Engineering Commons^™

Interfacial Chemistry Involved In Selective Separation Of Nmc/Lmo And Lco/Lmo Binary Cathode Materials By Froth Flotation Using Oleic Acid, Richard Kofi Oboh, Kaiwu Huang, Seoung Bum Son, Lei Pan

Michigan Tech Publications

The variability in cathode compositions within recycled lithium-ion battery (LIB) feedstocks poses a significant challenge to efficient downstream refining processes. This study demonstrates the feasibility of using froth flotation with oleic acid as a collector to selectively separate lithium nickel-manganese-cobalt oxide (NMC) and lithium cobalt oxide (LCO) from lithium manganese oxide (LMO) materials. Laboratory-scale flotation tests achieved an 80% separation efficiency in a single stage, producing a froth product with >90% purity of NMC/LCO at approximately 90% yield. Concurrently, the LMO materials were enriched in the sink product with ∼90% purity and ∼90% yield. This approach was further validated using …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

A Transcendental Phenomenological Study Of Cognitive Overload In Manufacturing Engineers During Online Training In An Industrial Environment, Sarah J. May

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to explore experiences of cognitive overload among engineers in manufacturing during digital training in industrial environments. Engineers in manufacturing manage substantial physical and cognitive demands in order to maintain safe and efficient industrial environments. In addition to these responsibilities, engineers are often expected to engage in digital training within active work settings, frequently without physical or cognitive separation from job-related tasks. A qualitative research design was employed, with data collected from 10 participants and analyzed through triangulation and thematic coding. Data collection methods included a qualitative questionnaire, individual interviews, and a letter-writing …

Predictive Analysis Of Greenhouse Gas Emissions From Electric Vehicle Charging In The United States, Mahyar Amirgholy, Faysal A. Chowdhoury, Chenyu Wang, S Nikhila Kanigiri

Faculty Articles

Electric vehicles (EVs) emit substantially fewer air pollutants than conventional internal combustion engine vehicles. However, the continuous increase in electricity demand from the power grid for EV charging, resulting from the growing adoption and total vehicle miles traveled, leads to higher greenhouse gas emissions from the power sector. This study presents a predictive analysis of energy sector greenhouse gas emissions from EV charging at the regional level across the United States under various projection scenarios of technology costs, fuel prices, demand growth, and electricity sector policies. The predictive modeling of greenhouse gas emissions from EV charging is performed using a …

Comparative Assessment Of Uav-Based Tseb And Field-Calibrated Aquacrop For Evapotranspiration On The Arid Coast Of Peru, Roxana Peña-Amaro, José Huanuqueño-Murillo, Lia Ramos-Fernández, Abel Ramos-Ayala, David Quispe-Tito, Lena Cruz-Villacorta, Elizabeth Heros-Aguilar, Edwin Pino-Vargas, Alfonso Torres-Rua

Civil and Environmental Engineering Faculty Publications

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature …

Predicting Tart Cherry Stem Water Potential Using Uav Multispectral Imagery And Environmental Data Via Symbolic Regression, Anderson L. S. Safre, Alfonso Torres-Rua, Kurt Wedegaertner, Brent Black, Brennan Bean, Burdette Barker, Matt Yost

Civil and Environmental Engineering Faculty Publications

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Redesigning Online Graduate Orientation To Foster Academic Resilience And Prevent Underperformance, Stella Michael-Makri, David E. Rodriguez

Journal of Academic Underperformance

Graduate students in fully online programs often begin their academic journey without adequate preparation for the emotional, structural, and cultural challenges of graduate-level work. For students who are first-generation, racially marginalized, international, or returning to education after time away, this lack of scaffolding can lead to early disengagement, underperformance, or attrition. Orientation, often treated as a checklist of logistical tasks, represents a missed opportunity for meaningful academic intervention. This manuscript proposes a five-module conceptual model for online graduate orientation designed to proactively support online graduate students in the domains of emotional regulation and academic identity, time management and executive functioning, …

