黑料吃瓜网

Gasson 203

Jonathan Ahern,聽Associate Director, Graduate Programs, Woods College of Advancing Studies|
Michelle Elias Bloomer,聽Associate Dean, Undergraduate Programs, Woods College of Advancing Studies
John FitzGibbon,聽Associate Director of Digital Learning Innovation, Center for Digital Innovation in Learning
No毛l Ingram,聽Digital Teaching Programs Administrator, Center for Digital Innovation in Learning
Tristan Johnson,聽Associate Dean of Graduate Programs, Woods College of Advancing Studies
Emily Kent,聽Associate Director, Undergraduate Programs, Woods College of Advancing Studies

This case presentation highlights a college collaboration that led to the development and delivery of a faculty professional development workshop series focused on the effective integration of artificial intelligence (AI) technologies into college teaching practices. The initiative was designed to equip faculty with practical strategies, pedagogical approaches, and conceptual frameworks to thoughtfully incorporate AI into their course design and instruction.

Through shared expertise, workshops, and innovative practices, faculty members are reimagining course design, enhancing student engagement, and preparing learners for an AI-driven future.聽 We will share key insights into what worked well鈥攁nd what fell short鈥攄uring the planning, design, and implementation of the workshop series, offering lessons learned to guide future efforts.

Gasson 302

Moderator: Rachel Greenberg, Director, Career Center
Adrienne Chiozzi, Takeda
Hampton Clarkson, Dell
Casey McInley, Connor Group
Tate Krasner,聽Air Space Intelligence

This panel session will explore how artificial intelligence is changing the workplace. AI skills have become among the fastest growing skills needed for today鈥檚 workforce. Representatives from local organizations across multiple industries will discuss the essential technical and non-technical skills and knowledge that employers are prioritizing in new college graduates as they pertain to AI. Attendees will gain insights into how we can all help prepare students and new graduates for the ever-changing job market.

Gasson 205

Cristin Richard, Director, IT Service Management & Computing Support, ITS
Peter Salvitti, Chief Technologist, ITS
Nirmal Trivedi,聽Assistant Director for Teaching, Learning, and Technology,聽Center for Teaching Excellence

This session equips you with essential knowledge about the potential risks and best practices of using AI tools in higher education, while also providing insights into Boston College's current AI landscape and future direction.

Gasson 209

Amittai Aviram,聽Associate Professor of the Practice, Computer Science
Mark Behn, Professor,聽Earth and Environmental Sciences
Marcus Breen,聽Associate Professor of the Practice, Communication
Dogus Dogru, Research Assistant, Biology
Ji Yoon Jung, Senior Research Specialist, IEA's TIMSS & PIRLS International Study Center, Lynch School of Education and Human Development
Cristina Maier,聽Assistant Professor of the Practice, Computer Science

Co-organized by the Department of Computer Science and the Schiller Institute for Integrated Science and Society, this session will feature a mix of talks and poster presentations, providing a platform for participants to share their work and laying the groundwork for collaborative team proposals.

Gasson 301

Elliott Hibbler,聽Head Librarian, Scholarly Platforms and Discovery Services
Erin Sibley,聽Director, Research Protections, Education, & Postdoctoral Affairs
David J. Thomas, Digital Scholarship Specialist, University Libraries

In this session, a panel of staff from both Boston College Libraries and the Office of Research Protections will discuss the risks and safeguards researchers need to consider when using AI in their scholarship. Specifically, the panel will discuss: the implications of using AI with research data, particularly human research data and Institutional Review Board requirements; which uses of AI are allowed in works for academic publishers and how those uses are disclosed; and what it means for a scholarly publication to exist in a world where AI companies see everything as potential training data.

Gasson 204

ITS Research Services team members:
Viktoriya Babicheva, MPH
Rani Dalgin, MSW,
M.Ed.Matt Gregas, Ph.D.
Melissa McTernan, Ph.D.

Join research methodology and data acquisition experts from the 黑料吃瓜网 ITS Research Services Team for an engaging roundtable discussion on the transformative impact of generative AI across various research methodologies including quantitative, qualitative and mixed methods research and the associated methodological and security considerations. This session will delve into how AI-driven tools are revolutionizing research by enhancing data analysis and interpretation, expediting research through assisting with coding statistical models and assisting with qualitative and mixed methods coding. Whether you're a seasoned researcher or new to the field, this promises to be a lively conversation.

Gasson 112

Seoyeon Bae,聽Ph.D. Candidate
Ashley Duggan,聽Professor, Health Communication
Stefane Cahill Farella,聽Senior Associate Director, Employee Development, Human Resources
Kyle Fidalgo,聽Academic Technologist, Law School
Marta Mirete Hernandez,聽Lecturer of Spanish; Coordinator of Intermediate Spanish Language Program
Callid Keefe-Perry,聽Assistant Professor,聽Clough School of Theology and Ministry
Tim Lindgren,聽Assistant Director for Design Innovation, Center for Digital Innovation in Learning
Chris Wilson, Professor Emeritus, English

Join us for an interactive 鈥淪how & Tell鈥� session featuring a range of custom AI Assistants developed by members of the Boston College community. Attendees are invited to drop by, ask questions, try out the chatbots, and learn firsthand from the creators about their design choices, lessons learned, and future ideas.

