Engineering Commons^™

Between Blockchain And Black Markets: South Africa's Legal Readiness For Crypto-Driven Cyberfraud, Sagwadi Mabunda, Yassin Chande

Communications of the IIMA

This paper examines whether the proliferation of cryptocurrency-facilitated fraud warrants a reclassification of the terrestrial crime of fraud into the distinct statutory offence of cyberfraud under South African law. Engaging with established fraud typologies — exit scams, Initial Coin Offering (ICO) scams, Ponzi schemes, pump-and-dump schemes, and market manipulation — the article tests their definitional fit against both the common law of fraud and section 8 of the Cybercrimes Act 19 of 2020. Through a hypothetical composite scenario combining multiple fraud typologies, the article demonstrates that whilst cryptocurrency significantly amplifies the reach and complexity of fraudulent schemes, it functions primarily …

Balancing Innovation And Integrity: Ethical Implications Of Ai Use In Higher Education, Petronella Muziti, Ntando S. Sibanda

African Conference on Information Systems and Technology

Artificial intelligence (AI) is rapidly transforming higher education, creating opportunities while raising ethical concerns such as bias, privacy violations, academic misconduct and reduced human-centred teaching. This study examined how institutions can balance AI innovation with academic integrity and equity, focusing on lecturers’ ethical dilemmas and institutional readiness. A triangulation design incorporating quantitative and qualitative methods was used. Twelve semi-structured interviews and two focus group discussions involving eighteen participants were conducted across five universities, while 150 questionnaires were distributed, yielding 128 valid responses. Descriptive statistics and thematic analysis were applied. Findings showed that lecturers reported student misuse and plagiarism (78%), AI …

Ubuntu Signs: A Tool For Real-Time Translation Of South African Sign Language, Simone Beets, Hasita Rama

African Conference on Information Systems and Technology

Deaf and Hard-of-Hearing (DHH) individuals often face communication challenges with people who do not understand sign language. Existing systems lack support for South African Sign Language (SASL) and are limited in accessibility, efficiency and accuracy. This paper introduces Ubuntu Signs, a mobile prototype tool for real-time SASL translation using a camera-based interface and machine learning. The tool uses two models to recognize gestures: a static model for alphabetical and numerical gestures and a dynamic model for continuous gestures representing words and phrases. A preliminary user study evaluated the effectiveness, efficiency, accuracy and satisfaction of Ubuntu Signs. Results indicated high usability …

Robbed With Permission: Toward A Receiver-Consent Architecture For Trust-Preserving Mobile Money Transactions In Africa, Raphael Amponsah

African Conference on Information Systems and Technology

Mobile money transacted more than USD 2 trillion globally in 2025 across 2.3 billion registered accounts, with Sub-Saharan Africa holding over half of all accounts and two-thirds of transaction value. Between 58 and 72 percent of mobile money fraud on the continent exploits trust rather than technology, yet current security architecture authenticates the sender and merely notifies the receiver, leaving the transaction execution moment structurally unguarded. This paper introduces the Receiver-Consent Transaction Protocol (RCTP), a USSD-native protocol requiring the receiver's active PIN consent on their physical SIM before any credit completes, grounded in Design Science Research with Routine Activity Theory …

Genai Goes To Campus: A Narrative Literature Review Of Benefits, Challenges And Policies, Jayson Warren Peters, Mmasechaba J. Seopa, Justin T. Maistry, Tebogo J. Matome, Christen Du Toit, Maureen C. Tanner

African Conference on Information Systems and Technology

This study examines the role of Generative Artificial Intelligence (GenAI) in the learning and administrative experiences of students and educators in higher education, through a narrative literature review of thirty-one articles. The literature was thematically reviewed and coded to identify themes and factors contributing to challenges, benefits, and policies and guidelines. These themes were explored in depth with their relation to GenAI for learning outcomes, the role of GenAI in student’s learning experiences and the effectiveness of using GenAI tools to support educators’ administrative processes. In light of the challenges and deterrents identified (e.g. implementation - maintenance, privacy and security, …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

Cybersecurity Governance Of Industrial Iot In Sub-Saharan Africa: Policy Gaps, Threat Landscape, And Lessons From Comparative African Contexts, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid deployment of Industrial Internet of Things (IIoT) systems across Sub-Saharan Africa's extractive, energy, logistics, and agro-industrial sectors has introduced a cybersecurity challenge of growing urgency: industrial networks that were designed for operational efficiency are increasingly exposed to cyber threats for which neither the organizations nor the regulatory frameworks are adequately prepared. This article examines the cybersecurity governance of IIoT systems in a developing African economy, using Mozambique as a primary case study and drawing comparative lessons from South Africa, Rwanda, and Kenya. Through an integrative literature review and documentary analysis of national digital, cybersecurity, and industrial policies, the …

Latent Space Exploration Of Stylegan2-Ada For Fashion Image Synthesis: A Practical Implementation Guide For Moderate-Sized Datasets, Ali H. Shareef, Hajer Ghodhbani, Tarek M. Hamdani, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

\hlThis paper presents the use of StyleGAN2-ADA for fashion image synthesis and editing. We demonstrate that generative adversarial networks (GANs) can generate realistic and diverse fashion images even under moderate data constraints. The model trained on a dataset of 9,000 fashion images achieved a Fréchet Inception Distance (FID) of 10.8, a Garment Structure Accuracy (GSA) of 0.85, a Pattern Continuity Score (PCS) of 0.78, and an Attribute Transfer Accuracy (ATA) averaging 75% across key attributes. These quantitative results confirm the model’s ability to produce high-quality, structurally consistent, and semantically coherent fashion images. Unlike previous studies focusing on …

Resolution Of Expression Of Concern: Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Editor's Note: This Resolution notice concludes the investigation initiated by the Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1386) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1302). The publication timeline is as follows: Original Article → Expression of Concern → Resolution.

Resolution of Expression of Concern:

The editorial team of the Iraqi Journal for Computer Science and Mathematics has completed a thorough post-publication review of the above-mentioned article, the supporting data, and the authors’ responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors.

We have found …

Summer Proceedings 2026, Computational Modeling Serving Portland, The Altreu Program, Portland State University, Teuscher Lab, Portland State University

altREU Projects

The 2026 Summer Proceedings bring together research conducted by undergraduate students participating in three complementary programs at Portland State University: the National Science Foundation Research Experiences for Undergraduates (NSF REU) site, Computational Modeling Serving Portland; the alternative Research Experiences for Undergraduates (altREU) program, Designing, Programming, and Using Computers to Benefit Society; and the Teuscher Lab internship program.

The NSF REU is a fully virtual, 10-week program centered on computational modeling that serves Portland and the greater region. Students begin with two weeks of intensive training in computational modeling, followed by eightweeks of mentored research with faculty, research collaborators, and community …

Design And Development Of Solar-Powered Water Desalination Systems, Salam Ali Hassoon Alabooda

Al-Esraa University College Journal for Engineering Sciences

Background: Freshwater scarcity and growing energy demand have promoted interest in sustainable desalination technologies, particularly in arid areas like Iraq which has high solar resource and decentralized water supply becomes a key factor. Aim: The purpose of this study was to design and test a solar powered water desalination system that could enhance fresh water productivity, thermal efficiency and quality of water under the climatic conditions of Iraq. Methodology: It was designed as hypothetical pilot-scale system that comprised of a single-slope solar still combined with a flat-plate solar collector, a 200 W photovoltaic-driven circulation pump and 18 kg of phase …

Trends In Non-Profit Cybersecurity: Analyzing Three Years Of Incident Data From The Npcir, Stanley Mierzwa, Joanna Paliszkiewicz, Edyta Skarzyńska

Center for Cybersecurity

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

How Do Social Network Models Compare To All-To-All Models For Forecasting Tuberculosis Epidemics? A Mathematical Modeling Study, Masabho Peter Milali, Hae-Young Kim, George Corliss, Anna Bershteyn

Electrical and Computer Engineering Faculty Research and Publications

Background. Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions.

Methods. We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention.

Results. Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Expression Of Concern: Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

EXPRESSION OF CONCERN FOR: E. T. Khudair, O. Lazzez, M. Zaied, T. M. Hamdani, A. T. Sadiq, H. Chabchoub, and A. M. Alimi, “Ontology Features-Based Arabic Text Augmentation Using Word2Vec,” Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 3, 2025, Article 40. DOI: https://doi.org/10.52866/2788-7421. 1302 Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/40

Editor's Note: The Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1386) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1302) has been resolved. The publication timeline is as follows: Original Article → Expression of Concern → Resolution.

Securing U.S. Leadership In Agentic Ai Literacy And Adoption: U.S. Vs Chinese Government Policies And Initiatives, Satyadhar Joshi

Harrisburg University Other Works

This paper conducts a comparative analysis of U.S. and Chinese frameworks for AI literacy and adoption, with focus on agentic AI and Artificial General Intelligence (AGI) systems capable of autonomous reasoning and execution. We examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent paradigms: the U.S. Favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; while China pursues centralized, state-led …

A Review Of Routing Attacks In Routing Protocol Over Low-Power And Lossy-Based Iot Networks, Lanka Chris Sejaphala Mr., Vusimuzi Malele Prof, Francis Lugayizi Prof.