Developing Low-Cost Gnss Remote Sensing Hardware To Measure Precipitable Water Vapor For Flash Flood Nowcasting In Southern Appalachia, Austin Gleydura

Student Research Symposium (SRS)

Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing …

Mitigating Uas Airspace Risks Through Policy Innovation, Christopher Daniel Sidor

Student Research Symposium (SRS)

Uncrewed Aircraft Systems (UAS), commonly known as drones, have become an everyday part of our lives. Once a technology reserved for the defense industry, UAS are now widely available and affordable in the commercial market. These systems have been used for intelligence, surveillance, and reconnaissance (ISR) missions, route mapping, and kinetic deployment of munitions. In modern warfare, drones have been at the forefront, leveraging new tactics, techniques, and procedures to enhance lethality and destruction. The integration of fiber-optic (FO) connected drones, first-person view (FPV) technology, and 3D printed munition-dropping devices in particular demonstrates a dire need for legislative intervention. These …

The Wildlife Intelligence For Aviation Safety (Wild-Ai), Bao Khoa Tran

Student Research Symposium (SRS)

Wildlife strikes pose a significant safety risk and economic burden to the aviation industry. Over 291,000 strikes have been reported in the U.S. since 1990, causing an estimated $248 million in annual losses. The Wild-AI (Wildlife Intelligence for Aviation Safety) project leverages cutting-edge technologies, including large language models (LLMs), machine learning (ML), explainable AI (XAI), and Unmanned Air Vehicles (UAV) imagery, to enhance wildlife hazard management at airports. Wild-AI transforms traditional Wildlife Hazard Assessments (WHAs) by integrating advanced data analytics and UAV monitoring. By utilizing historical wildlife-strike data and ML algorithms, we will identify key predictors of damaging strikes, such …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Sun, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO₄)₂ and Zn(OTf)₂, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Wu, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in …

Translating Graduate Level Engineering Methods For K12 Students, Ahmad Bshennaty, Joey Marano, Hoda Hatoum

Michigan Tech Publications

Purpose

Advanced engineering tools such as particle image velocimetry (PIV) and computational fluid dynamics (CFD) are rarely introduced in K12 education despite their relevance in modeling engineering systems. This study evaluates whether K12 students can meaningfully engage with these traditional graduate-level methods when applied to cardiovascular flows and delivered through structured modules.

Methods

A 5-day full-time summer class was designed and enrolled 13 secondary students (grades 9–11) to introduce them to 3D printing, coding, PIV, medical image segmentation, and CFD modeling. Students participated in both experimental and computational activities and completed two evaluation forms assessing learning outcomes, instructional methods, challenges, …

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

Reinforcement Learning For Imbalanced Data In Robotic Anomaly Detection Within Autonomous Manufacturing, Salma Messaoudi, Ahmed Bendaouia, El Hassan Abdelwahed, Mohammed Ameksa, Hajar Mousannif, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Ensuring reliable anomaly detection in industrial robots is critical for safe and autonomous manufacturing operations. However, it remains challenging due to temporal dependencies and class imbalance in sensor data. This study presents a reinforcement learning approach using Deep Q-Network (DQN) enhanced with Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) for robust anomaly detection in robotic systems. The proposed framework integrates an LSTM into the DQN policy to capture temporal patterns. It also introduces a novel GBM-based reward mechanism that mitigates class imbalance by applying SMOTE (Synthetic Minority Over-sampling Technique) after removing temporal dependencies. Experimental results demonstrate that this …

Scheduling As An Organizational Capability, Felix Delmonte

Mechanical and Civil Engineering Faculty Publications

This study synthesizes evidence on schedule governance as an organizational capability integrating Lean practices, BIM, Digital Twin, and AI to convert scheduling into continuous, data driven decision making that links front line observations to executive risk appetite and funding choices, showing how 4D/5D BIM, clash detection, automated material takeoff, and IoT enabled twins shorten planning horizons, reduce design errors by up to 80%, improve timeline adherence by 20%–30%, and, when AI is applied, further cut delays by about 18% and procurement costs by approximately $2.5 million; a four layer Integrated Lean–Digital Framework is proposed to align strategic intent, collaborative digital …

