and Research

Emulsion liquid membranes (ELMs) have emerged as excellent alternatives for the separation and recovery of biologically active drugs (BADs) from industrial waste at trace concentrations. Conventional ELMs generally utilize toxic petroleum-based solvents (PBS) that pose environmental and health risks. Although virgin vegetable oils have been appeared as sustainable alternatives to PBS, their widespread application is limited by economic constraints, resource sustainability, and competition with the food supply. In contrast, waste vegetable oil (WVO) represents an abundant, low-cost, and eco-friendly alternative, yet its potential as a green diluent for ELMs remains largely unexplored. To address this challenge, present study investigated the …

Aidws3: Stubborn And Hallucinating Agents, Inal Mashukov

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop, part of the AI Demo Day Workshop Series hosted by the Paul English Applied AI Institute (PEAAII) at UMass Boston, explores the bias-variance tradeoff and how it affects the performance and reliability of AI models and agents. Led by Inal Mashukov, PhD Researcher at UMass Boston, the session covers concepts such as underfitting, overfitting, and regularization techniques to improve model performance. Through practical examples and hands-on exercises using PyTorch, participants explore why AI agents may hallucinate, repeat mistakes, or struggle to adapt, and learn approaches to building more reliable AI systems.

Aidws1: Building With Llms And Ai Agent, Olivier Kepler Francois

Paul English Applied Artificial Intelligence (AI) Institute Publications

The Paul English Applied Artificial Intelligence Institute (PEAAII) at the University of Massachusetts Boston hosted the first session of its AI Demo Workshop Series, “Building with LLMs and AI Agents,” on October 5, 2026, led by Olivier Kepler Francois, Founder and Researcher of WebAIAgent. This hands-on workshop introduced participants to developing AI-powered web applications using Large Language Models (LLMs), Next.js, React, HTTP communication, and the OpenAI API. Through guided demonstrations and practical exercises, participants explored frontend and backend development, API integration, user interaction, and the end-to-end process of building an interactive AI application. The session provided foundational knowledge and practical …

Nanoparticle-Enabled Superplasticity In Extruded Az91 Magnesium Alloy With Elongation Exceeding 600%, Bai Xin Dong, Hong Yu Yang, Xin Zhang, Feng Qiu, Qi Chuan Jiang, Lai Chang Zhang

Research outputs 2022 to 2026

No abstract provided.

Ph-Responsive Mixed-Charge Ultrafiltration Membranes For Adaptive Control Of Single And Mixed Nanoplastic Fouling, Mohadeseh Najafi, Javad Farahbakhsh, Roham Ghanbari, Ebrahim Mahmoudi, Michael Johns, Masoumeh Zargar

Research outputs 2022 to 2026

Nanoplastic (NP) fouling is an emerging barrier to the reliable application of ultrafiltration (UF) membranes, particularly in chemically complex waters where NP surface functionality governs deposition, cake formation, and cleaning performance. Herein, pH-responsive mixed-charge UF membranes were fabricated by plasma-induced graft copolymerisation of mono-2-(methacryloyloxy) ethyl succinate (MMES) and N-[3-(dimethylamino)propyl] methacrylamide (DMAPMA) onto poly(ethersulfone) (PES) to simultaneously regulate surface charge, hydration, and fouling reversibility. Unlike conventional single-charge or zwitterionic modifications, this approach enabled independent tuning of anionic and cationic moieties, generating composition-dependent and pH-switchable interfacial properties. ATR-FTIR, XPS, FESEM/EDX, AFM, WCA, streaming zeta potential, and MWCO analyses confirmed successful grafting, modified …

A Unified Pipeline For Network-Based Gene Pattern Discovery, Siddhartha Reddy Pullannagari, Sharlee Climer

Undergraduate Research Symposium

Large-scale genetic datasets from diverse diseases, including cancer and neurodegenerative disorders such as Alzheimer’s disease, provide unprecedented opportunities to study disease pathogenesis and generate hypotheses for drug discovery. However, identifying meaningful genetic interaction patterns within these datasets remains challenging due to combinatorial relationships amongst genes and heterogeneous biological underpinnings across subtypes. This project systematically analyzes a diverse collection of omics datasets (e.g., GSE44076, GSE97810, GSE127711, GSE112679) obtained from the NCBI Gene Expression Omnibus (GEO), spanning multiple biological conditions and data types. We use a unified computational pipeline designed to handle variation in dataset size, structure, and context. The steps in …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari

Jurnal Psikologi Sosial

Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …