Collection

2025 Northeast Section Conference

Authors

Sunil Dehipawala; Guozhen An; Arkadiy Portnoy; Tak Cheung

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Pedagogy of Artificial Intelligence with Machine Learning and Computer Vision in a Community College Setting Sunil Dehipawala, Guozhen An, Arkadiy Portnoy, Tak Cheung Physics Department CUNY Queensborough Community College New York City USA Abstract—A deployment of artificial intelligence-based (AI- environment would expedite the student research progress. Thebased) examples was

Collection

2025 Northeast Section Conference

Authors

Thomas C. McKinley

learn about computer during my 10th-grade year. The journey of starting andprogramming and the impact of artificial intelligence (AI). When expanding the club was both challenging and rewarding,I started the Computer Programming and AI Club at Boston providing valuable lessons in leadership, organization, andCollege High School in 10th grade, we only had three members. engagement [1].By using a mixture of creative marketing, fun activities, andteamwork, I helped grow our club to 16 members the following When I first started the club, participation was low, with onlyyear, peaking at 43 people signing up during the school’s club fair. three members attending meetings. Lack of knowledge

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2025 Northeast Section Conference

Authors

Yegin Genc; Gonca Altuger-Genc; Akin Tatoglu

. Altuger- The ASEE Computers in Education (CoED) IV. CONCLUSION Journal, vol. 4, no. 1, p. 105, 2013. AI interactions can significantly enhance self-directed [16]learning by assessing readiness, learning goals, learning 2023 ASEE North Central Section Conference, 2023. [17]motivation, and outcomes. The adaptability of AI to different in 2023 ASEE Annual

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2025 Northeast Section Conference

Authors

Navarun Gupta; Deana DiLuggo; Junling Hu; Abhilasha Tibrewal; Ahmed Elsayed; Theresa Bruckerhoff

student success and the ability of theby adapting and implementing the Affinity Research Group institution to compete for future NSF funding and piloting a(ARG) Model. HSI Pilot Project: Fostering Hispanic Achievementin Computer Science and Engineering with Affinity Research short-term, well-defined goal to enhance the availability ofGroup Model (Project Achieve) aims to enhance the quality of the high-quality undergraduate STEM education at the HSI. Toundergraduate STEM program at UB and to provide a learning realize these project goals, Project Achieve is designed tointervention that improves retention so that all students canrealize future career aspirations in

Collection

2025 Northeast Section Conference

Authors

Steven Bercik; Mehmet Furkan Baylan; Ansa Brew-Smith; Don Heiman; Bala Maheswaran; Haridas Kumarakuru

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Learning Through Logic: An Educational Digital Guessing Game with LED FeedbackSteven Bercik1, Mehmet Furkan Baylan1, Ansa Brew-Smith1, Don Heiman1, Bala Maheswaran2, Haridas Kumarakuru1 1 Department of Physics, 2Department of Electrical and Computer Engineering Northeastern University, Boston, MA 02115 USA Abstract—This project introduces a digital guessing game, engaging, and fun, fostering an overall deeper understandingwhere player-1, the guesser, attempts to deduce a correct and appreciation of

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2025 Northeast Section Conference

Authors

Elyas Irankhah; Sashank Narain; Kelilah L. Wolkowicz

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Interactive AI Learning Through Game Play: Engaging K-8 Students with Tic-Tac-Toe AI Game Elyas Irankhah Sashank Narain Kelilah L. WolkowiczMechanical & Industrial Engineering Computer Science Mechanical & Industrial EngineeringUniversity of Massachusetts Lowell University of Massachusetts Lowell University of Massachusetts Lowell Lowell, MA, USA Lowell, MA, USA Lowell, MA, USA Elyas_Irankhah@student.uml.edu

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2025 Northeast Section Conference

Authors

Marvin Gayle; Danny Mangra

in circuit theoretical concepts through experiments, simulations, andsimulation software and virtualization platforms for their role in hands-on exercises. Mechanical engineering students benefitimproving accessibility, scalability, and student engagement in when using AutoCAD or working with Computer Numericalengineering instruction. Additionally, by working in groups or Control [CNC] manufacturing tools. Traditional labs, in ateams in laboratory environments, students develop teamwork Computer Science perspective, may include softwareskills, peer learning, leadership, and communication skills

Collection

2025 Northeast Section Conference

Authors

Rudra Mitra; Intiser Islam; Shariar Islam Saimon; Xingguo Xiong

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA. A Hybrid Deep Learning Model for Pneumonia Diagnosis: Bridging Gaps in Imaging and Patient History Data Rudra Mitra Intiser Islam Department of Computer Science and Engineering Department of Computer Science University of Bridgeport University of Bridgeport Bridgeport, CT Bridgeport, CT rmitra