Corrigendum Notice: Motion Fusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Almulla, Hussein K.; Mohammed, Hussam J.; Al-Waisy, Alaa S.; Al-Fahdawi, Shumoos; Had, Ahmed Adnan; and AL-Attar, Bourair (2025) ``MotionFusion: A Robust Ensemble Learning Framework for Accurate Sensor-Based Human Activity Recognition,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 14.
DOI: https://doi.org/10.52866/2788-7421.1289.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/14.
Reason for Corrigendum: In the published version of the above article, a number of typographical and formatting errors were identified. These corrections do not affect the study design, results, or conclusions, and are provided below to ensure accuracy and consistency. 1) Feature subset sizes …

Corrigendum Notice: Machine Learning Algorithms To Detect Cyber-Attack In The Internet Of Things Platform, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Al-anni, Maad Kamal; Almuttairi, Rafah M.; Al-Hamadani, Ammar A.; Zidan, Khamis A.; Alsaadi, Husam Ibrahiem Husain; and Al-Sultany, Ghaidaa A (2025) ``Machine Learning Algorithms to Detect Cyber-Attack in the Internet of Things Platform,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 26.
DOI: https://doi.org/10.52866/2788-7421.1268.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/26.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. DOI correction: The DOI printed in the article PDF as ``10.52866/ijcsm.0000'' is incorrect and should be ``10.52866/2788-7421.1268.''
2. Dataset split correction: …

Corrigendum Notice: Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Lafta, Mariem Hassan and Hassan, Zahir Abdul Haddi (2025) ``Finding General Mathematical Formulas for Extraction the Minimal Path Sets of Complex Parallel-Series Networks,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 11.
DOI: https://doi.org/10.52866/2788-7421.1237.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/11.
Reason for Corrigendum: In the published article, the definition of minimal path set is stated incorrectly. The text defines a minimal path set as ``a set of components whose failure leads to failure of the whole CPSN,'' which corresponds to a minimal cut set, not a minimal path set. Correction: A …

Corrigendum Notice: Federated Learning-Driven Iot And Edge Cloud Networks For Smart Wheelchair Applications, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Mohammed, Mazin Abed; Abd Ghani, Mohd Khanapi; Lakhan, Abdullah; AL-Attar, Bourair; and Khaled, Waleed (2025) ``Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Applications,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 9.
DOI: https://doi.org/10.52866/2788-7421.1241.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/9.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. Table 5 (Task Status entry) — data cell error
In Table 5, the entry for Device ID 3, Iteration 5 contains an incorrect/misaligned value in the Task Status …

Corrigendum Notice: A New Approach For Multiprocessor System-On-Chip Application Scheduling In Hybrid Flow Shop, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Khraibet, Tahani Jabbar; Kalaf, Bayda Atiya; and Jasim, Ahmed Abbas (2025) ``A New Approach for Multiprocessor System-On-Chip Application Scheduling in Hybrid Flow Shop,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 45.
DOI: https://doi.org/10.52866/2788-7421.1318.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/45.
Reason for Corrigendum: This note corrects (i) author-affiliation metadata, (ii) minor textual duplications/typographical errors, (iii) a naming inconsistency of the proposed algorithm, and (iv) duplicated sentences in the dataset description. These corrections do not change the experimental results or conclusions; they improve clarity and metadata accuracy. 1) Correction to author affiliation metadata …

Retraction Notice: Reliability-Based Design Optimization Using Differential-Algebraic Equations, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Abed, Saad Abbas; Ghassan, Mona; Latef, Shaimaa Qais; and Hassan, Hind S. (2025) ``Reliability-Based Design Optimization Using Differential-Algebraic Equations,'' Iraqi Journal for Computer Science and Mathematics: vol. 6, Iss. 3, Article 8. DOI: https://doi.org/10.52866/2788-7421.1280.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/8.