Gasson 100

Sheikh Ahmad Shahm, PhD student

This poster is about how Large Language Model (LLM) chatbots like Copilot can support non-STEM undergraduates with limited coding experience. Three lab sessions were conducted focusing on creating JavaScript-based games and simulations, where students used LLM tools to create progressively more complex programs, the final one being their own project. While survey results were mostly inclusive, students鈥� chat history and reflections suggest growth in students鈥� ability to write better prompts to generate better code. However, students had less critical engagement with code itself. The study provides early insights into the role of AI tools in coding education among novice learners.

Gasson 112

Library Instruction Department

This poster conveys the importance of critical information literacy and the library's role in supporting students' learning and research processes.

Gasson 100

Nora Alwash, Class of 2026, Undergraduate Research Assistant
Hayoung Cho, Class of 2025, Research Student
Vera Hernandez, Class of 2026,聽Undergraduate Research Assistant
Mary Loeb,聽Class of 2026,聽Undergraduate Research Assistant
Bryan Ranger,聽Ferrante Family Assistant Professor, Department of Engineering

Monitoring neonatal body composition non-invasively can detect malnutrition and growth patterns, critical for preterm infants. We used abdominal, biceps, and quadriceps ultrasound images to measure tissue thickness, predicting fat-free mass (FFM) and fat mass (FM) via non-linear regression methods and Gaussian Process Regression. Sensitivity analysis showed individual measures were insufficient, prompting UNet-based cross-sectional area segmentation. We developed gelatin-based phantoms (1% psyllium husk for adipose; 2% aluminum oxide in agar for muscle) with echogenicity comparable to clinical datasets. Dice scores were 0.9272 for phantom, 0.6089 for clinical, and 0.8027 for mixed data. Ongoing work focuses on improving predictive performance on mixed and clinical datasets.聽

Gasson 100

Maddie Bruns, Class of 2025
Annette Choi
Aimee Cowles, Class of 2026
Hannah Cunniffe, Class of 2026
Ashley Duggan, Professor, Health Communication
Lily Harden, Class of 2025
Aliza Jernigan, Class of 2025
Erin Kolenda, Class of 2026
Charlotte LaBossiere, Class of 2025
Frances Lee, Class of 2026
Cro铆a Loughnane

As digital technologies increasingly shape how we communicate, learn, and care for ourselves, there is growing potential to use artificial intelligence (AI) to support reflection, connection, and wellbeing. AI chatbots, in particular, are emerging as tools not just for delivering content, but for facilitating transformative conversations鈥攖he kinds that spark awareness, build empathy, and inspire action. Two custom chatbots were designed to be thoughtful, curious, and facilitative conversations, drawing inspiration from person-centred health and wellbeing professionals with strong communication skills. This study employed a qualitative exploratory design to investigate the effectiveness of the reflective AI chatbot within the context of a university course on Health and Illness in Relationships at Boston College. This poster focuses on themes identified in student written reflections about their AI chatbot experience.

Gasson 100

Shrey Gupta,聽Post Doctoral Research Fellow, Computer Science
Yi Ming, Institute Professor of Climate Science and Society, Schiller Institute for Integrated Science and Society
George Mohler,聽Daniel J. Fitzgerald Professor, Computer Science

Accurate crop yield prediction is vital for food security and agricultural planning, yet building robust models that handle the complexities of temporal and spatial variations remains a challenge. This study introduces a deep learning framework employing a hybrid Convolutional Neural Network鈥揕ong Short-Term Memory (CNN-LSTM) architecture to model both temporal dynamics and spatial dependencies in long-term agricultural data. Trained on multi-year, county-level crop yield data alongside daily meteorological variables (temperature, precipitation, humidity, and more), the model captures intricate patterns across time and space.The LSTM component effectively learns long-range temporal dependencies and seasonal fluctuations, which are crucial for accurate forecasting. Simultaneously, the CNN component extracts localized spatial features from county-level inputs, enabling the model to understand spatial patterns in climate and their impact on yield variations across regions.The results demonstrate that this CNN-LSTM framework achieves accurate yield predictions while effectively capturing the complex interactions between long-term climatic trends and spatial differences in county-level crop yield data. This hybrid approach offers a significant step towards more reliable and insightful crop yield forecasting.

Gasson 100

Helen Huiting Zheng, PhD Candidate, Psychology
Kyle Fiore Law, Postdoctoral Researcher
Liane Young, Professor, Psychology聽

In recent years, it has become increasingly common for people to seek advice, including moral guidance, from artificial intelligence (AI) chatbots. This poster presents findings from a representative U.S. adult sample (N = 348) examining the relationship between individuals鈥� religiosity and their tendency to seek advice from AI chatbots for both general and moral matters. The findings offer initial insights into how religious involvement and commitment shape the tendency to seek advice from AI chatbots. This work contributes to understanding the intersection of technology, belief systems, and advice-seeking behavior in contemporary society.