Journal of Cybersecurity Education, Research and Practice

Low-power and Lossy IoT Networks (LLNs) comprise physical sensors, processing capability, power, and other technologies to exchange information between systems and devices over the internet. However, these networks are susceptible to routing attacks affecting resources, traffic flow, and topology formation. In the related work, it has been discovered that many previous studies do not consider algorithms and implementation approaches for routing attacks. This research study provides a comprehensive in-depth synthesis insight into the description, effects, and algorithms & implementation of four routing attacks in LLNs i.e., rank, sinkhole, DIS-flooding, and worst parent attacks. The findings of this research study highlight …

Utility-Privacy-Communication Trade-Offs In Federated Financial Fraud Detection Under Non-Iid Client Heterogeneity, Baraa I. Farhan

Polytechnic Journal

Financial fraud detection is difficult because fraudulent transactions are rare, unevenly distributed, and sensitive. This study evaluates a lightweight multilayer perceptron under Federated Averaging (FedAvg), Federated Proximal optimization (FedProx), and Federated Adam (FedAdam), using three seeds and validation-selected decision thresholds. Corrected R4 experiments cover the Credit Card Fraud Detection benchmark, the larger Credit Card Transactions dataset, PaySim as a stand-alone synthetic stress test, and the Bank Account Fraud (BAF) Dataset Suite. On Credit Card, FedProx with 20 clients and alpha = 0.5 achieved F1 = 0.789 +/- 0.010. On Transactions, the best tested federated setting used 10 clients and achieved …

Generative Imaging For Computational Pathology, Md Jillur Rahman Saurav

Computer Science and Engineering Dissertations

Hematoxylin and eosin (H&E) staining remains central to cancer diagnosis, providing morphological information essential for pathological assessment. Immunohistochemistry (IHC) and newer multiplexed imaging technologies complement H&E by revealing molecular information critical for accurate tumor subtyping and treatment decisions. In practice, however, H&E and IHC are obtained from different consecutive sections that are not spatially aligned, comprehensive multiplexed panels are expensive and tissue-consumptive, and not all stains are available at every clinical site, limiting comprehensive molecular profiling and the full diagnostic potential of these technologies in clinical practice. This dissertation addresses these gaps through three complementary generative deep learning studies in …

Computer Vision-Based Collision Prevention In Construction: Bridging Academic Advances And Industrial Requirements, Marwa Ziadia, François Gauthier, Damien Burlet-Vienney

Études primaires

Computer vision (CV) technologies have emerged as promising tools for real-time collision risk management. This paper examines CV-based collision-prevention systems from academic and industrial perspectives with a focus on identifying technical and implementation challenges affecting their deployment on construction sites. A scoping review of 158 publications was conducted, alongside an assessment of commercial CV-based collision-prevention systems currently used in construction. The analysis focused on five key functions: object detection, distance measurement and proximity estimation, tracking, worker re-identification (ReID), and deployment robustness. Academic studies demonstrate substantial progress in detection accuracy, real-time processing, trajectory tracking, and predictive risk assessment through deep learning …

Modern (Networked) Warfare: Its Concepts, Evolution, And Issues, Ramesh Subramanian

Journal of International Technology and Information Management

The on-going Russo-Ukraine war (February 2012-present), the recent India-Pakistan war (May 7-10, 2025), and the Israel-US attacks on Iran (June 13, 2025) have become interesting for a variety of reasons, and garnered a lot of attention in international defense publications and weapons manufacturers. To many defense analysts and defense industry professionals, these wars have become real-life testing platforms to examine and evaluate various present-day war technologies. They have opened up discussion on the future of war, the increasing use of inter-connected networks in warfare, and the increasing use of remote-operated drones in conducting surveillance and in offensive actions against …

Decoding Climate Change: A Comprehensive Statistical Insight Into Temperature Anomalies In Iraq And Its Neighboring Countries, Jameel T. Al-Naffakha, Mohammed R. Al-Qassabanda, Israa Jafar

Al-Esraa University College Journal for Engineering Sciences

This study applies advanced statistical techniques, including the Mann-Kendall Trend Test and ARIMA forecasting, to validate and predict temperature anomalies across Iraq and its neighboring countries (2014–2024). Findings confirm a statistically significant warming trend (p < 0.00000005) across all nations, with Syria (1.02°C) and Turkey (0.97°C) experiencing the highest anomalies. GIS-based spatial analysis highlights regional disparities, identifying climate-vulnerable zones. The persistent rise in temperature anomalies correlates with worsening water scarcity, desertification, and extreme weather events, including prolonged droughts and heatwaves. Iraq’s peak anomaly (1.48°C) has exacerbated heat stress, agricultural decline, and reduced river inflows, while Kuwait and Saudi Arabia struggle to maintain critical infrastructure under record-breaking temperatures exceeding 50°C. These climate shifts pose severe risks to water availability, food security, and energy demand, necessitating urgent policy interventions. Key recommendations include enhanced water resource management, climate-adaptive agriculture, and renewable energy expansion. If left unaddressed, rising temperatures could destabilize the region, increasing socio-economic vulnerabilities and escalating resource conflicts. This study underscores the need for cross-border collaboration and sustainable adaptation policies to mitigate the long-term impacts of climate change in the Middle East.