Collection

2025 Northeast Section Conference

Authors

Lina H. Kloub, University of Connecticut

Empowering Students with AI: A Universal Design Framework for Learning and Growth Lina H. Kloub School of Computing University of Connecticut Storrs, CT, USA lina.kloub@uconn.edu Abstract—The integration of artificial intelligence (AI) tools in challenging in the curriculum. The course demands a deepeducation presents a unique opportunity to enhance learning ex- understanding of algorithmic principles and the ability to applyperiences, foster

Collection

2025 Northeast Section Conference

Authors

Dinh Cuong Nguyen; Gregory Lovisolo; Dan Tenney

employees at Intell. Fuzzy Syst., pp. 1–9, April 2024.risk of burnout and younger employees at early career stages, [7] Dr. Anamika Rana, Sushma Malik, and madhu Chauhan, “Employeereflecting their distinct needs. The practical use of attrition prediction using machine learning techniques,” J. Kufa Math.counterfactual explanations enables HR departments to Comput., vol. 11, no. 2, August 2024.pinpoint precisely which employee-specific factors can be [8] J. H. Friedman, “Greedy function approximation: A gradient boosting machine.,” Ann. Stat., vol. 29, no. 5, October 2001.adjusted, such as

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2025 Northeast Section Conference

Authors

Paul Cotae; Nian Zhang; Onyinye Obioha-Val

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA. A Decision-Making Framework for Addressing the Imbalanced Learning Problem in Standoff Detection Paul Cotae Nian Zhang Onyinye Obioha-Val Department of Electrical and Department of Electrical and Department of Electrical and Computer Engineering Computer Engineering Computer Engineering University of the District of Columbia University of the District of Columbia University of the District of Columbia Washington, D.C

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2025 Northeast Section Conference

Authors

Susrutha Babu Sukhavasi; SUPARSHYA BABU SUKHAVASI

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Exploring AI in Education: A Review of Its Impact on Classrooms, Learning Management, and Pedagogical Strategies Susrutha Babu Sukhavasi Suparshya Babu Sukhavasi Electrical and Computer Engineering Electrical and Computer Engineering Wentworth Institute of Technology University of New Haven Boston, MA, USA. West Haven, CT, USA. sukhavasis@wit.edu

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2025 Northeast Section Conference

Authors

Samuel Servati; PS. Dhanasekaran

findings,and design the entire system in CAD software such as IV. BRIDGING THE GAP BETWEENAutodesk Inventor. To validate their work, they THEORY AND APPLICATIONconduct Finite Element Analysis (FEA) and compare One of the fundamental takeaways from research ontheir computational models to real-world load-bearing PBL’s impact is its role in bridging the gap betweendata. This hands-on approach enables students to theoretical knowledge and hands-on application.internalize engineering principles more effectively Many graduates enter the workforce with a strongthan passive learning methods would allow [1]. theoretical foundation but limited experience

Collection

2025 Northeast Section Conference

Authors

Bala Maheswaran; Meghna Sridhar; Yiannis Levendis; Hameed Metghalchi

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA. Developing a Sustainable Engineering Mindset Through Heliostat Activities in Project-Based Learning Bala Maheswaran Meghna Sridhar Yiannis Levendis Hameed Metghalchi Electrical and Computer College of Engineering Mechanical and Industrial Mechanical and Industrial Engineering and FYE Northeastern University Engineering Engineering Northeastern University Northeastern University

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2025 Northeast Section Conference

Authors

Srilekha Bandla; Mukesh Reddy Jonnala; Peiqiao Wu; Sarosh Patel; Xingguo Xiong

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA. A Novel Integrated Machine Learning-Driven System for Seizure Management: Real-Time Detection and Dual-Mode Intervention System Srilekha Bandla Mukesh Reddy Jonnala Department of Biomedical Engineering Department of Biomedical Engineering University of Bridgeport, Bridgeport, CT. University of Bridgeport, Bridgeport, CT Email: sbandla@my.bridgeport.edu Email: mjonnala

Collection

2025 Northeast Section Conference

Authors

Mina Gaber Wahba Ibrahim; Xingguo Xiong; Navarun Gupta; Ahmed El-Sayed

2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA. AI-Driven Wildfire Detection with Integrated Air Quality and Machine Learning Vision Systems Powered by an Accelerator for Early Action Mina Gaber Wahba Ibrahim*, Xingguo Xiong, Navarun Gupta, Ahmed El-Sayed Department of Electrical Engineering Department of Electrical and Computer Engineering, University of Bridgeport University of Bridgeport, Bridgeport, CT 06604, USA Bridgeport, CT 06604, USA