Three-Dimensional Printing Of The Epineurium For Peripheral Nerve Repair: A Comprehensive Review Of Novel Scaffolds For Nerve Conduits, Alynah J. Adams, Iulianna C. Taritsa, Kaavian Shariati, Aaron Dadzie, Jose A. Foppiani, Maria Jose Escobar-Domingo, Daniela Lee, Angelica Hernandez-Alvarez, Kirsten Schuster, Helen Xun

School of Medicine Publications

Background: Nerve conduits are used to bridge peripheral nerve defects caused by trauma, iatrogenic injury, or oncologic disruption. Three-dimensional (3D) biomimetic scaffolds for peripheral nerve regeneration have advanced significantly in recent years, driven by improvements in printing technology and neuronal seeding techniques. We report on published designer conduits that can recreate the epineurium, a critical yet challenging-to-manufacture feature of nerve tissue.

Methods: A medical librarian conducted a literature search for our systematic review on EMBASE, Web of Science, and PUBMED, following PRISMA guidelines, for articles from January 2010 to January 2026 for the systematic review. Descriptive statistical analysis was performed …

The Ripple Effect: How The Hse Data Breach Shaped Public Opinion, Mugdha Srivastava, Abhishek Kaushik, Roisin Loughran, Fergal Mccaffery

Women+ in Early Career Research Symposium

The digital world is rapidly evolving, and healthcare systems now depend heavily on data-driven and connected technologies. While these tools bring many benefits, they also increase exposure to data breaches and cyberattacks, raising concerns about privacy, security, and trust. Although earlier research has focused on technical and policy solutions for data protection, less attention has been paid to how major cyber incidents influence public attitudes. This study addresses that gap by examining how a major cyberattack relates to people’s views on data security and trust in digital health. We surveyed individuals about their personal cybersecurity practices and their opinions on …

Beyond The Sayable: Wittgenstein, Theory Of Mind And Affective Simulation Of Llms, Haomiaomiao Wang, Lili Zhang, Tomás E. Ward

Women+ in Early Career Research Symposium

Wittgenstein’s distinction between what can be said and what can only be shown frames a limit of propositional language. Affect and aesthetic are not primarily matters of correct description but of how expressions function within shared forms of life. Large language models (LLMs), however, increasingly produce fluent language that resembles such understanding by reproducing the patterns through which people ordinarily talk about emotion and perspective.

This paper argues that apparent Theory of Mind (ToM) in LLMs is understood as competence in the publicly shared patterns of mental-state language, rather than as grounded understanding. Using abstract artworks, we show that LLMs …

Bridging Research And Practice: Your Guide To Implementing Evidence-Based Gender Equality Actions In Computing Education, Alina Berry

Women+ in Early Career Research Symposium

What if you could access proven strategies for promoting gender equality without spending months reviewing academic and grey literature? TechMate makes this possible. This free, open-access toolkit transforms scattered research into actionable guidance (actions) that anyone can use, whether you are a lecturer wanting to make your classroom more inclusive, a department head developing new policies, or a student advocate pushing for change. Actions are structured into four context-based categories and relevant subcategories. Each action comes with implementation instructions, where relevant, with ready-to-use templates, and links to the supporting evidence. While developed for computing higher education, TechMate’s research-driven approach is …

Engaging Older Adults Through Artefact-Driven Approaches For Ai Literacy Development: A Multi Stakeholder For Digital Inclusion, Paula Kelly, Mayank Parmar, Damon Berry, André Almo, Fatima Badmos, Peterson Jean

Women+ in Early Career Research Symposium

As Artificial Intelligence (AI) becomes deeply integrated into modern ICT and e-Accessibility services, older adults face a growing "digital divide" often characterised by a lack of awareness regarding how automated AI systems frequently functioning as "black boxes" infiltrate and impact their daily lives. This paper presents a multimodal workshop model designed to foster critical AI literacy among older adults, transitioning them from passive recipients to active stakeholders. Utilising an Action Research framework, the study details the co-creation phase of a three-phase national Age- Friendly AI literacy initiative in Ireland. Central to the methodology is the use of tangible exhibits as …