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Thermal Performance Evaluation Of Solar Cooling Systems For Educational Facilities In Baghdad, Yasser Alawi Mohammed Attia Al-Jubouri

Al-Esraa University College Journal for Engineering Sciences

Schools in Baghdad also suffer from poor thermal comfort, which is a common problem in high-temperature and low-humidity arid climates such as that of the city, where using cooling devices leads to a large energy demand and overloads on the national power grid. Such study offers a thorough assessment of the thermal performance of solar-based cooling systems in an efficient manner to be suitable for educational buildings in Baghdad. The analysis is centred on a solar thermal cooling system with absorption as the heat interaction medium, and an experimental characterisation and mathematical simulation itself are performed. A pilot installation, consisting …

Using Machine Learning To Predict Women At Risk Having A Child With Congenital Heart Defects, Amany M. Abdo Prof., Asmaa M. Mosallam Ms., Laila M. Abdelhamid Assoc.Prof.

Information Systems

Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and …

Application Of Adversarial Volumetric Cnns To 3d Face Generation Using Latent Space Gaussian Embeddings, Ali Raad Abdulkareem, Marwa Jabberi, Islem Jarraya, Tarek M. Hamdani, Khmaies Ouahada, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Although 3D face generation is extensively studied in computer vision, most existing methods prioritize reconstructing 3D geometry from available 2D or 3D inputs rather than generating novel faces directly from latent representations. To bridge this gap, we present the application of Adversarial Volumetric Convolutional Neural Networks (AVCNN), a tailored adaptation of the vanilla 3D Generative Adversarial Network (3D-GAN), to 3D face generation using latent space Gaussian embeddings. We first assemble a custom 3D facial dataset to provide the requisite facial characteristics and to ensure sufficient coverage of geometric variation across identities. The generator, implemented as a decoder, maps latent space …

Leveraging Google Earth Engine For Computationally Efficient Pixel-Level Analysis And Vector Delineation From Satellite Data., Rishita Garg

Theses and Dissertations

Analyzing large-scale, high-resolution satellite imagery is a computationally intensive task requiring time and computing resources. This can be accelerated using cloud computing platforms such as Google Earth Engine (GEE) where computational and storage requirements can be scaled based on demand. However, cloud-based platforms for processing high-resolution imagery remain underutilized in environmental applications such as agriculture, and forest health. This thesis explored the application of GEE to two geospatial problems in agricultural conservation and disease mapping in forestry: 1) Extraction of agricultural field boundaries from Sentinel-2 satellite imagery, for use in conservation, precision agriculture, land management, and organization, etc., and 2) …

Improving Road Safety Through Multimodal Deep Learning For Driver Drowsiness Detection, Hadel A. Hussain, Mohammed A. Subhi, Ahmed S. Al Tmeme, Ahmed D. Radhi, Marwan Ali Albahar

Iraqi Journal for Computer Science and Mathematics

One of the most common causes of road accidents globally is driver drowsiness and it needs solutions that are reliable and can be applicable in numerous real-life situations. We present this paper with the aim of developing a deep-learning system that is capable of reliably detecting drowsiness in diverse and varied conditions across different drivers, environments, and sensor types. Our system is known as Multimodal Attention Network (MMAN), which combines information of eye and head movement, heart-rate and breathing pattern, and vehicle-dynamics signal. MMAN has a gradient-reversal layer that enables the layer to be domain-adaptive such that it does not …

Design Considerations For Conversational Agents To Assess The Social-Emotional Well-Being Of Young Children In Low-Income South African Communities, Lucretia A. Williams, Elizabeth A. Ankrah, Catherine E. Draper, Caylee J. Cook, Franceli L. Cibrian, Jesus A. Beltran, Kimberley D. Lakes, Gillian R. Hayes

Engineering Faculty Articles and Research

A variety of digital technologies have been used to support early childhood development (ECD) programs in low-income South African communities. Even though technology has provided opportunities to increase access to health interventions, the lack of trust and socio-economic constraints under which these tools would need to work pose complex challenges. We examine home visitors’ work processes, experiences, and preferences of a conversational agent to support their work of administering social-emotional well-being assessments to young children ages 0-5. Analysis of the results of focus groups with 51 home visitors indicates the need for designing conversational agents that support ECD in the …