Collection

2025 Northeast Section Conference

Authors

Mohammad Rafiq Muqri; Simon Obeid; Gusteau Duclos, DeVry University

learning pathways tailored to individual student needs. I. INTRODUCTION/BACKGROUND Generative AI makes learning experiences more personalized Generative Artificial Intelligence (AI) is revolutionizing by analyzing large volumes of educational data and generatingeducation by providing innovative tools to enhance learning customized quizzes, lessons, and feedback. However,experiences, automate assessments, and support personalized assessing the effectiveness and impact of AI-driven educationinstruction. AI refers to the capability of a computer system to remains a challenge. To address this, we propose a holisticmimic human cognitive functions such as learning and

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2025 Northeast Section Conference

Authors

PS. Dhanasekaran

privacyconcerns. As AI increasingly enhances students' learning, time feedback, and intelligent administrative support.these innovations may overshadow critical ethical issues. AI-supported classrooms have been found to improveTeachers play a crucial role in ensuring that AI is used engagement and student achievement compared toethically in education, and their training needs to be traditional methods. Computer-based education is set tocomprehensive in addressing these concerns. AI transforms become an integral part of the 21st century, with AIeducation by focusing on students' needs and aiding playing a crucial role in this transformation. It is essentialeducators in developing personalized

Collection

2025 Northeast Section Conference

Authors

Basile Panoutsopoulos

interactive androom by providing a more personalized environment. immersive learning environments where students can explore and interact with content more engagingly and memorably. Artificial intelligence (AI) refers to the capability ofcomputational systems to perform tasks typically associated Automation: AI can automate administrative tasks, such aswith human intelligence, such as learning, reasoning, problem- grading, to free up teachers’ time to focus on other importantsolving, perception, and decision-making. It is a field of aspects of teaching.research in computer science that develops and studies In this work, the case

Collection

2025 Northeast Section Conference

Authors

Lakshmi Aishwarya Malladi; Navarun Gupta; Ahmed El-Sayed; Xingguo Xiong

YOLO imbalances mitigated by data augmentation.and Faster R-CNN [10, 11].Mobile Net: Though lacking inresilience [12], optimized for edge devices. VGG16 Regarding For real-time fire monitoring, autonomous drone-based fireWildfire Identification Using deep feature extraction and detection, and early wildfire warning systems, this dataset helpstransfer learning, attained a great accuracy of 96.2%.Beats deep learning models including VGG16, ResNet, and YOLOResNet50 in terms of false positive minimization. Restrictions: for wildfire identity discover training value.Increased computational cost, dataset bias, overfit susceptibility[13].D

Collection

2025 Northeast Section Conference

Authors

Mohamed Elwakil; Tooran Emami Ph. D.

student learning. Using a academic backgrounds and career trajectories.mixed-methods approach, we collected data from 35 students (25from DB and 10 from EEM) through pre- and post-study surveys, The significance of integrating prompt engineering intoskills assessments, and qualitative feedback. Key findings reveal curricula spans disciplines. In computational fields, it enhancesthat students in both courses reported an improved understanding technical workflows such as code generation or databaseof AI and proficiency in prompt engineering. Students in the DB optimization, while in engineering or applied sciences, itcourse, which has a

Collection

2025 Northeast Section Conference

Authors

Rachmadian Wulandana

of comparing it with lecture materials, textbooks, andthe final year. Viewing FEA as a useful computational tool for Wikipedia.computer simulation, analysis of complex physics, and 7. I rely on AI tools to get information on cutting-edgeengineering designs, the Intro to FEA course was introduced research on heat transfer.early in the junior year so that students can use it for their 8. I use AI tools to create images needed for illustrationsenior design projects and research they are interested in. Intro in my reports etc.to FEA class is dominated by junior students. This course isdesigned as semi project-based learning. Team projects (48percent

Collection

2025 Northeast Section Conference

Authors

Sailesh Adhikari; Navarun Gupta; Xingguo Xiong; Ahmed El-Sayed

and artificial intelligence (AI) have opened new possibilities I. INTRODUCTION for automated wildfire detection and monitoring. In particular, deep learning has revolutionized the field of computer vision, Wildfires have emerged as one of the offering state-of-the-art solutions for image classification,most devastating natural disasters, posing significant object detection, and segmentation tasks [7].threats to human lives, infrastructure, and ecosystems. Inrecent years, the frequency and intensity of wildfires Convolutional Neural Networks (CNNs) have

Collection

2025 Northeast Section Conference

Authors

Julianne Torreno; Nealesh Guha; Mashtura Rahman; Michael Ventouratos; David Lee; Shivansh Sharma; Sunil Dehipawala; Guozhen An; Tak Cheung

continue with new studentgenerate new results, the skill learning project is still beneficial members.for the students to gain experience in image entropy analysis. The AI Large Language Models (AI LLM) to translate C++ codes to Python codes is an efficient feature in some student IV. IMPLEMENTATION III projects, for instance, some drone control programs written in The computation experience learned on the PubMed and C++ codes could be transformed to become Python codes withSDO data analysis

Collection

2025 Northeast Section Conference

Authors

Nusrat Zahan; Sidike Paheding

ods for performance enhancement for example-based single- image super-resolution (SISR), where one of them was data Image super-resolution (SR) is a key challenge in computer augmentation. They found consistent improvements acrossvision that reconstructs high-resolution (HR) images from their models and datasets, using rotation and flipping. However,equivalent low-resolution (LR) images [1]. they only evaluated geometric distortions using traditional SR Although SR has made great strides in recent years with the models [11], [12]. Feng et al. [9] tested a method calledemergence of deep learning, its

Collection

2025 Northeast Section Conference

Authors

Lina H. Kloub, University of Connecticut; Christina Smith, University of Connecticut; Faiyhaa-Sydra Saulat, University of Connecticut

- education, bolstering students’ problem-solving competencies.ers value candidates who can demonstrate critical thinking The ”Machine Learning, Modeling, and Simulation: Engi-and adaptability in real-world situations [7]. AI can enhance neering Problem-Solving in the Age of AI” program providesproblem-solving skills by providing students with dynamic a hands-on approach to understanding computational toolsproblem-based learning environments, adaptive learning plat- essential for modern engineering challenges. This programforms, and AI-driven simulations that challenge them to de- bridges traditional engineering skills with contemporary ma-velop innovative solutions in real time. Additionally, AI can

Collection

2025 Northeast Section Conference

Authors

Teresa Piliouras; Steffi Crasto; Chinmay Dharap; Pui Lam Yu; Navarun Gupta

claims. History and literature consistent audience. courses can emphasize source analysis and perspective- taking, helping students critically assess historical • Parental Support and Independent Learning: documents, media narratives, and bias. Technology and Providing parents and students with resources to computer science courses can foster cybersecurity develop critical thinking skills nurtures reflective, awareness by teaching students to identify online analytical minds and bridges gaps in traditional threats, assess privacy risks, and navigate ethical education. Bogart offers strategies for parents to foster challenges in the digital world. Examples of mini

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2025 Northeast Section Conference

Authors

Kalyan Khatry; Reihaneh Samsami

both educators and learners. In practice, this Some universities have begun introducing modules on could mean incorporating AI orientation sessions at the startmachine learning and AI fundamentals even for non-computer of courses, developing short online modules on AI literacy forscience engineering majors, recognizing that understanding students, and organizing faculty seminars on Gen AI inhow AI works is becoming as important as, say, understanding pedagogy.circuit theory or thermodynamics for certain fields.Additionally, courses on technical communication areevolving to include guidance

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2025 Northeast Section Conference

Authors

Shashi Kiran Chandrappa, Fairfield University; Sidike Paheding, Fairfield University

handling the high-dimensional and complex nature ofvolume of data associated with HSI pose unique challenges HSI data. Traditional machine learning approaches, such asin terms of classification and analysis. Traditional classifi- support vector machines (SVMs) [10] and random forestscation algorithms, such as multi-layer perceptrons [5] and [11], paved the groundwork by providing early solutions forconvolution neural networks (CNNs) [6] have shown promise HSI classification. However, these traditional approaches werebut frequently require extensive computational resources and limited by their inability to fully capture pertinent featureslarge amounts of labeled data, which are often

Collection

2025 Northeast Section Conference

Authors

Lina H. Kloub, University of Connecticut; Vraj Patel; Tina Huey

AI tools in the Spotlight: Addressing Educators’ Concerns and Building Trust Lina H. Kloub Vraj Patel Tina Huey School of Computing School of Computing Center for Excellence in Teaching and Learning University of Connecticut University of Connecticut Department of English Storrs, CT, USA Storrs, CT, USA University of Connecticut Storrs, CT, USA Abstract—As artificial intelligence (AI) transforms higher learning